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Title & Speakers	Event
Search London's 15th Birthday - Community and Measuring AI in 2026 2026-02-24 · 18:00 Overview Join us in London to celebrate 15 years of Search, Community and more recently AI in person! Search London's 15th Birthday Bash! Join us in person for an epic celebration of 15 years of Search London. Dive into the future with talks on Community and Measuring AI in 2026. It’s the perfect chance to connect, learn, and network with our community. To celebrate turning 15, we are teaming up with Search 'n Stuff. Come and meet and network with digital marketers from both communities. 🗓️ Date : Tuesday, 24 February, 2026 ⏰ Time : 18:00 – 23:00 📍 Venue : Farringdon - venue to be confirmed in January Buy your ticket today on Evenbrite to secure your place. Meet our speakers! Come and learn from our speakers: Erin and Jon ! Erin Simmons (They / Them) Erin is Managing Director at the Women in Tech SEO community - focused on democratising successful careers through connection, education, opportunities, & amplification. With 15+ years in marketing and analytics, they now focus on better understanding people and connecting them to the relationships and resources that support them most. Erin will speak about: Trust → Visibility: SEO Powered by Community You can AI-engineer your content and website all day, but even if you show up in the answers, you still won’t be chosen if people don’t trust you. Trust isn’t built between a person and a brand; it’s built in the communities, conversations, and human spaces where people help each other make sense of the world. In this talk, Erin Simmons explores the growing trust gap in search and why the real SEO edge comes from showing up where trust is being exchanged. * When you earn trust, people make you visible -- they reference you in conversations, recommend you in threads, and surface you in the exact places AI and search engines now look for signals. Visibility is a byproduct of trust. * And if you focus on trust first, you’ll show up everywhere that matters: in AI results, in search, and in the moments when people turn to one another to decide what to believe. Jonathan Moore Jonathan is an independent SEO and analytics consultant based in Cheltenham. With over 15 years of experience, including six years heading up the SEO and analytics for one of the UK's largest independent digital marketing agencies, he has got a knack for digging into the details to find what really matters. He has a degree in Computer Science and works with large data sets, APIs, and scripts the form the backbone of his business. Whether you need help with technical SEO, analytics audits, or uncovering competitive insights, he provides hand on consultancy for any business. Jonathan will speak about: Measuring search disruption: What metrics matter and who needs to know them This talk is for SEOs who need to understand and communicate the impact of emerging AI-driven search disruptors, including LLMs and AI Overviews. The audience will learn which metrics genuinely matter and how to communicate the right insights to the right stakeholders at the right moment. The takeaway is practical advice for measuring disruption that supports sound decisions without getting lost in hyper-tactical detail. Ticket includes: ✅ Entry to the event ✅ Finger foods ✅ x2 drinks per person (soft drinks, wine and beer) ✅ Access to all talks and networking ✅ Surprise gifts from our network 😉 Early Bird Special with 50% discount until Decemeber 22nd ! We have 50% off the ticket price for the first 30 tickets sold until December 22nd. Buy your ticket today on Evenbrite and learn from Jonathan and Erin, plus network with up to 120 search professionals with enough food and drinks for everyone.	Search London's 15th Birthday - Community and Measuring AI in 2026
Secure Software Development - Girl Code at CGI 2026-02-05 · 18:00 Waterworks, airports, the energy grid or telephone traffic: all interesting targets in the Netherlands for cyber or hybrid attacks from countries like Russia and China, say intelligence experts. A highly digitalized world asks for a movement from DevOps to DevSecOps, this means us as developers should specialize in security now too! More than ever, a hack on your software can have serious impact on the daily lives of people: from leaking their personal information to shutting down the energy supply to Dutch homes. Think the leak of private data of police in 2024, and the one of the Bevolkingsonderzoek Nederland in 2025. In other words your software might not be responsible for trains to be able to ride, or payments being done through banks, but most companies do process at least some personal data. Just like accessibility, security often is an afterthought, and that will just not do anymore. Security needs to be considered from day one in every software project. Tonight we will give the stage to three developers to talk about their security approach. Program 17.45 walk in / dinner (vegetarian) 18.30 Intro Girl Code by Ineke Scheffers 18.35 Intro CGI by Janine Tjassens, CGI BU Leader Randstad and North Netherlands 18.40 Secure Software Development - Erica Welling, Software Security Specialist at CGI Erica will discuss several core principles that help make IT structurally more secure. With these insights into our daily work, we can build systems that are not only fast and beautiful, but also resilient and future-proof. 19.00 Supply Chain Attacks: Here to Stay - Jasmijn van Genesen, Application Security Engineer at KVK Supply chain attacks like Shai Hulud show how hackers exploit package managers and CI/CD pipelines to steal secrets. In her talk Jasmijn will plead for protecting the whole chain—not just writing secure code—by making mitigation of risks a standard in DevSecOps. 19.20 Business Logic Flaws: How to Prevent Them in Your APIs - Alexandra Charikova, The Elephant in AppSec podcast host & community manager at Escape Attackers exploit valid features in unintended ways to bypass rules, abuse workflows, or manipulate sensitive operations. In this talk, Alexandra explores real-world API examples, how attackers chain valid actions for harmful outcomes, and how defenders can catch issues early through ie. threat modeling and security integration in CI/CD. 19.40 Q&A with all speakers 20.00 - 21.00 drinks, snacks, mingle "At CGI, we value and actively work together to foster an environment where every voice is heard and respected and where every member has equal opportunity to share their ideas, lead and grow. Estimates are that less than 30% of technology jobs are held by women. And according to the numbers above that seems a little too generous even. To increase this it is important to inspire and empower women, which is why initiatives such as Girl Code are so important. A little while ago we hosted Girl Code at CGI, in short, it was a great success! Looking at the current (political) climate the topic of diversity is more important than ever. With that in mind we couldn't be more excited to host another Girl Code event!" - Sara Larsson\, Software Architect & Engineer at CGI Team Ineke Scheffers - organizer and founder Girl Code Sara Larsson - host CGI Jeroen de Bekker - host CGI Frédérique Doek - host CGI Kimberly Bisschops - host CGI Attendance policy We take attendance. If you RSVP'd but didn't show up, it will have consequences. Also be sure to cancel on time, 'cause cancelling 1 day before will be too late and count as a no-show. (Illness is of course an exception to this rule). Public transport It's a 5 min walk from station Rotterdam Alexander. At the station take the exit which ISN’T connected to the metro. Find the entrance by following the George Hintzenweg. Call reception at the visitors door, take the stairs up, it's the 1st building on the left. Car The employee garage is open for us between 17.30 and 18.30. Before or after: use the intercom. The parking garage can be reached along George Hintzenweg. Take the stairs in the middle, then you will find the CGI office on the left (road as orientation). Accessibility If you need disability parking or use a wheelchair contact us at [email protected], so we can make the right arrangements.	Secure Software Development - Girl Code at CGI
TidyTuesday 2026-01-27 · 23:00 Join R-Ladies Ottawa for a casual evening of programming on Tuesday, January 27th. We'll be participating in TidyTuesday, a weekly data visualization challenge organized by the R for Data Science community. What is TidyTuesday? Every week, a new dataset is posted online on the TidyTuesday GitHub repo, and folks from around the world create data visualizations using the dataset. It's an opportunity to put your programming skills into practice using real-world data in a way that's fun! It's also a great way for everyone to learn from each other, by sharing their visualizations and code. What will the dataset be? Even we don't know that (yet)! We'll have to wait until the day before the event to know what data we'll be working with. If you're interested in seeing some past datasets, take a look at the examples below, or visit the TidyTuesday GitHub repo to see all of the datasets dating back to 2018. Examples from past TidyTuesdays: Donuts, Data, and D'oh - A Deep Dive into The Simpsons Pokemon What have we been watching on Netflix? Do I have to use R? No! You can use any programming language or visualization software that you want. In fact, Python users from around the globe participate in "TyDyTuesday" on a weekly basis. Who is this event for? No previous programming experience is required to participate, and we'll have experienced programmers in the room who can help you get started (or unstuck), if needed. ...But if you want to get the most out of the event, a good way to prepare is to watch the recording of the introduction to data visualization workshop we hosted back in 2024. :) What should I bring? Please bring a laptop so you can code along. We recommend that you have RStudio or another IDE (such as VS Code or Positron) installed ahead of time, but we can help you get one installed if needed! Come ready to learn, share, and contribute to a safe and welcoming community! How will this event work? First few minutes of the event: Introductions, and taking a look at the dataset together as a group. Time to create a data visualization using the language or software of your choice, either on your own or with a (new) friend! Grab a free snack while you're at it :) Last \~30 minutes of the event: Show and tell session for anyone who would like to share their creation with the group. What else do I need to know? This event (like all R-Ladies events) is totally FREE to attend. The event will take place at Bayview Yards, which is located just a few steps away from the Bayview O-Train station. There is also a free parking lot available for those who are driving. You can find us in the "Training Room", which is on the second floor of the Bayview Yards building. This is an in-person event with limited space! Please only RSVP if you are able to attend in-person! *Please note that the mission of R-Ladies is to increase gender diversity in the R community. This event is intended to provide a safe space for women and gender minorities. We ask for male allies to be invited by and accompanied by a woman or gender minority.* We’re grateful to be part of the Bayview Meetups initiative and extend our thanks to Bayview Yards for generously providing the venue space.	TidyTuesday
Jan 22 - Women in AI 2026-01-22 · 23:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.	Jan 22 - Women in AI
Jan 22 - Women in AI 2026-01-22 · 23:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.	Jan 22 - Women in AI
Jan 22 - Women in AI 2026-01-22 · 23:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.	Jan 22 - Women in AI
Jan 22 - Women in AI 2026-01-22 · 23:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.	Jan 22 - Women in AI
Jan 22 - Women in AI 2026-01-22 · 23:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.	Jan 22 - Women in AI
Jan 22 - Women in AI 2026-01-22 · 17:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.	Jan 22 - Women in AI
Jan 22 - Women in AI 2026-01-22 · 17:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.	Jan 22 - Women in AI
Jan 22 - Women in AI 2026-01-22 · 17:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.	Jan 22 - Women in AI
Data Leadership World Summit 1.0 2025-12-31 · 13:30 1st Anniversary and New Year Celebration'26 Registration: Fill this [Must] Bought to you by Uttar Pradesh Power BI Club & powered by Sessionize. Agenda: Click here to view *6 tracks:* Technology and Innovation Career and Growth Impact and Ethics Leadership and Culture Startup and Entrepreneurship Women and Inclusive Environment Gain priority, in-person access to our flagship Summit 2.0 by joining this crucial online session. We are distributing a limited batch of 50 exclusive, confirmed seat passes to active online participants. We hope you to see you soon both online & offline. Contact us at [email protected] [email protected] or [email protected]	Data Leadership World Summit 1.0
🎁 Special end-of-year gift: an exclusive Vibe Coding workshop 2025-12-19 · 09:00 🎁 *Women in Big Data Paris and SCAI – Sorbonne University invite you to a special end-of-year gift: an exclusive Vibe Coding* workshop with Swati Awasthi. The event aims to showcase how AI is revolutionizing data analytics and driving innovation across sectors. Whether you are new to AI or an experienced professional, this event offers a platform to learn, network, and explore the endless possibilities of AI programming. Don't miss this opportunity to be part of the conversation shaping the future of AI technology. 📅 19 December 2025 🕒 Time details available in this Meetup event 📍 Sorbonne University – Pierre and Marie Curie Campus, Room TD5666 – 105 ✨ A dynamic, collaborative way to explore coding from a fresh perspective. This event is free, but registration is required due to limited capacity. ✨Don't forget to bring you laptop ! 👉 Save your spot now. 🎁 Women in Big Data Paris et SCAI – Sorbonne Université vous invitent à un cadeau de fin d’année exceptionnel : une séance exclusive de Vibe Coding avec Swati Awasthi.** Swati Awasthi, fondatrice de Women in Product India, anime des ateliers reconnus pour leur impact et leur énergie. Sa session à Bengaluru a été unanimement saluée comme l’une des plus inspirantes du sommet. 📅 19 décembre 2025 🕒 Horaires précisés dans l’événement Meetup 📍 Sorbonne Université – Campus Pierre et Marie Curie, salle TD5666 – 105 ✨ Un atelier dynamique pour expérimenter le code autrement, dans une ambiance collaborative. L’évènement est gratuit, mais l’inscription est obligatoire – les places sont limitées. 👉 Réservez votre place dès maintenant.	🎁 Special end-of-year gift: an exclusive Vibe Coding workshop
Predicting women's chronic disease flares from wearables 2025-12-12 · 19:00 Ipek Ensari – Assistant Professor @ Icahn School of Medicine at Mount Sinai Heart rate variability (HRV) is a well-known digital biomarker and is increasingly available in consumer wearables. However, extracting actionable predictions from HRV data, in particular for clinical use, remains challenging. Using specialized R packages, this presentation demonstrates how to model 24-hour periodic patterns in HRV metrics as non-linear circadian components to predict chronic disease flares. Grounded in real-life data from a NIH-funded longitudinal mHealth-based study of female chronic pelvic pain disorders, we will investigate how mixed-effects cosinor regression accommodates individual variation and complex interactions between circadian parameters and time-varying covariates (menstrual cycle, physical activity, sleep quality). These examples aim to illustrate how patient-generated data from everyday wearables can democratize access to predictive medicine by helping patient-users maximize the benefits of their data to gain predictive insights into their health status. hrv wearables r cosinor regression mixed-effects cosinor regression digital phenotyping mhealth	The Data-Powered Patient: Predicting Women's Chronic Disease from Wearables
AI Demo Night: Learn and Connect 2025-12-11 · 23:00 Come get the ⚡️AI Spark⚡️ with NYC Women in Machine Learning and Data Science! We are wrapping up the year with an evening of inspiration, demos, learning and connection. This week is also the New York AI Summit (12/10-12/11) and we have 1 free pass to give to one of our members (provided by NYAI). RSVP to our demo night by 5pm today to be entered into the raffle to win the ticket. One RSVP'd member will be selected by 5pm and will be emailed their ticket. NYAI is our partner for the New York AI Summit. What to Expect: 6:00pm - 6:30pm: Warm-up & Introduction (30 min) Welcome remarks and overview of the evening Brief introductions and housekeeping 6:30pm - 6:45pm: Demo 1 (15 min: 10 min demo + 5 min Q&A) Presenter: Gaëlle Agahozo (in-person) Demo project: Using AI to sell branded clothes from Rwanda 6:45pm - 7:00pm: Demo 2 (15 min: 10 min demo + 5 min Q&A) Presenter: Amita Shukla (in-person) Demo project: Adding soon. 7:00pm - 7:15pm: Demo 3 (15 min: 10 min demo + 5 min Q&A) Presenter: Neelanjana Dutta (remote) Demo project: Juni - AI-powered parenting app 7:15pm - 7:30pm: Demo 4 (15 min: 10 min demo + 5 min Q&A) Presenter: Trupeer.ai (remote) (tentitive) Demo project: AI video platform for your software lifecycle 7:30pm - 8:00pm: Open Networking & Conversations (30 min) Continue discussions with presenters and attendees Exchange contacts and ideas We'll do a couple AI demos to spark ideas and conversation, as well as general networking with fellow women in AI, Machine Learning and Data Science. Whether you're exploring AI casually or building something ambitious, this is a relaxed, welcoming space to learn from others and share what you're working on. Bring a demo if you have one! This could be a side project, startup or a AI/ML project at your company -- demos won't be recorded. It doesn't have to be polished! We're demoing for support and community. We will have time for adhoc demos, but if you want to save a dedicated spot early on, let us know what you are demoing here! This event is hosted by our wonderful partners at BrainStation. BrainStation is a global leader in digital skills training and workforce transformation, offering certificate courses and bootcamps in disciplines such as Data Science, UX Design, Digital Marketing, and Product Management. In addition to education, BrainStation hosts a wide range of industry events, panel discussions, and thought leadership sessions that connect professionals, hiring partners, and industry leaders. With campuses in major cities and a strong online presence, BrainStation empowers individuals and organizations to thrive in the digital economy. Stay connected: WiMLDS NYC LinkedIn WiMLDS NYC on X Or request to join our Slack (send us a message on Meetup or LinkedIn)	AI Demo Night: Learn and Connect
How AI Is Transforming Data Careers — A Panel Discussion 2025-12-10 · 21:15 Chuxin Liu , Gayathri Ramanathan AI is transforming data careers. Roles once centered on modeling and feature engineering are evolving into positions that involve building AI products, crafting prompts, and managing workflows shaped by automation and augmentation. In this panel discussion, ambassadors from Women in Data Science (WiDS) share how they have adapted through this shift—turning personal experiments into company practices, navigating uncertainty, and redefining their professional identities. They’ll also discuss how to future-proof your career by integrating AI into your daily work and career growth strategy. Attendees will leave with a clearer view of how AI is reshaping data careers and practical ideas for how to evolve their own skills, direction, and confidence in an era where AI is not replacing, but redefining, human expertise. AI/ML Data Science	PyData Boston 2025 Video
PyLadiesCon 2025 (Online & Free) 2025-12-05 · 06:00 Event Details Date: December 5–7, 2025 Location: Online (Join from anywhere!) Cost: Free — but registration is required Registration: https://pretix.eu/pyladiescon/2025/ Schedule: https://2025.conference.pyladies.com/en/schedule/ Sprints: https://2025.conference.pyladies.com/en/sprints/ We’re thrilled to announce that registration for PyLadiesCon 2025 is officially open! This free, global online conference brings together Python enthusiasts, professionals, and newcomers from all around the world for three days of learning, inspiration, and connection. 🌟 What Awaits You at PyLadiesCon 2025 This year’s edition of PyLadiesCon is packed with exciting opportunities for learning and community engagement. By registering, you’ll gain access to: Keynote Talks by inspiring leaders shaping the Python ecosystem. Talk Tracks covering topics from data science and machine learning to open source, web development, and community building. Interactive Sessions & Panels where speakers, sponsors, and attendees share insights and experiences. Networking Spaces to connect with participants worldwide and make meaningful professional connections. Language Diversity: Sessions available in multiple languages, reflecting our global community. Live Chat & Discord Access to interact directly with speakers, mentors, and volunteers throughout the event. Whether you’re taking your first steps in Python or have years of experience, there’s a space for you at PyLadiesCon! 🗓 Explore the Program The conference program is now live! Take a look at the full schedule to discover all the talks, panels, and activities planned for PyLadiesCon 2025. Browse by track, language, or topic to find the sessions that inspire you the most — and start planning your PyLadiesCon experience today! 💜 Support PyLadiesCon with a Donation While participation is free, donations help us keep PyLadiesCon and other global initiatives accessible to everyone. Your contribution supports speaker mentorship programs, translation efforts, and resources for women in tech worldwide. Support PyLadiesCon 📝 Ready to Join? Click here to register to secure your spot and explore the program — featuring an inspiring lineup of talks, keynotes, and community sessions. 📢 Spread the Word! Invite your friends, colleagues, and local PyLadies chapters to join the celebration of diversity, learning, and collaboration in Python. Let’s make PyLadiesCon 2025 our most vibrant edition yet!	PyLadiesCon 2025 (Online & Free)
Event Xebia Women in Data third edition 2025-12-03
Talk to your data: Natural language data analysis with Snowflake Intelligence 2025-12-03 · 19:05 Snowflake
How Community Builds Confidence 2025-12-03 · 18:30

From Full-Time Mom to Head of Data and Cloud - Xia He-Bleinagel 2025-11-28 · 18:20 Xia He-Bleinagel – Head of Data & Cloud @ NOW GmbH In this talk, Xia He-Bleinagel, Head of Data & Cloud at NOW GmbH, shares her remarkable journey from studying automotive engineering across Europe to leading modern data, cloud, and engineering teams in Germany. We dive into her transition from hands-on engineering to leadership, how she balanced family with career growth, and what it really takes to succeed in today’s cloud, data, and AI job market. TIMECODES: 00:00 Studying Automotive Engineering Across Europe 08:15 How Andrew Ng Sparked a Machine Learning Journey 11:45 Import–Export Work as an Unexpected Career Boos t17:05 Balancing Family Life with Data Engineering Studies 20:50 From Data Engineer to Head of Data & Cloud 27:46 Building Data Teams & Tackling Tech Debt 30:56 Learning Leadership Through Coaching & Observation 34:17 Management vs. IC: Finding Your Best Fit 38:52 Boosting Developer Productivity with AI Tools 42:47 Succeeding in Germany’s Competitive Data Job Market 46:03 Fast-Track Your Cloud & Data Career 50:03 Mentorship & Supporting Working Moms in Tech 53:03 Cultural & Economic Factors Shaping Women’s Careers 57:13 Top Networking Groups for Women in Data 1:00:13 Turning Domain Expertise into a Data Career Advantage Connect with Xia- Linkedin - https://www.linkedin.com/in/xia-he-bleinagel-51773585/ - Github - https://github.com/Data-Think-2021 - Website - https://datathinker.de/ Connect with DataTalks.Club: - Join the community - https://datatalks.club/slack.html - Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ - Check other upcoming events - https://lu.ma/dtc-events - GitHub: https://github.com/DataTalksClub - LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/ AI/ML Cloud Computing Data Engineering GitHub HTML	DataTalks.Club Listen

Search London's 15th Birthday - Community and Measuring AI in 2026 2026-02-24 · 18:00

Overview

Join us in London to celebrate 15 years of Search, Community and more recently AI in person!

Search London's 15th Birthday Bash!

Join us in person for an epic celebration of 15 years of Search London. Dive into the future with talks on Community and Measuring AI in 2026. It’s the perfect chance to connect, learn, and network with our community. To celebrate turning 15, we are teaming up with Search 'n Stuff. Come and meet and network with digital marketers from both communities.

🗓️ Date : Tuesday, 24 February, 2026 ⏰ Time : 18:00 – 23:00 📍 Venue : Farringdon - venue to be confirmed in January

Buy your ticket today on Evenbrite to secure your place.

Meet our speakers!

Come and learn from our speakers: Erin and Jon !

Erin Simmons (They / Them) Erin is Managing Director at the Women in Tech SEO community - focused on democratising successful careers through connection, education, opportunities, & amplification. With 15+ years in marketing and analytics, they now focus on better understanding people and connecting them to the relationships and resources that support them most.

Erin will speak about:

Trust → Visibility: SEO Powered by Community

You can AI-engineer your content and website all day, but even if you show up in the answers, you still won’t be chosen if people don’t trust you. Trust isn’t built between a person and a brand; it’s built in the communities, conversations, and human spaces where people help each other make sense of the world.

In this talk, Erin Simmons explores the growing trust gap in search and why the real SEO edge comes from showing up where trust is being exchanged. * When you earn trust, people make you visible -- they reference you in conversations, recommend you in threads, and surface you in the exact places AI and search engines now look for signals. Visibility is a byproduct of trust. * And if you focus on trust first, you’ll show up everywhere that matters: in AI results, in search, and in the moments when people turn to one another to decide what to believe.

Jonathan Moore Jonathan is an independent SEO and analytics consultant based in Cheltenham. With over 15 years of experience, including six years heading up the SEO and analytics for one of the UK's largest independent digital marketing agencies, he has got a knack for digging into the details to find what really matters. He has a degree in Computer Science and works with large data sets, APIs, and scripts the form the backbone of his business.

Whether you need help with technical SEO, analytics audits, or uncovering competitive insights, he provides hand on consultancy for any business.

Jonathan will speak about:

Measuring search disruption: What metrics matter and who needs to know them

This talk is for SEOs who need to understand and communicate the impact of emerging AI-driven search disruptors, including LLMs and AI Overviews.
The audience will learn which metrics genuinely matter and how to communicate the right insights to the right stakeholders at the right moment.
The takeaway is practical advice for measuring disruption that supports sound decisions without getting lost in hyper-tactical detail.

Ticket includes:

✅ Entry to the event ✅ Finger foods ✅ x2 drinks per person (soft drinks, wine and beer) ✅ Access to all talks and networking ✅ Surprise gifts from our network 😉

Early Bird Special with 50% discount until Decemeber 22nd !

We have 50% off the ticket price for the first 30 tickets sold until December 22nd.

Buy your ticket today on Evenbrite and learn from Jonathan and Erin, plus network with up to 120 search professionals with enough food and drinks for everyone.

Search London's 15th Birthday - Community and Measuring AI in 2026

Secure Software Development - Girl Code at CGI 2026-02-05 · 18:00

Waterworks, airports, the energy grid or telephone traffic: all interesting targets in the Netherlands for cyber or hybrid attacks from countries like Russia and China, say intelligence experts. A highly digitalized world asks for a movement from DevOps to DevSecOps, this means us as developers should specialize in security now too! More than ever, a hack on your software can have serious impact on the daily lives of people: from leaking their personal information to shutting down the energy supply to Dutch homes. Think the leak of private data of police in 2024, and the one of the Bevolkingsonderzoek Nederland in 2025. In other words your software might not be responsible for trains to be able to ride, or payments being done through banks, but most companies do process at least some personal data. Just like accessibility, security often is an afterthought, and that will just not do anymore. Security needs to be considered from day one in every software project. Tonight we will give the stage to three developers to talk about their security approach.

Program 17.45 walk in / dinner (vegetarian) 18.30 Intro Girl Code by Ineke Scheffers 18.35 Intro CGI by Janine Tjassens, CGI BU Leader Randstad and North Netherlands

18.40 Secure Software Development - Erica Welling, Software Security Specialist at CGI Erica will discuss several core principles that help make IT structurally more secure. With these insights into our daily work, we can build systems that are not only fast and beautiful, but also resilient and future-proof. 19.00 Supply Chain Attacks: Here to Stay - Jasmijn van Genesen, Application Security Engineer at KVK Supply chain attacks like Shai Hulud show how hackers exploit package managers and CI/CD pipelines to steal secrets. In her talk Jasmijn will plead for protecting the whole chain—not just writing secure code—by making mitigation of risks a standard in DevSecOps. 19.20 Business Logic Flaws: How to Prevent Them in Your APIs - Alexandra Charikova, The Elephant in AppSec podcast host & community manager at Escape Attackers exploit valid features in unintended ways to bypass rules, abuse workflows, or manipulate sensitive operations. In this talk, Alexandra explores real-world API examples, how attackers chain valid actions for harmful outcomes, and how defenders can catch issues early through ie. threat modeling and security integration in CI/CD. 19.40 Q&A with all speakers 20.00 - 21.00 drinks, snacks, mingle

"At CGI, we value and actively work together to foster an environment where every voice is heard and respected and where every member has equal opportunity to share their ideas, lead and grow. Estimates are that less than 30% of technology jobs are held by women. And according to the numbers above that seems a little too generous even. To increase this it is important to inspire and empower women, which is why initiatives such as Girl Code are so important. A little while ago we hosted Girl Code at CGI, in short, it was a great success! Looking at the current (political) climate the topic of diversity is more important than ever. With that in mind we couldn't be more excited to host another Girl Code event!" - Sara Larsson\, Software Architect & Engineer at CGI

Team Ineke Scheffers - organizer and founder Girl Code Sara Larsson - host CGI Jeroen de Bekker - host CGI Frédérique Doek - host CGI Kimberly Bisschops - host CGI

Attendance policy We take attendance. If you RSVP'd but didn't show up, it will have consequences. Also be sure to cancel on time, 'cause cancelling 1 day before will be too late and count as a no-show. (Illness is of course an exception to this rule).

Public transport It's a 5 min walk from station Rotterdam Alexander. At the station take the exit which ISN’T connected to the metro. Find the entrance by following the George Hintzenweg. Call reception at the visitors door, take the stairs up, it's the 1st building on the left.

Car The employee garage is open for us between 17.30 and 18.30. Before or after: use the intercom. The parking garage can be reached along George Hintzenweg. Take the stairs in the middle, then you will find the CGI office on the left (road as orientation).

Accessibility If you need disability parking or use a wheelchair contact us at [email protected], so we can make the right arrangements.

Secure Software Development - Girl Code at CGI

TidyTuesday 2026-01-27 · 23:00

Join R-Ladies Ottawa for a casual evening of programming on Tuesday, January 27th. We'll be participating in TidyTuesday, a weekly data visualization challenge organized by the R for Data Science community.

What is TidyTuesday?

Every week, a new dataset is posted online on the TidyTuesday GitHub repo, and folks from around the world create data visualizations using the dataset. It's an opportunity to put your programming skills into practice using real-world data in a way that's fun! It's also a great way for everyone to learn from each other, by sharing their visualizations and code.

What will the dataset be?

Even we don't know that (yet)! We'll have to wait until the day before the event to know what data we'll be working with. If you're interested in seeing some past datasets, take a look at the examples below, or visit the TidyTuesday GitHub repo to see all of the datasets dating back to 2018.

Examples from past TidyTuesdays:

Do I have to use R?

No! You can use any programming language or visualization software that you want. In fact, Python users from around the globe participate in "TyDyTuesday" on a weekly basis.

Who is this event for?

No previous programming experience is required to participate, and we'll have experienced programmers in the room who can help you get started (or unstuck), if needed.

...But if you want to get the most out of the event, a good way to prepare is to watch the recording of the introduction to data visualization workshop we hosted back in 2024. :)

What should I bring?

Please bring a laptop so you can code along. We recommend that you have RStudio or another IDE (such as VS Code or Positron) installed ahead of time, but we can help you get one installed if needed!
Come ready to learn, share, and contribute to a safe and welcoming community!

How will this event work?

First few minutes of the event: Introductions, and taking a look at the dataset together as a group.
Time to create a data visualization using the language or software of your choice, either on your own or with a (new) friend! Grab a free snack while you're at it :)
Last \~30 minutes of the event: Show and tell session for anyone who would like to share their creation with the group.

What else do I need to know?

This event (like all R-Ladies events) is totally FREE to attend.

The event will take place at Bayview Yards, which is located just a few steps away from the Bayview O-Train station. There is also a free parking lot available for those who are driving. You can find us in the "Training Room", which is on the second floor of the Bayview Yards building.

This is an in-person event with limited space! Please only RSVP if you are able to attend in-person!

***Please note that the mission of R-Ladies is to increase gender diversity in the R community. This event is intended to provide a safe space for women and gender minorities. We ask for male allies to be invited by and accompanied by a woman or gender minority.***

We’re grateful to be part of the Bayview Meetups initiative and extend our thanks to Bayview Yards for generously providing the venue space.

TidyTuesday

Jan 22 - Women in AI 2026-01-22 · 23:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.

Jan 22 - Women in AI

Jan 22 - Women in AI 2026-01-22 · 23:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.

Jan 22 - Women in AI

Jan 22 - Women in AI 2026-01-22 · 23:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.

Jan 22 - Women in AI

Jan 22 - Women in AI 2026-01-22 · 23:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.

Jan 22 - Women in AI

Jan 22 - Women in AI 2026-01-22 · 23:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.

Jan 22 - Women in AI

Jan 22 - Women in AI 2026-01-22 · 17:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.

Jan 22 - Women in AI

Jan 22 - Women in AI 2026-01-22 · 17:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.

Jan 22 - Women in AI

Jan 22 - Women in AI 2026-01-22 · 17:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.

Jan 22 - Women in AI

Data Leadership World Summit 1.0 2025-12-31 · 13:30

1st Anniversary and New Year Celebration'26 Registration: Fill this [Must] Bought to you by Uttar Pradesh Power BI Club & powered by Sessionize.

Agenda: Click here to view

6 tracks:

Technology and Innovation
Career and Growth
Impact and Ethics
Leadership and Culture
Startup and Entrepreneurship
Women and Inclusive Environment

Gain priority, in-person access to our flagship Summit 2.0 by joining this crucial online session. We are distributing a limited batch of 50 exclusive, confirmed seat passes to active online participants. We hope you to see you soon both online & offline. Contact us at [email protected] [email protected] or [email protected]

Data Leadership World Summit 1.0

🎁 Special end-of-year gift: an exclusive Vibe Coding workshop 2025-12-19 · 09:00

🎁 Women in Big Data Paris and SCAI – Sorbonne University invite you to a special end-of-year gift: an exclusive Vibe Coding workshop with Swati Awasthi.

The event aims to showcase how AI is revolutionizing data analytics and driving innovation across sectors. Whether you are new to AI or an experienced professional, this event offers a platform to learn, network, and explore the endless possibilities of AI programming. Don't miss this opportunity to be part of the conversation shaping the future of AI technology.

📅 19 December 2025 🕒 Time details available in this Meetup event 📍 Sorbonne University – Pierre and Marie Curie Campus, Room TD5666 – 105

✨ A dynamic, collaborative way to explore coding from a fresh perspective. This event is free, but registration is required due to limited capacity.

✨Don't forget to bring you laptop !

👉 Save your spot now.

🎁 Women in Big Data Paris et SCAI – Sorbonne Université vous invitent à un cadeau de fin d’année exceptionnel : une séance exclusive de Vibe Coding avec Swati Awasthi. Swati Awasthi, fondatrice de Women in Product India, anime des ateliers reconnus pour leur impact et leur énergie. Sa session à Bengaluru a été unanimement saluée comme l’une des plus inspirantes du sommet. 📅 19 décembre 2025 🕒 Horaires précisés dans l’événement Meetup 📍 Sorbonne Université – Campus Pierre et Marie Curie, salle TD5666 – 105 ✨ Un atelier dynamique pour expérimenter le code autrement, dans une ambiance collaborative. L’évènement est gratuit, mais l’inscription est obligatoire – les places sont limitées. 👉 Réservez votre place dès maintenant.

🎁 Special end-of-year gift: an exclusive Vibe Coding workshop

Predicting women's chronic disease flares from wearables 2025-12-12 · 19:00

Ipek Ensari – Assistant Professor @ Icahn School of Medicine at Mount Sinai

Heart rate variability (HRV) is a well-known digital biomarker and is increasingly available in consumer wearables. However, extracting actionable predictions from HRV data, in particular for clinical use, remains challenging. Using specialized R packages, this presentation demonstrates how to model 24-hour periodic patterns in HRV metrics as non-linear circadian components to predict chronic disease flares. Grounded in real-life data from a NIH-funded longitudinal mHealth-based study of female chronic pelvic pain disorders, we will investigate how mixed-effects cosinor regression accommodates individual variation and complex interactions between circadian parameters and time-varying covariates (menstrual cycle, physical activity, sleep quality). These examples aim to illustrate how patient-generated data from everyday wearables can democratize access to predictive medicine by helping patient-users maximize the benefits of their data to gain predictive insights into their health status.

hrv wearables r cosinor regression mixed-effects cosinor regression digital phenotyping mhealth

The Data-Powered Patient: Predicting Women's Chronic Disease from Wearables

AI Demo Night: Learn and Connect 2025-12-11 · 23:00

Come get the ⚡️AI Spark⚡️ with NYC Women in Machine Learning and Data Science! We are wrapping up the year with an evening of inspiration, demos, learning and connection.

This week is also the New York AI Summit (12/10-12/11) and we have 1 free pass to give to one of our members (provided by NYAI). RSVP to our demo night by 5pm today to be entered into the raffle to win the ticket. One RSVP'd member will be selected by 5pm and will be emailed their ticket. NYAI is our partner for the New York AI Summit.

What to Expect:

6:00pm - 6:30pm: Warm-up & Introduction (30 min)
Welcome remarks and overview of the evening
Brief introductions and housekeeping
6:30pm - 6:45pm: Demo 1 (15 min: 10 min demo + 5 min Q&A)
Presenter: Gaëlle Agahozo (in-person)
Demo project: Using AI to sell branded clothes from Rwanda
6:45pm - 7:00pm: Demo 2 (15 min: 10 min demo + 5 min Q&A)
Presenter: Amita Shukla (in-person)
Demo project: Adding soon.
7:00pm - 7:15pm: Demo 3 (15 min: 10 min demo + 5 min Q&A)
Presenter: Neelanjana Dutta (remote)
Demo project: Juni - AI-powered parenting app
7:15pm - 7:30pm: Demo 4 (15 min: 10 min demo + 5 min Q&A)
Presenter: Trupeer.ai (remote) (tentitive)
Demo project: AI video platform for your software lifecycle
7:30pm - 8:00pm: Open Networking & Conversations (30 min)
Continue discussions with presenters and attendees
Exchange contacts and ideas

We'll do a couple AI demos to spark ideas and conversation, as well as general networking with fellow women in AI, Machine Learning and Data Science. Whether you're exploring AI casually or building something ambitious, this is a relaxed, welcoming space to learn from others and share what you're working on.

Bring a demo if you have one! This could be a side project, startup or a AI/ML project at your company -- demos won't be recorded. It doesn't have to be polished! We're demoing for support and community.

We will have time for adhoc demos, but if you want to save a dedicated spot early on, let us know what you are demoing here! This event is hosted by our wonderful partners at BrainStation.

BrainStation is a global leader in digital skills training and workforce transformation, offering certificate courses and bootcamps in disciplines such as Data Science, UX Design, Digital Marketing, and Product Management. In addition to education, BrainStation hosts a wide range of industry events, panel discussions, and thought leadership sessions that connect professionals, hiring partners, and industry leaders. With campuses in major cities and a strong online presence, BrainStation empowers individuals and organizations to thrive in the digital economy.

Stay connected:

WiMLDS NYC LinkedIn
WiMLDS NYC on X
Or request to join our Slack (send us a message on Meetup or LinkedIn)

AI Demo Night: Learn and Connect

How AI Is Transforming Data Careers — A Panel Discussion 2025-12-10 · 21:15

Chuxin Liu , Gayathri Ramanathan

AI is transforming data careers. Roles once centered on modeling and feature engineering are evolving into positions that involve building AI products, crafting prompts, and managing workflows shaped by automation and augmentation. In this panel discussion, ambassadors from Women in Data Science (WiDS) share how they have adapted through this shift—turning personal experiments into company practices, navigating uncertainty, and redefining their professional identities. They’ll also discuss how to future-proof your career by integrating AI into your daily work and career growth strategy. Attendees will leave with a clearer view of how AI is reshaping data careers and practical ideas for how to evolve their own skills, direction, and confidence in an era where AI is not replacing, but redefining, human expertise.

AI/ML Data Science

PyData Boston 2025

Video

PyLadiesCon 2025 (Online & Free) 2025-12-05 · 06:00

Event Details

Date: December 5–7, 2025
Location: Online (Join from anywhere!)
Cost: Free — but registration is required
Registration: https://pretix.eu/pyladiescon/2025/
Schedule: https://2025.conference.pyladies.com/en/schedule/
Sprints: https://2025.conference.pyladies.com/en/sprints/

We’re thrilled to announce that registration for PyLadiesCon 2025 is officially open! This free, global online conference brings together Python enthusiasts, professionals, and newcomers from all around the world for three days of learning, inspiration, and connection.

🌟 What Awaits You at PyLadiesCon 2025

This year’s edition of PyLadiesCon is packed with exciting opportunities for learning and community engagement. By registering, you’ll gain access to:

Keynote Talks by inspiring leaders shaping the Python ecosystem.
Talk Tracks covering topics from data science and machine learning to open source, web development, and community building.
Interactive Sessions & Panels where speakers, sponsors, and attendees share insights and experiences.
Networking Spaces to connect with participants worldwide and make meaningful professional connections.
Language Diversity: Sessions available in multiple languages, reflecting our global community.
Live Chat & Discord Access to interact directly with speakers, mentors, and volunteers throughout the event.

Whether you’re taking your first steps in Python or have years of experience, there’s a space for you at PyLadiesCon!

🗓 Explore the Program

The conference program is now live! Take a look at the full schedule to discover all the talks, panels, and activities planned for PyLadiesCon 2025. Browse by track, language, or topic to find the sessions that inspire you the most — and start planning your PyLadiesCon experience today!

💜 Support PyLadiesCon with a Donation

While participation is free, donations help us keep PyLadiesCon and other global initiatives accessible to everyone. Your contribution supports speaker mentorship programs, translation efforts, and resources for women in tech worldwide. Support PyLadiesCon

📝 Ready to Join?

Click here to register to secure your spot and explore the program — featuring an inspiring lineup of talks, keynotes, and community sessions.

📢 Spread the Word!

Invite your friends, colleagues, and local PyLadies chapters to join the celebration of diversity, learning, and collaboration in Python. Let’s make PyLadiesCon 2025 our most vibrant edition yet!

PyLadiesCon 2025 (Online & Free)

From Full-Time Mom to Head of Data and Cloud - Xia He-Bleinagel 2025-11-28 · 18:20

Xia He-Bleinagel – Head of Data & Cloud @ NOW GmbH

In this talk, Xia He-Bleinagel, Head of Data & Cloud at NOW GmbH, shares her remarkable journey from studying automotive engineering across Europe to leading modern data, cloud, and engineering teams in Germany. We dive into her transition from hands-on engineering to leadership, how she balanced family with career growth, and what it really takes to succeed in today’s cloud, data, and AI job market.

TIMECODES: 00:00 Studying Automotive Engineering Across Europe 08:15 How Andrew Ng Sparked a Machine Learning Journey 11:45 Import–Export Work as an Unexpected Career Boos t17:05 Balancing Family Life with Data Engineering Studies 20:50 From Data Engineer to Head of Data & Cloud 27:46 Building Data Teams & Tackling Tech Debt 30:56 Learning Leadership Through Coaching & Observation 34:17 Management vs. IC: Finding Your Best Fit 38:52 Boosting Developer Productivity with AI Tools 42:47 Succeeding in Germany’s Competitive Data Job Market 46:03 Fast-Track Your Cloud & Data Career 50:03 Mentorship & Supporting Working Moms in Tech 53:03 Cultural & Economic Factors Shaping Women’s Careers 57:13 Top Networking Groups for Women in Data 1:00:13 Turning Domain Expertise into a Data Career Advantage

Connect with Xia- Linkedin - https://www.linkedin.com/in/xia-he-bleinagel-51773585/ - Github - https://github.com/Data-Think-2021 - Website - https://datathinker.de/

Connect with DataTalks.Club: - Join the community - https://datatalks.club/slack.html - Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ - Check other upcoming events - https://lu.ma/dtc-events - GitHub: https://github.com/DataTalksClub - LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

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Search London's 15th Birthday Bash!

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Early Bird Special with 50% discount until Decemeber 22nd !

🌟 What Awaits You at PyLadiesCon 2025

🗓 Explore the Program

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