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🎉 Come along to Women in Scala! 🎉 In this event you'll hear from some experienced Scala developers - who also happen to be women! This will be a fireside-chat-style panel discussion, where we will hear from our panelists' experiences, such as how they got into Scala, experiences on different teams, their view of the industry, navigating career progression, and their various interests and journeys. There will be plenty of opportunity to ask questions and network with other women in the community.

Please note that you must identify as a woman in order to attend this event.

Agenda 6:00pm - 🥤 Doors open. Come along and grab a drink! 6:45pm - 🗣️ Panel Discussion 8:15pm - 🍕 Socialising: Join us for some free food and drinks! Vegan, vegetarian and gluten-free options are provided. Let us know if you'd like something special - we'd be happy to accommodate. 9:00pm - 🍻 Join us at a pub!

⭐ Lydia Skuse ⭐ Lydia is a Principal Backend Engineer at ClearScore. She initially learned programming on the job, gaining experience across a variety of languages; however it was Scala’s elegance and expressive power that captured her complete attention. Lydia is particularly impressed by the ongoing efforts to evolve and refine Scala, including with the release of Scala 3.

Beyond her technical work, Lydia has played a key role in shaping ClearScore's backend engineering culture, notably leading the company's backend hiring programme for the past five years. She actively champions career changers in technology, ensuring that aspiring engineers from all backgrounds have the opportunity to thrive in the field.

⭐ Zainab Ali ⭐ Zainab is a functional programming trainer at Pure Async. She maintains several open source libraries in the Scala ecosystem, organizes the London Scala User Group, is a community representative for the Scala Center and proud to be a Scala ambassador.

She discovered the joy of programming a decade ago by designing game engines in her spare time. She's since worked as a functional programmer in a variety of sectors, such as finance, accessibility and advertizing, and now enjoys teaching in industry.

As well as coding, she's likes her cat, climbing, singing, DIY and all sorts of crafts.

⭐ Sophie Collard ⭐ Sophie is a Software Engineer and ex-Data Scientist with a fondness for strongly typed functional languages such as Scala and Elm. She currently works as a Backend Engineer for DigitalGenius while exploring climate tech startup ideas in her free time.

She discovered Scala a decade ago while eavesdropping on the engineering team next to her and catching word of an obscure programming language and paradigm which held the promise of improved domain modelling and considerably fewer bugs.

When she isn't sitting behind her laptop, she enjoys learning to sail, hiking in the French Alps and long-distance train journeys.

⭐ Maria Livia Chiorean ⭐ Maria began her career as a Scala Developer nearly a decade ago, building her industry experience in the media and travel sectors. Throughout her journey, she has been a passionate advocate for the Scala community.

Over the past three years, Maria has transitioned into engineering management, embracing the challenges of managing teams at Expedia.

⭐ Chen Wang and Raluca Cocioban ⭐ Chen Wang and Raluca Cocioban are from Morgan Stanley Institutional Securities Technology division, they both work on the Optimus platform.

Raluca is a Vice President in the execution engine team with a strong focus on caching, profiling tools and optimizations for Scala applications. Raluca has extensive experience of building highly scalable, uniform infrastructure that can be applied in a variety of different financial services contexts. She graduated from UCL with a Masters in Computer Science.

Optimus is a large platform within IST, that is used to deliver applications from trade entry, through risk management to back office functions. The platform relies on constructs from functional programming being combined with a new programming paradigm. The platform is actively developed by more than 1000 developers spread across the globe.

Chen is an Executive Director, globally responsible for the Optimus UI Application Platform and has extensive experience building high-performance, fault tolerant applications with Scala. Chen joined the firm in 2014 working on the Fixed Income Risk Infrastructure platform in Shanghai, and relocated to London in 2017. She graduated from Fudan University with a degree in Software Engineering.

———————————————————— 🗣️ Would you like to present, but are not sure how to start? Give a talk with us and you'll receive mentorship from a trained toastmaster! Get in touch through this form and we'll get you started 🏡 Interested in hosting or supporting us? Please get in touch through this form and we can discuss how you can get involved. 📜 All London Scala User Group events operate under the Scala Community Code of Conduct: https://www.scala-lang.org/conduct/ We encourage each of you to report the breach of the conduct, either anonymously through this form or by contacting one of our team members. We guarantee privacy and confidentiality, as well as that we will take your report seriously and react quickly.

Zoom Link

https://voxel51.com/computer-vision-events/september-14-meetup/

ARMBench: An Object-Centric Benchmark Dataset for Robotic Manipulation

Amazon Robotic Manipulation Benchmark (ARMBench), is a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. ARMBench contains images, videos, and metadata that corresponds to 235K+ pickand-place activities on 190K+ unique objects. The data is captured at different stages of manipulation, i.e., pre-pick, during transfer, and after placement from a robotic workcell in an Amazon warehouse. Benchmark tasks are proposed by virtue of high-quality annotations and baseline performance evaluation are presented on three visual perception challenges, namely 1) object segmentation in clutter, 2) object identification, and 3) defect detection. ARMBench can be accessed at http://armbench.com.

Chaitanya Mitash is Sr. Applied Scientist at Amazon Robotics. His research focuses on computer vision and manipulation for item manipulation. He received his Ph.D. in computer science from Rutgers University.

Fan Wang is a Research Scientist at Amazon Robotics, with a focus on robotic manipulation and perception. She holds a Ph.D. in electrical and computer engineering from Duke University. Her undergraduate degree was in electrical and mechanical engineering from the University of Edinburgh, UK.

Mani Nambi is a Sr. Applied Scientist at Amazon Robotics. His research focuses on manipulation systems for item and package handling. He received his Ph.D. in mechanical engineering from the University of Utah.

From Model to the Edge, Putting Your Model into Production

This talk delves into the journey from model training to deployment at the edge – an often neglected yet vital aspect of machine learning implementation. It elucidates the essential practices and challenges associated with transitioning an AI model from a controlled environment to real-world edge devices.

Joy is a Machine learning Engineer at Secury360, a startup offers a hardware box that turns your security cameras into a perimeter security system with no false detections. He is responsible for the model training, active learning infrastructure and managing the labeling team. If you have been in the FiftyOne Slack you probably have seen him around.

Optimizing Distributed Fine-Tuning Workloads for Stable Diffusion with the Intel Extension for PyTorch on AWS

In this talk, we explore the use of the Intel Extension for PyTorch to optimize a vision generative AI workload. The vision workload focuses on the fine-tuning of a stable diffusion model, on the AWS cloud using Intel’s 4th Generation Xeon Processors. We leverage optimizations like Intel Advanced Matrix Extensions (AMX) and mixed-precision with BF16 and FP32, to speed up training. Attendees can expect (1) A technical dive into the workload and solution (2) a brief code walkthrough (3) workload setup on AWS, and (4) a short demo.

Eduardo Alvarez is a Senior AI Solutions Engineer at Intel and a specialist in applied deep learning and AI solution design. His background includes building software tools for the energy sector, and his primary interests lie in time-series analysis, computer vision, and cloud solutions architecture. Additionally, he is a community leader in data science and ML/AI for geosciences.

Zoom Link

https://voxel51.com/computer-vision-events/september-14-meetup/

ARMBench: An Object-Centric Benchmark Dataset for Robotic Manipulation

Amazon Robotic Manipulation Benchmark (ARMBench), is a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. ARMBench contains images, videos, and metadata that corresponds to 235K+ pickand-place activities on 190K+ unique objects. The data is captured at different stages of manipulation, i.e., pre-pick, during transfer, and after placement from a robotic workcell in an Amazon warehouse. Benchmark tasks are proposed by virtue of high-quality annotations and baseline performance evaluation are presented on three visual perception challenges, namely 1) object segmentation in clutter, 2) object identification, and 3) defect detection. ARMBench can be accessed at http://armbench.com.

Chaitanya Mitash is Sr. Applied Scientist at Amazon Robotics. His research focuses on computer vision and manipulation for item manipulation. He received his Ph.D. in computer science from Rutgers University.

Fan Wang is a Research Scientist at Amazon Robotics, with a focus on robotic manipulation and perception. She holds a Ph.D. in electrical and computer engineering from Duke University. Her undergraduate degree was in electrical and mechanical engineering from the University of Edinburgh, UK.

Mani Nambi is a Sr. Applied Scientist at Amazon Robotics. His research focuses on manipulation systems for item and package handling. He received his Ph.D. in mechanical engineering from the University of Utah.

From Model to the Edge, Putting Your Model into Production

This talk delves into the journey from model training to deployment at the edge – an often neglected yet vital aspect of machine learning implementation. It elucidates the essential practices and challenges associated with transitioning an AI model from a controlled environment to real-world edge devices.

Joy is a Machine learning Engineer at Secury360, a startup offers a hardware box that turns your security cameras into a perimeter security system with no false detections. He is responsible for the model training, active learning infrastructure and managing the labeling team. If you have been in the FiftyOne Slack you probably have seen him around.

Optimizing Distributed Fine-Tuning Workloads for Stable Diffusion with the Intel Extension for PyTorch on AWS

In this talk, we explore the use of the Intel Extension for PyTorch to optimize a vision generative AI workload. The vision workload focuses on the fine-tuning of a stable diffusion model, on the AWS cloud using Intel’s 4th Generation Xeon Processors. We leverage optimizations like Intel Advanced Matrix Extensions (AMX) and mixed-precision with BF16 and FP32, to speed up training. Attendees can expect (1) A technical dive into the workload and solution (2) a brief code walkthrough (3) workload setup on AWS, and (4) a short demo.

Eduardo Alvarez is a Senior AI Solutions Engineer at Intel and a specialist in applied deep learning and AI solution design. His background includes building software tools for the energy sector, and his primary interests lie in time-series analysis, computer vision, and cloud solutions architecture. Additionally, he is a community leader in data science and ML/AI for geosciences.

Zoom Link

https://voxel51.com/computer-vision-events/september-14-meetup/

ARMBench: An Object-Centric Benchmark Dataset for Robotic Manipulation

Amazon Robotic Manipulation Benchmark (ARMBench), is a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. ARMBench contains images, videos, and metadata that corresponds to 235K+ pickand-place activities on 190K+ unique objects. The data is captured at different stages of manipulation, i.e., pre-pick, during transfer, and after placement from a robotic workcell in an Amazon warehouse. Benchmark tasks are proposed by virtue of high-quality annotations and baseline performance evaluation are presented on three visual perception challenges, namely 1) object segmentation in clutter, 2) object identification, and 3) defect detection. ARMBench can be accessed at http://armbench.com.

Chaitanya Mitash is Sr. Applied Scientist at Amazon Robotics. His research focuses on computer vision and manipulation for item manipulation. He received his Ph.D. in computer science from Rutgers University.

Fan Wang is a Research Scientist at Amazon Robotics, with a focus on robotic manipulation and perception. She holds a Ph.D. in electrical and computer engineering from Duke University. Her undergraduate degree was in electrical and mechanical engineering from the University of Edinburgh, UK.

Mani Nambi is a Sr. Applied Scientist at Amazon Robotics. His research focuses on manipulation systems for item and package handling. He received his Ph.D. in mechanical engineering from the University of Utah.

From Model to the Edge, Putting Your Model into Production

This talk delves into the journey from model training to deployment at the edge – an often neglected yet vital aspect of machine learning implementation. It elucidates the essential practices and challenges associated with transitioning an AI model from a controlled environment to real-world edge devices.

Joy is a Machine learning Engineer at Secury360, a startup offers a hardware box that turns your security cameras into a perimeter security system with no false detections. He is responsible for the model training, active learning infrastructure and managing the labeling team. If you have been in the FiftyOne Slack you probably have seen him around.

Optimizing Distributed Fine-Tuning Workloads for Stable Diffusion with the Intel Extension for PyTorch on AWS

In this talk, we explore the use of the Intel Extension for PyTorch to optimize a vision generative AI workload. The vision workload focuses on the fine-tuning of a stable diffusion model, on the AWS cloud using Intel’s 4th Generation Xeon Processors. We leverage optimizations like Intel Advanced Matrix Extensions (AMX) and mixed-precision with BF16 and FP32, to speed up training. Attendees can expect (1) A technical dive into the workload and solution (2) a brief code walkthrough (3) workload setup on AWS, and (4) a short demo.

Eduardo Alvarez is a Senior AI Solutions Engineer at Intel and a specialist in applied deep learning and AI solution design. His background includes building software tools for the energy sector, and his primary interests lie in time-series analysis, computer vision, and cloud solutions architecture. Additionally, he is a community leader in data science and ML/AI for geosciences.

Zoom Link

https://voxel51.com/computer-vision-events/september-14-meetup/

ARMBench: An Object-Centric Benchmark Dataset for Robotic Manipulation

Amazon Robotic Manipulation Benchmark (ARMBench), is a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. ARMBench contains images, videos, and metadata that corresponds to 235K+ pickand-place activities on 190K+ unique objects. The data is captured at different stages of manipulation, i.e., pre-pick, during transfer, and after placement from a robotic workcell in an Amazon warehouse. Benchmark tasks are proposed by virtue of high-quality annotations and baseline performance evaluation are presented on three visual perception challenges, namely 1) object segmentation in clutter, 2) object identification, and 3) defect detection. ARMBench can be accessed at http://armbench.com.

Chaitanya Mitash is Sr. Applied Scientist at Amazon Robotics. His research focuses on computer vision and manipulation for item manipulation. He received his Ph.D. in computer science from Rutgers University.

Fan Wang is a Research Scientist at Amazon Robotics, with a focus on robotic manipulation and perception. She holds a Ph.D. in electrical and computer engineering from Duke University. Her undergraduate degree was in electrical and mechanical engineering from the University of Edinburgh, UK.

Mani Nambi is a Sr. Applied Scientist at Amazon Robotics. His research focuses on manipulation systems for item and package handling. He received his Ph.D. in mechanical engineering from the University of Utah.

From Model to the Edge, Putting Your Model into Production

This talk delves into the journey from model training to deployment at the edge – an often neglected yet vital aspect of machine learning implementation. It elucidates the essential practices and challenges associated with transitioning an AI model from a controlled environment to real-world edge devices.

Joy is a Machine learning Engineer at Secury360, a startup offers a hardware box that turns your security cameras into a perimeter security system with no false detections. He is responsible for the model training, active learning infrastructure and managing the labeling team. If you have been in the FiftyOne Slack you probably have seen him around.

Optimizing Distributed Fine-Tuning Workloads for Stable Diffusion with the Intel Extension for PyTorch on AWS

In this talk, we explore the use of the Intel Extension for PyTorch to optimize a vision generative AI workload. The vision workload focuses on the fine-tuning of a stable diffusion model, on the AWS cloud using Intel’s 4th Generation Xeon Processors. We leverage optimizations like Intel Advanced Matrix Extensions (AMX) and mixed-precision with BF16 and FP32, to speed up training. Attendees can expect (1) A technical dive into the workload and solution (2) a brief code walkthrough (3) workload setup on AWS, and (4) a short demo.

Eduardo Alvarez is a Senior AI Solutions Engineer at Intel and a specialist in applied deep learning and AI solution design. His background includes building software tools for the energy sector, and his primary interests lie in time-series analysis, computer vision, and cloud solutions architecture. Additionally, he is a community leader in data science and ML/AI for geosciences.