talk-data.com talk-data.com

Filter by Source

Select conferences and events

People (24 results)

See all 24 →
Showing 4 results

Activities & events

Title & Speakers Event
Event DataFramed 2025-06-02
Dan Hannah – Associate Director @ SES AI Corporation , Richie – host @ DataCamp , Nick Becker – Group Product Manager @ NVIDIA

GPU acceleration is transforming how data scientists tackle computationally intensive problems in the AI and materials science fields. When dealing with billions of potential molecular combinations or massive datasets requiring dimensionality reduction, traditional CPU approaches often become prohibitively slow and expensive. How can data professionals determine when GPU acceleration will provide meaningful benefits to their workflows? Understanding the right applications for this technology can mean the difference between waiting hours versus minutes for critical results. Nick Becker is a Group Product Manager at NVIDIA, focused on building RAPIDS and the broader accelerated data science ecosystem. Nick has a professional background in technology and government. Prior to NVIDIA, he worked at Enigma Technologies, a data science startup. Before Enigma, he conducted economics research and forecasting at the Federal Reserve Board of Governors, the central bank of the United States. Dan Hannah is an Associate Director at SES AI Corporation. At SES, Dan leads a research program focused on discovering new battery materials using machine learning, chemical informatics, and physics-driven simulations. Prior to joining SES, Dan spent several years as a data scientist in the cybersecurity industry. Dan holds a Ph.D. in Physical Chemistry from Northwestern University and did a postdoctoral fellowship at Berkeley National Lab, where his focus was the discovery of novel inorganic materials for energy applications. In the episode, Richie, Nick, and Dan explore the quest for new battery technologies, the role of data science and machine learning in material discovery, the integration of NVIDIA's GPU technology, the balance between computational simulations and lab work, and much more. Links Mentioned in the Show: NVIDIA RAPIDSSES AI CorporationConnect with Dan and NickCareer Track: Machine Learning Scientist in PythonRelated Episode: Data Science Trends from 2 Kaggle Grandmasters with Jean-Francois Puget, Distinguished Engineer at NVIDIA & Chris Deotte, Senior Data Scientist at NVIDIARewatch sessions from RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

AI/ML Data Science
Chris Deotte – Senior Data Scientist @ NVIDIA , Richie – host @ DataCamp , Jean-Francois Puget – PhD, Distinguished Engineer @ NVIDIA

With AI agents and GPU acceleration at the forefront, data science is entering a new era of efficiency and innovation. How are AI copilots transforming the way data scientists code and solve problems? Are they a reliable partner or a source of new complexities? On the other hand, the move to GPU-accelerated data science tools is revolutionizing model training and experimentation. What does this mean for the future of data science workflows? Explore these cutting-edge developments and their impact on the industry. Jean-Francois got a PhD in machine learning in the previous millennium. Given the AI winter at the time, he worked for a while on mathematical optimization software as dev manager for CPLEX in a startup. He came back to Machine Learning when IBM acquired the startup. Since then he discovered Kaggle and became one of the best Kagglers in the world. He joined NVIDIA 5 years ago and leads the NVIDIA Kaggle Grandmaster team there. Chris Deotte is a senior data scientist at NVIDIA. Chris has a Ph.D. in computational science and mathematics with a thesis on optimizing parallel processing. Chris is a Kaggle 4x grandmaster. In the episode, Richie, Jean-Francois, and Chris explore the transformative role of AI agents in data science, the impact of GPU acceleration on workflows, the evolution of competitive data science techniques, the importance of model evaluation and communication skills, and the future of data science roles in an AI-driven world, and much more. Links Mentioned in the Show: NVIDIANVIDIA RapidsFew shot learningConnect with Jean-Francois on Linkedin and Kaggle and check out Chris on KaggleCourse: Winning a Kaggle Competition in PythonRelated Episode: Becoming a Kaggle GrandmasterSign up to attend RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

AI/ML Data Science IBM
Richie – host @ DataCamp , Jean-Francois Puget – PhD, Distinguished Engineer @ NVIDIA

Oftentimes, Kaggle competitions are looked at as an excellent way for data scientists to sharpen their machine learning skills and become technically excellent. This begs the question, what are the hallmarks of high-performing Kaggle competitors? What makes a Kaggle Grand Master? Today’s guest, Jean-Francois Puget PhD, distinguished engineer at NVIDIA, has achieved this impressive feat three times.  Throughout the episode, Richie and Jean-Francois discuss his background and how he became a Kaggle Grandmaster. He shares his scientific approach to machine learning and how he uses this to consistently achieve high results in Kaggle competitions. Jean-Francois also discusses how NVIDIA employs nine Kaggle Grandmasters and how they use Kaggle experiments to breed innovation in solving their machine learning challenges. He expands on the toolkit he employs in solving Kaggle competitions, and how he has achieved 50X improvements in efficiencies using tools like RAPIDS.  Richie and Jean-Francois also delve into the difference between competitive data science on Kaggle and machine learning work in a real-world setting. They deep dive into the challenges of real-world machine learning, and how to resolve the ambiguities of using machine learning in production that data scientists don’t encounter in Kaggle competitions.

AI/ML Data Science
Rob Thomas – guest , Seth Dobrin – Vice President and Chief Data Officer for IBM Analytics @ IBM , Jean-Francois Puget – PhD, Distinguished Engineer @ NVIDIA , Al Martin – WW VP Technical Sales @ IBM

Send us a text Happy holidays from the Making Data Simple team! Enjoy a rebroadcast of a conversation with Seth Dobrin, Vice President and Chief Data Officer for IBM Analytics, as he and Al explore the strategies and people your company needs to disrupt and succeed in the year ahead. Do you or your team members need new credentials to work in data? Seth also discusses what you need in your toolkit to be a data scientist at IBM.

Show Notes 00.30 Connect with Al Martin on Twitter and LinkedIn. 01.00 Connect with Seth Dobrin on Twitter and LinkedIn. 01.40 Read "What IBM looks for in a Data Scientist" by Seth Dobrin and Jean-Francois Puget. 06.00 Learn more about GDPR.  13.00 Learn more about master data management. 13.05 Learn more about unified governance and integration.  13.25 Learn more about machine learning.  14.00 Connect and learn more about Ginni Rometty.  14.40 Learn more about cognitive computing. 19.35 Connect with Rob Thomas on Twitter and LinkedIn. 21.00 Connect with Jean-Francois Puget on Twitter and LinkedIn. Follow @IBMAnalytics Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

AI/ML Analytics Data Management GDPR/CCPA IBM Master Data Management
Making Data Simple
Showing 4 results