#304 Accelerating Data Science with Nick Becker, Technical Product Manager at NVIDIA & Dan Hannah, Associate Director at SES AI

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Jun 2, 2025

Nick Becker, a Technical Product Manager at NVIDIA, and Dan Hannah, Associate Director at SES AI, dive into how GPU acceleration is revolutionizing data science in AI and materials discovery. They discuss the importance of transitioning from CPU to GPU for efficiency and speed, especially in battery technology. Topics include the role of machine learning in developing new materials, leveraging NVIDIA's advanced frameworks, and the impact of dimensionality reduction techniques like UMAP in identifying viable molecular candidates for innovation.

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INSIGHT

GPUs Empower Parallel Data Science

GPUs excel at parallel processing, making them valuable for AI, data frames, clustering, and graph analytics.
This broad applicability explains why GPUs accelerate various data science tasks beyond just neural networks.

ANECDOTE

Hunting Electrolytes with AI

Dan Hannah's team explores new battery electrolytes via AI models and computational chemistry.
They aim to discover novel molecules to improve battery performance by simulating billions of candidates.

ADVICE

Switch to GPUs Seamlessly

When datasets grow large, assess if GPU acceleration benefits your workflow.
Installing NVIDIA Rapids allows seamless switching to GPU with no code changes, boosting efficiency.

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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.

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