Part 1
Title: Accelerating (Astro)chemical discovery with machine learned atomistic models and Computer Vision for Scientific Discovery
Presenter: Kelvin Lee, Senior Scientific Machine Learning Engineer at NVIDIA

Part 2
Title: Computer Vision for Scientific Discovery
Presenter: Zezhou Cheng, Tenure-track assistant professor in the Department of Computer Science at the University of Virginia

Link to recording

11 AM CT / 12PM ET

Venue: In person at NRAO

Zoom https://utexas.zoom.us/j/87159746528?pwd=ouQu8lN9ARbb6aRFpvdf6Ddb1Oqa8B.1
Meeting ID: 871 5974 6528 | Passcode: 996082
Join by SIP: 87159746528@zoomcrc.com 

Part 1 Abstract: Among the many rapidly developing and expanding fields in AI/ML, applications to atomistic modeling are perhaps one of the most exciting, owing to advances in model architectures and the growing availability of large-scale datasets. Perhaps most exemplary of these capabilities are those used in biomolecular modeling, such as those from the AlphaFold/RoseTTAFold families, which were awarded the 2024 Nobel Prize in Chemistry. These models feature a host of capabilities, including the ability to respect Euclidean symmetries.

While it may not have received nearly as much public attention, the same modeling principles are invariant to translation when applied outside the life sciences, namely in the chemical and materials sciences. In this talk, I will discuss some of the recent advances in the design of machine learned interatomic potentials, how they're trained, and their capabilities and applications in fully atomistic simulations. In the final part of my talk, I will also discuss some potential use cases where the same models can potentially be used to drive astrochemical and observational/spectroscopic discovery.

Part 2 Abstract: Artificial intelligence has recently made remarkable contributions across scientific fields. Within AI, computer vision—focused on enabling machines to see and interpret the 3D visual world—has become a key driver of progress. In this talk, I will first highlight our efforts in applying computer vision to pressing challenges in climate change and materials discovery. I will then present our advances in 3D reconstruction and scene understanding, a core task in computer vision. Finally, I will discuss the broader potential of these techniques to accelerate discovery in astronomy and beyond.

Speaker Bios:

Kelvin is currently a Senior Scientific Machine Learning engineer at NVIDIA, where he works on developing high-performance AI workflows to the chemical and physical sciences. Kelvin completed his PhD on laser-induced reaction dynamics in 2017 at the University of New South Wales in Sydney, Australia, under the guidance of Drs. Scott Kable and Meredith Jordan. Afterwards, he held postdoctoral research positions at the Center for Astrophysics | Harvard & Smithsonian (2017 - 2020), working with Dr. Michael McCarthy, and MIT Chemistry (2020 - 2021), working with Dr. Brett McGuire on problems ranging from high-resolution molecular spectroscopy, to automated analysis of astronomical spectra to machine learning for unknown molecule identification. Kelvin is the proud father of two cats, lives in the beautiful Pacific Northwest, and lists hiking and sim racing as his hobbies.

Zezhou is a tenure-track assistant professor in the Department of Computer Science at the University of Virginia, leading the Computer Vision Lab. Before joining UVA, Zezhou was a Postdoctoral Researcher at Caltech, advised by Georgia Gkioxari. Zezhou obtained their Ph.D. in Computer Science at UMass Amherst in 2023, where I was co-advised by Subhransu Maji and Daniel Sheldon. I received my Bachelor's degree at Sichuan University in China in 2015. During my undergraduate studies, I worked with Qingxiong Yang and Bin Sheng at Shanghai Jiao Tong University.