event
Tim de Silva | AI Financial Advice: Supply, Demand, and Life Cycle Implications
SeminarOct 07, 202612:00 PM - 1:15 PM
October
07
2026
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This session covers the foundational knowledge of GPUs, including their architecture, functionality, and applications in computing. It provides an introduction to GPU computing through the lens of the Marlowe SuperPod and prepares learners for advanced topics such as GPU-accelerated data science and machine learning.
Location: CoDa W401
This session covers the foundational knowledge of GPUs, including their architecture, functionality, and applications in computing. It provides an introduction to GPU computing through the lens of the Marlowe SuperPod and prepares learners for advanced topics such as GPU-accelerated data science and machine learning.
Location: CoDa W401
For 9/23 and 10/7 only, sessions are at the David Packard Electrical Engineering Building, Room 101. Each session features a different Stanford speaker; talk titles are announced by the organizers.
For 9/23 and 10/7 only, sessions are at the David Packard Electrical Engineering Building, Room 101. Each session features a different Stanford speaker; talk titles are announced by the organizers.
The Center for Decoding the Universe is a new center jointly supported by Stanford Data Science (SDS) and the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC). The Center brings together researchers from across campus and SLAC to collaborate on leveraging complex data to inform physical inference.
This Center will pursue answers to the biggest open questions about the universe. On the largest scales, we seek to understand the nature of dark matter and dark energy, and how the structure of the universe as a whole has evolved over cosmic time. We are also investigating the physics that drives the sources observed in the universe—stars, black holes, violent explosions, galaxies, and other exotic or yet-to-be-discovered objects. And we seek to understand our own origins: the structure of our home Milky Way galaxy, and how stars, planets, and eventually life formed within it. We pursue these questions using massive, multi-modal data sets and large numerical simulations.
Astrophysical data and analysis techniques share many commonalities with other domains. This inaugural quarterly Forum will kickstart the Center’s work of identifying and nourishing new ways to apply data science techniques to astrophysical questions. We will hear three broad talks introducing big open questions in astrophysics. Each will be juxtaposed with a talk from a non-astrophysicist, introducing areas of inquiry that use similar data or inference methodologies in different contexts. In each session, we will also hear a series of shorter talks, selected to inspire cross-disciplinary discussion.
| Time | Session | Speaker(s) |
|---|---|---|
| 12:00 - 1:00 PM | Registration & Lunch | |
| 1:00 - 2:10 PM | Session 1 (videos) | |
| 1:00 - 1:10 PM | Introduction and Welcome | Chris Mentzel & Risa Wechsler |
| 1:10 - 1:30 PM | Astro Framing: Where Do We Live? The Milky Way and Beyond | Susan Clark & Risa Wechsler |
| 1:30 - 1:50 PM | Complementary Methodology | Surya Ganguli |
| 1:50 - 1:57 PM | Recovering Physical Fields from Astronomical Data | Philipp Frank |
| 1:57 - 2:04 PM | Despreading with Energy Conserving Descent | Henry Zheng |
| 2:04 - 2:10 PM | Session 1 Discussion | |
| 2:10 - 2:30 PM | Break | |
| 2:30 - 3:40 PM | Session 2 (videos) | |
| 2:30 - 2:50 PM | Astro Framing: How Does the Universe Work? The Physics of Astrophysical Objects | Dalya Baron & Adam Bolton |
| 2:50 - 2:57 PM | Sequence Modeling and Design from Molecular to Genome Scale with Evo | Eric Nguyen |
| 2:57 - 3:04 PM | Prediction-Powered Inference | Tijana Zrnic |
| 3:04 - 3:10 PM | Session 2 Discussion | |
| 3:10 - 3:30 PM | Foundation Models for Solving Challenging Inverse Problems | Gordon Wetzstein |
| 3:30 - 3:50 PM | Break | |
| 3:50 - 5:00 PM | Session 3 (videos) | |
| 3:50 - 4:10 PM | Astro Framing: What is the Universe Made of? Cosmological Inference | Emmanuel Schaan & Phil Marshall |
| 4:10 - 4:30 PM | Is Distribution Shift Still an AI Problem? | Sanmi Koyejo |
| 4:30 - 4:37 PM | Simulation-Based Inference for 3D Galaxy Clustering | Kate Storey-Fisher |
| 4:37 - 4:44 PM | Modeling Strongly Lensed Quasars with Neural Posterior Estimation | Sydney Erickson |
| 4:44 - 5:00 PM | Session 3 Discussion | |
| 5:00 - 6:00 PM | Networking Reception |
