2025 Data Science Conference Recap: On-Demand Videos, Photos, and Bingo for Data Scientists
Thank you to everyone who made the 2025 Stanford Data Science Conference such a success!
Our fourth annual gathering brought together 250 brilliant minds from across disciplines to explore the power of data in advancing research. The energy, ideas, and connections were truly inspiring.
Catch the highlights and revisit the sessions on our YouTube channel, and don’t miss the photo gallery below to relive the moments. And congrats to the lucky Data Science Bingo winners!
2025 Stanford Data Science Conference On-Demand Videos
Welcome Remarks, Guido Imbens, SDS Faculty Director YouTube
Keynote: "UniLasso”—a novel statistical method for sparse regression, and "LLM-lasso"—sparse regression with LLM assistance; Rob Tibshirani, Professor of DBDS and Statistics, Stanford YouTube
Designing for Impact with Marlowe GPU-Based Computational Instrument:
World Modeling with Probabilistic Structure Integration; Klemen Kotar, Computer Science PhD Candidate YouTube
Advancing Equivariant Graph Neural Networks for Turbulence Modeling; Nikita Kozak, Mechanical Engineering PhD Candidate YouTube
Marlowe Speeds Up Data Processing of Single-Particle Cryo-EM for Large and Flexible Macromolecules; Dong-Hua Chen, Senior Research Scientist YouTube
Catalysts for Collaboration: The Role of Faculty-Led Research Centers in Data Science; Tom Abel, Ramesh Johari, David Lobell, Maya Mathur; Moderator: Elizabeth Wilsey YouTube
Beyond the Fellowship/Farm: Stanford Data Science Alumni Journeys; Emmanuel Balogun (Google X), Johannes Ferstad (OpenAI), Annie Lamar (UC Santa Barbara), Marissa (Lee) Sinopoli (Harvey Mudd College), Daniel Muise (Screenlake), Krishna Rao (Watershed); Moderator: John Chambers YouTube
Poster Preview Talks with SDS Data Science Scholars and SDS Postdoc Fellows YouTube
BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literature; Min Sun
The Importance of Implicit Semantic Context when Classifying Rare Phenomena in Large Scientific Imagery; Ellianna Abrahams
Deep learning enables efficient computation of white shark morphometrics from aerial imagery in Monterey Bay; Alexandra DiGiacomo
Using satellites and machine learning to accelerate impact evaluation; Iván Higuera-Mendieta
Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF; Syrine Belakaria
Towards faithful synthetic data generation via penalized optimal transport network; Sophia Lu
Closing Remarks; Chiara Sabatti YouTube
Below is a gallery of images from the day, courtesy of Paul Sakuma.
This article is a part of the Stanford Data Science legacy publication. Read more about the HAI and Stanford Data Science merger.
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