Veridical Data Science for Biology 2025

A one-day symposium to advance rigorous computational methods in the biological sciences

About this Event

Veridical Data Science for Biology is a one-day symposium sponsored by QB3-Berkeley that brings together Bay Area researchers at the intersection of computation and biosciences. Grounded in the principles of predictability, computability, and stability, this conference fosters cross-disciplinary dialogue on building reliable, domain-informed models; re-evaluating common practices; and improving the generalizability of computational methods in biology.

The event is organized by graduate students and postdocs from the Center of Computational Biology at UC Berkeley (Sindy Li, Hanlun Jiang, Jules Perez, and Rebecca Eliscu), with (Distinguished Professor of Statistics, Electrical Engineering & Computer Sciences, and Center of Computational Biology) serving as our faculty sponsor.

Call for abstracts

We invite undergraduate students, graduate students and postdoctoral researchers to submit abstracts for lightning talks or posters on topics including (but not limited to):

• Computational biology and data-driven modeling in biosciences
• Methods development or critical evaluation of existing tools
• Applications emphasizing interpretability, robustness, or generalizability
• Reflections on modeling assumptions and their biological grounding

Presentation Formats

• Lightning talks: 5-minute lightning talks with 3-minute Q&A
• Posters: To be presented during an end-of-day reception

Selected abstracts will be evaluated based on scientific merit and alignment with the conference theme. We strongly encourage submissions from trainees of diverse backgrounds.

Please click here to submit an abstract by the submission deadline: 7/4/2025

For more information please email dmRzYjIwMjUgfCBiZXJrZWxleSAhIGVkdQ==.

Agenda

Veridical Data Science towards Cost-Effect Hypothesis Generation in Biology
Host: Bin Yu (Key Note, UC Berkeley)
Towards Veridical Microbiome Analysis through Semisynthetic Data
Host: Kris Sankaran (University of Wisconsin Madison)
Mobility network models of COVID-19 explain inequities and inform reopening
Host: Emma Pierson (UC Berkeley)
How should we identify optimal markers of cell types and cell states?
Host: Michael Oldham (UCSF)
TBD
Host: Lior Pachter (Caltech)
Machine-readable Specifications for Biological Data Science
Host: Sina Booeshaghi (UC Berkeley)
TBD
Host: Will Fithian (UC Berkeley)
Protein language models are biased by unequal sequence sampling
Host: Frances Ding (Genentech)
TBD
Host: Dylan Cable (University of Michigan)