AI in Molecular Biology
January 1-4, 2026 | Location to be Determined
Scientific Organizers:
David R. Kelley and Jean Fan
In Person
On Demand
January 1-4, 2026 | Location to be Determined
Scientific Organizers:
David R. Kelley and Jean Fan
Available Formats:
In Person
On Demand
Supported by the Directors' Fund
Available Formats: = In Person = On Demand
Thursday, January 1, 2026
Booking Function
Merchandise Options
Registration Options
Registration
4:00–8:00 PM
Welcome Mixer
6:00–8:00 PM
Friday, January 2, 2026
Breakfast
7:00–8:00 AM
Welcome and Keynote Address
8:00–9:00 AM
David Baker †, University of Washington
Protein Design using Deep Learning
Protein Design using Deep Learning
Analyzing and Harnessing Proteins
9:00–11:15 AM
Debora Marks †, Harvard Medical School
Protein Fitness Landscapes
Protein Fitness Landscapes
Martin Steinegger †, Seoul National University
Fast and Accurate Protein Structure Search
Fast and Accurate Protein Structure Search
Short Talk(s) Chosen from Abstracts
Coffee Break
9:30–9:50 AM
On Own for Lunch
11:15–5:00 PM
Poster Setup
11:15–1:00 PM
Poster Viewing
1:00–10:00 PM
Symposia Spotlight 1
2:30–4:30 PM
Coffee Available
4:30–5:00 PM
Parsing and Annotating the Noncoding Genome
5:00–7:00 PM
David R. Kelley, Calico Life Sciences
Predicting Gene Expression from DNA Sequence
Predicting Gene Expression from DNA Sequence
Stein Aerts †, Katholieke Universiteit Leuven
Single-Cell Multiomic Inference of Gene Regulatory Networks
Single-Cell Multiomic Inference of Gene Regulatory Networks
Christina S. Leslie †, Memorial Sloan-Kettering Cancer Center
Machine Learning for Single Cell and Regulatory Genomics in Cancer
Machine Learning for Single Cell and Regulatory Genomics in Cancer
Short Talk(s) Chosen from Abstracts
Social Hour with Lite Bites
7:00–8:00 PM
Posters
7:30–10:00 PM
Saturday, January 3, 2026
Breakfast
7:00–8:00 AM
Mapping Cellular Interactions in Tissues with Spatial Imaging and Genomics
8:00–11:00 AM
Jean Fan, Johns Hopkins University
Machine Learning for Spatial Omics Data Integration
Machine Learning for Spatial Omics Data Integration
Barbara Engelhardt †, Stanford University School of Medicine
Bayesian Methods for Spatial Genomics
Bayesian Methods for Spatial Genomics
Faisal Mahmood †, Brigham and Women's Hospital
Data-Efficient and Multi-Modal Computational Pathology
Data-Efficient and Multi-Modal Computational Pathology
Mingyao Li †, University of Pennsylvania School of Medicine
Detecting and Characterizing Tertiary Lymphoid Structures
Detecting and Characterizing Tertiary Lymphoid Structures
Short Talk(s) Chosen from Abstracts
Coffee Break
9:00–9:20 AM
On Own for Lunch
11:00–5:00 PM
Poster Setup
11:00–1:00 PM
Poster Viewing
1:00–10:00 PM
Career Roundtable
3:00–4:30 PM
Coffee Available
4:30–5:00 PM
Learning a Language of Cellular Morphology from Imaging
5:00–7:00 PM
Anne E. Carpenter, Broad Institute of Harvard and MIT
Finding Disease Phenotypes and Candidate Therapeutics using Cell Painting Images
Finding Disease Phenotypes and Candidate Therapeutics using Cell Painting Images
Daphne Koller †, insitro
Digital Biomarkers from Histopathology
Digital Biomarkers from Histopathology
Juan Caicedo †, Morgridge Institute for Research
Learning Representations for Image-based Profiling of Perturbations
Learning Representations for Image-based Profiling of Perturbations
Berton Earnshaw †, Recursion Pharmaceuticals
Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
Short Talk(s) Chosen from Abstracts
Social Hour with Lite Bites
7:00–8:00 PM
Posters
7:30–10:00 PM
Sunday, January 4, 2026
Breakfast
7:00–8:00 AM
Toward an Integration of AI and Systems Biology
8:00–11:00 AM
George Karniadakis †, Brown University
Integrating Machine Learning into Systems Biology
Integrating Machine Learning into Systems Biology
Jacob Kimmel, NewLimit
Learning Combinatorial Perturbations for Reprogramming
Learning Combinatorial Perturbations for Reprogramming
Hector Garcia Martin †, Lawrence Berkeley National Laboratory
Bioengineering Cells to Produce Biofuels
Bioengineering Cells to Produce Biofuels
Short Talk(s) Chosen from Abstracts
Coffee Break
9:00–9:20 AM
On Own for Lunch
11:00–5:00 PM
Symposia Spotlight 2
2:30–4:30 PM
Coffee Available
4:30–5:00 PM
Extracting Interpretable Insights from Biological AI
5:00–6:45 PM
Su-In Lee †, University of Washington
Explainable AI for Biology and Medicine
Explainable AI for Biology and Medicine
Margaret Mitchell †, Hugging Face
Stronger Together: On the Articulation of Ethical Charters, Legal Tools, and Technical Documentation in ML
Stronger Together: On the Articulation of Ethical Charters, Legal Tools, and Technical Documentation in ML
James Zou †, Stanford University
Advancing Biological Discovery through AI: From Deciphering Life's Codes to Large-Scale Clinic-Genomic Insights
Advancing Biological Discovery through AI: From Deciphering Life's Codes to Large-Scale Clinic-Genomic Insights
Dmitry Kobak †, Tübingen University
Self-Supervised and Unsupervised Learning for 2D Visualization of Scientific Datasets
Self-Supervised and Unsupervised Learning for 2D Visualization of Scientific Datasets
Short Talk(s) Chosen from Abstracts
Meeting Wrap-Up: Outcomes and Future Directions (Organizers)
6:45–7:00 PM
Social Hour with Lite Bites
7:00–8:00 PM
Entertainment
8:00–11:00 PM
Cash Bar
8:00–11:00 PM
Monday, January 5, 2026
Departure
12:00–11:59 PM
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