Dear Colleagues, We will be livestreaming the upcoming workshop "Data-Driven Discovery: AI and Modeling in Biology" organized by Anton Arkhipov (Allen Institute), Tatiana Engel (Princeton), Michael Brenner (Harvard/Google), and Stephen Saalfeld (Janelia). Hosted by the Allen Institute on September 23-25, 2024, the meeting brings together experts who will discuss how problems in biology are being solved using recent developments in both AI and modeling. Please see the streaming link at the meeting website: https://alleninstitute.org/events/data-driven-discovery-ai-and-modeling-in-b... The goal of this meeting is to explore how problems in biology are being solved using cutting-edge computational approaches, with the focus on recent developments in both AI and modeling, including merging the two. Biology is a broad field. We aim to connect researchers across a wide range of computational biology to learn from each others’ approaches. Agenda is below (all times listed as Pacific Standard Time). ================================================== Monday, September 23, 2024 2:00–2:15pm Welcome and opening remarks 2:15–3:15pm KEYNOTE- David Baker, University of Washington Protein design for molecular recognition 3:15–3:45pm Bing Brunton, University of Washington Embodied intelligence through integrated neuromechanical models of natural behavior 3:45–4:15pm Andreas Tolias, Stanford University NeuroAI: Building Digital Twins of the Brain Tuesday, September 24, 2024 9:00–9:30am Matheus Viana, Allen Institute for Cell Science Towards a holistic and quantitative stem cell state landscape 9:30–10:00am Kim Stachenfeld, Columbia University/Google DeepMind Learning to Simulate and Control Fluid Dynamics with Graph Neural Networks 10:00–10:30am Armita Nourmohammad, University of Washington Learning the shape of the protein and immune universe 10:30-11:00am Break 11:00–11:30am Mariano Gabitto, Allen Institute for Brain Science Deep generative models for the multimodal analysis of single-cell datasets 11:30–12:00pm Roy Kishony, Technion-Israel Institute of Technology AI driven science 12:00–12:30pm Stefan Mihalas, Allen Institute Why is the activity in the brain so variable? 12:30-2:00pm Break 2:00–2:30pm Michael Elowitz, California Institute of Technology Many-to-many protein networks as flexible computational modules Wednesday, September 25, 2024 9:00–10:00am KEYNOTE- Emily Fox, Stanford University, insitro Machine Learning for Better Medicines 10:00–10:30am Xiaojun Li, Allen Institute for Immunology Application of AI in Immunology Research 10:30-11:00am Break 11:00–11:30am Gokul Upadhyayula, University of California Berkeley Navigating challenges and opportunities with high-resolution in vivo imaging 11:30–12:00pm Laura Driscoll, Allen Institute for Neural Dynamics Fast and slow learning in artificial and biological networks 12:00–12:30pm Eric Shea-Brown, University of Washington Assigning credit through the "other” connectome 12:30-2:00pm Break 2:00–2:30pm Viren Jain, Google Simulating a zebrafish brain with functional connectomics and AI 2:30–3:00pm Kristin Branson - Janelia Research Campus, Howard Hughes Medical Institute What can we learn from deep-learning-based forecasting models of biological time series? 3:00–3:30pm Ben Cowley, Cold Spring Harbor Laboratory Mapping model units to visual neurons reveals population code for social behavior ================================================== Best regards, Anton Arkhipov Investigator, Allen Institute