[visionlist] Workshop "Data-Driven Discovery: AI and Modeling in Biology" at the Allen Institute

[Anton Arkhipov]

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-biology/

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).

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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
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Best regards,

Anton Arkhipov
Investigator, Allen Institute


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