Chicago, IL 60611 United States
Overview
APPLY here
DEADLINE: Applications will be accepted on a rolling basis. We will be evaluating/selecting and notifying candidates monthly while space remains.
Overview
Modern imaging now allows us to observe living systems across molecular, cellular, and tissue scales with unprecedented precision, yet our ability to extract mechanistic understanding to “learn the rules of life” from these data remains limited. Unlike molecular “omics” data, imaging captures continuous spatial and temporal information—shapes, motions, forces, concentrations and chemical potentials—that are inherently complex to represent, analyze, and interpret. The challenge lies in transforming this deluge of high-dimensional, dynamic spatiotemporal data into quantitative models, and linking these to other multi-modal data (e.g. proteomics, genomics, metabolomics), to reveal the governing principles of biological organization and dynamics across scales.
The goal of the program is to accelerate the development of image-based scientific machine learning for biological dynamics. Such approaches will enable “learning the laws” of biology directly from experimental images and movies, yielding new insight into how complex forms and behaviors emerge and evolve from the scales of sub-cellular structures to whole organs. Building shared benchmarks, interpretable methodologies, open tools, and cross-disciplinary collaborations will accelerate discovery and lay the foundation for a new era of AI-enabled scientific discovery for theory in biology across scales. We aim to (rapidly) bring together physicists, computer scientists, mathematicians and biologists to build on the current momentum in this evolving field.
Schedule/Activities
Scholars in Residence
We will select 16-18 scholars to be in residence at NITMB during the entirety of the 6-week program (February 1, 2027 to March 12, 2027). NITMB will pay for travel and lodging during the 6 weeks while at NITMB.
DEADLINE: Applications will be accepted on a rolling basis. We will be evaluating/selecting and notifying candidates monthly while space remains.
APPLY here
Short Workshops
We invite proposals for small, focused workshops (1-3 days) to be held as part of the NITMB Scientific Focus Program on Image-Based Scientific Machine Learning for Theories of Biological Dynamics Across Scales. Submissions should clearly articulate the workshop’s focus, intended outcomes, and how it complements the scientific focus. Proposals should be submitted to programs@nitmb.org.
Large Workshops
During the long program there will be three large workshops (4-5 days, 55-75 participants) associated with the goals of the program.
Workshop 1: “Image-based Scientific ML for advancing theory in biology” (February 1-5, 2027) Organizers: Aaron Dinner (University of Chicago), Ehssan Nazockdast (Allen Institute, Cell Science), Herve Turlier (Collège de France)
This workshop will focus on data-driven methods to employ imaging data in model discovery and building. This will include: force inference techniques, physics-constrained AI modeling approaches and data-driven model discovery. Apply now.
Workshop 2: “Information-theoretic methods for learning dynamics” (February 8-12, 2027) Organizers: Kristofer Bouchard (Lawrence Berkeley National Laboratory), Ilya Nemenman (Emory University) Apply now.
Workshop 3: “Learning Multivariate Spatio‑Temporal Dynamical Models” (March 1-5, 2027) Organizers: Wenying Shou (University College London), Margaret Johnson (Johns Hopkins University), Yanlan Mao (University College London), Jianhua Xing (University of Pittsburgh). Apply now.
Other Activities during Long Program
We will select and support other types of activities during the Long Program.
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Small, focused workshops (~20 people, 1-3 days)
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Dedicated Hackathons & Training
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Opportunity for short term visitors who wish to participate in activities some of the activities
Other Activities outside the dates of the Long Program
There will be synergistic activities associated with upcoming NITMB workshops to build momentum throughout the year, and after the long program.
Tutorials/Discussions for Planned Workshops:
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Cells States & Transitions (June 30-July 2, 2026)
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Pre Workshop Tutorials and Poster Session (June 29)
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Optimal Transport and Learning Dynamical Systems Equations by Elizabeth Jerison (12:15-1:45)
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AI Tools/Methodologies to Learn Cell State Transitions From Single Cell Data (2:00-3:30)
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Speaker: Samantha Riesenfeld (University of Chicago)
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Poster Session (4:00-5:00)
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Biological Function and Space & Time: From Forces & Cues to Emergent Decision Making (Sept 14-18, 2026)
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Representing and Learning Morphology in Biology (Oct 19-23, 2026)
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Participants
Image-Based Scientific Machine Learning for Advancing Theory in Biology
Organizers
- Aaron Dinner, University of Chicago
- Ehssan Nazockdast, Allen Institute
- Herve Turlier, French National Centre for Scientific Research
Participants
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Hervé Turlier – CNRS
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Aaron Dinner – University of Chicago
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Jin Wang – Stony Brook University
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Ondrej Maxian – University of Notre Dame
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Ivo Sbalzarini – University of Zurich
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Mor Nitzan – Hebrew University
Information-theoretic methods for learning dynamics
Organizers
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Kristof Bouchard, Lawrence Berkeley National Laboratory
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Ilya Nemenman, Emory University
Participants
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El Hady Ahmed – MPI Animal Behavior / U Konstanz
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Ahmed Elhady – University of Konstanz / Max Planck Institute of Animal Behavior
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Stephens Greg – VU Amsterdam
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Sidhartha Goyal – University of Toronto
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Allon Klein – Harvard Medical School
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David Schwab – CUNY Graduate Center
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Greg Stephens – Vrije Universiteit Amsterdam & OIST Graduate University
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Vincenzo Vitelli – University of Chicago
Learning Multivariate Spatio‑Temporal Dynamical Models
Organizers
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Wenying Shou, University College London
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Margaret Johnson, Johns Hopkins University
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Yanlan Mao, University College London
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Jianhua Xing, University of Pittsburgh
Participants
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Alex Browning – University of Melbourne
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Rebecca Crossley – University of Oxford
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Carles Falco – University of Oxford
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Rikki Garner – The University of Texas at Austin
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Brian Munsky – Colorado State University
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Hervé Turlier – CNRS