Multi-dimensional imaging

Biology operates across scales. Molecular machines make decisions in milliseconds. Immune systems adapt over decades. Understanding how these processes connect — and how they go wrong in disease — requires seeing them as they unfold, in real time, at every level of detail.

Today’s imaging forces tradeoffs: resolution or field of view, speed or depth, one modality or another. We’re building integrated platforms that eliminate these tradeoffs, combining frontier imaging with AI to capture biological systems as they actually work.

Our goal is to make AI-powered, multidimensional imaging a foundational technology for biology — one that lets researchers see how living systems function, forecast what they’ll do next, and ultimately redirect cellular behavior to prevent or cure disease.

Our focus areas:

  • Bioforecasting — AI-powered tools that automate the path from imaging to insight, creating the computational foundation for predictive biology and autonomous discovery.
  • Dynamic structural cell biology — High-resolution structural snapshots at critical moments, building the molecular-scale foundation for understanding changes in cellular function at a molecular level.
  • Cell × State — Scalable technologies to decode how cells translate inputs into outputs, enabling prediction and control of cellular behavior across biological contexts.
  • Developmental systems mapping — Comprehensive mapping of vertebrate development, linking cellular dynamics and lineage to multi-omic data.
  • Immunological surveillance — Profile immune responses across scales and lifespans, from cellular interactions to organism-wide dynamics through development, aging, and disease.

“With AI, computer tools, mechanistic models, and theory, we’ve got the ability to do in biology what applied physics, theoretical physics, and engineering did so fruitfully a century ago – building and testing large-scale approaches to hard problems in the field. We now have the ability to extract truly meaningful knowledge from a wealth of biological data.”  — Scott Fraser, president of imaging

News

  • Simple ‘Cocktail’ of Amino Acids Dramatically Boosts Power of Anti-Inflammatory mRNA Therapies and CRISPR Gene Editing

    Adding three common amino acids to lipid nanoparticle injections increased mRNA delivery up to 20-fold, pushed gene editing efficiency to nearly 90%, and suppressed inflammation in a model of acute liver disease.

  • New tool reveals how T cell responses evolve across organs

    By tracking recently activated T cells over time and across tissues, researchers uncover immune dynamics that may inform future therapies for infection, cancer, and autoimmunity

  • Forbes: Gene Editing Has Struggled To Go Commercial. This Nobel Laureate Has A $1 Billion Plan To Fix That.

Research

  • Tissue-specific clonal selection and differentiation of CD4⁺ T cells during infection

    Roham Parsa, Helder Assis, Tiago B.R. de Castro, et al. (2025) | bioRxiv

  • rbio1-training scientific reasoning LLMs with biological world models as soft verifiers

    Ana-Maria Istrate, Fausto Milletari, Fabrizio Castrotorres, et al. (2025) | bioRxiv

  • GREmLN: A Cellular Regulatory Network-Aware Transcriptomics Foundation Model

    Mingxuan Zhang, Vinay Swamy, Rowan Cassius, et al. (2025) | bioRxiv

Team leaders

  • Garrett Greenan

    Garrett Greenan

    Platform Leader, Microscopy (FIB-SEM)

  • Matthias Haury

    Matthias Haury

    Chief Operating Officer

  • Adrian Jacobo

    Adrian Jacobo

    Senior Group Leader, Quantitative Morphogenesis

  • Dari Kimanius

    Dari Kimanius

    Group Leader, AI/ML Algorithms

  • Manuel Leonetti

    Manuel Leonetti

    Research Director, Cell x State

  • Alan Lowe

    Alan Lowe

    Director, Software Engineering, Artificial Intelligence/Machine Learning

Investigator program

Our investigator and residency programs support research and collaborations by some of the best minds in science, medicine, engineering and technology.

Join us in our mission

We are a collaborative community of scientists, engineers, and AI and machine learning experts from across multiple fields who are passionate about tackling complex challenges and share a unified vision of a world without disease.