Kristen A. Severson

Principal Researcher · Microsoft Research New England · BioML Team

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Microsoft Research

Cambridge, MA

I am a principal researcher at Microsoft Research New England where I work on the BioML team. My research focuses on developing machine learning methods for high-impact scientific and healthcare applications, with current emphasis on three areas:

Computational Pathology & Foundation Models. I lead research on large-scale foundation models for digital pathology, including the Virchow and Virchow2 tile-level models and the Prism and Prism2 multi-modal slide-level models. This work was published in Nature Medicine and enables clinical-grade computational pathology and rare cancer detection.

Disease Progression Modeling. I develop probabilistic models that discover disease subtypes and predict progression from longitudinal clinical data. This includes work on Parkinson’s disease (published in The Lancet Digital Health) and ALS (published in Nature Computational Science).

Machine Learning for Science. I apply ML to accelerate scientific discovery, including data-driven prediction of battery cycle life (published in Nature Energy) and Bayesian optimization for sustainable materials design (published in Matter).

Previously, I was a postdoc at the Center for Computational Health and a research staff member in the MIT-IBM Watson AI Lab at IBM Research. I received my PhD in Chemical Engineering from MIT, where I worked in the Braatz lab, and my BS from Carnegie Mellon University.

selected publications

  1. Matter
    Closed-loop optimization using machine learning for the accelerated design of sustainable cements incorporating algal biomatter
    Meng-Yen Lin, Kristen Severson, Paul Grandgeorge, and Eleftheria Roumeli
    Matter, 2025
  2. arXiv
    Prism2: Unlocking Multi-modal General Pathology AI with Clinical Dialogue
    Eugene Vorontsov, George Shaikovski, Adam Casson, Julien Viret, Eric Zimmermann, Neil Tenenholtz, and 13 more authors
    arXiv preprint arXiv:2506.13063, 2025
  3. Nat. Med.
    A Foundation Model for Clinical-Grade Computational Pathology and Rare Cancers Detection
    Eugene Vorontsov, Alican Bozkurt, Adam Casson, George Shaikovski, Michal Zelechowski, Kristen Severson, and 27 more authors
    Nature Medicine, 2024
  4. arXiv
    Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology
    Eric Zimmermann, Eugene Vorontsov, Julien Viret, Adam Casson, Michal Zelechowski, George Shaikovski, and 8 more authors
    arXiv preprint arXiv:2408.00738, 2024
  5. arXiv
    Prism: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology
    George Shaikovski, Adam Casson, Kristen Severson, Eric Zimmermann, Yi Kan Wang, Joseph D. Kunz, and 16 more authors
    arXiv preprint arXiv:2405.10254, 2024
  6. Nat. Comput. Sci.
    Identifying Patterns in Amyotrophic Lateral Sclerosis Progression from Sparse Longitudinal Data
    Divya Ramamoorthy, Kristen Severson, Soumya Ghosh, Karen Sachs, Jonathan D. Glass, Christina N. Fournier, and 1 more author
    Nature Computational Science, 2022
  7. Lancet Digit. Health
    Discovery of Parkinson’s Disease States and Disease Progression Modelling: A Longitudinal Data Study Using Machine Learning
    Kristen A. Severson, Lana M. Chahine, Luba A. Smolensky, Murtaza Dhuliawala, Mark Frasier, Kenney Ng, and 2 more authors
    The Lancet Digital Health, 2021
  8. Nat. Energy
    Data-Driven Prediction of Battery Cycle Life Before Capacity Degradation
    Kristen A. Severson, Peter M. Attia, Norman Jin, Nicholas Perkins, Benben Jiang, Zi Yang, and 8 more authors
    Nature Energy, 2019