Kristen A. Severson
Principal Researcher · Microsoft Research New England · BioML Team
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
- MatterClosed-loop optimization using machine learning for the accelerated design of sustainable cements incorporating algal biomatterMatter, 2025
- arXivPrism2: Unlocking Multi-modal General Pathology AI with Clinical DialoguearXiv preprint arXiv:2506.13063, 2025
- Nat. Med.A Foundation Model for Clinical-Grade Computational Pathology and Rare Cancers DetectionNature Medicine, 2024
- arXivVirchow2: Scaling Self-Supervised Mixed Magnification Models in PathologyarXiv preprint arXiv:2408.00738, 2024
- arXivPrism: A Multi-Modal Generative Foundation Model for Slide-Level HistopathologyarXiv preprint arXiv:2405.10254, 2024
- Nat. Comput. Sci.Identifying Patterns in Amyotrophic Lateral Sclerosis Progression from Sparse Longitudinal DataNature Computational Science, 2022
- Lancet Digit. HealthDiscovery of Parkinson’s Disease States and Disease Progression Modelling: A Longitudinal Data Study Using Machine LearningThe Lancet Digital Health, 2021
- Nat. Energy