Research Labs

Researcher Michael Berger focuses on massively parallel sequencing of tumor DNA for biomarker discovery and clinical diagnostics.

Computational chemist John Chodera uses statistical mechanics, molecular modeling, and automated biophysical experiments to help identify new potential therapeutics and investigate mechanisms of drug resistance in cancer.

The Greenbaum lab utilizes techniques from statistical physics, information theory, and evolutionary biology to better understand the role of self/non-self discrimination in tumor evolution, model response to immunotherapies, and quantify drivers of virus and cancer evolution.

We take real-world data, clean it and use it to discover new predictive biomarkers to guide cancer management (often with AI), and then implement these findings in clinical practice.

The Lareau Lab synthesizes computational and experimental genomics methods to study how immune cells in our bodies adapt, expand, differentiate, and evolve over an individual’s lifetime.

Our group, the Laboratory of Experimental Cancer Immunogenomics, is a multi-disciplinary team of cancer scientists working at the intersection of genomics, molecular genetics, animal modeling, and biostatistical data sciences.

We develop and clinically deploy novel computational algorithms for analyzing multiscale, multimodal biomedical imaging data.

Computational biologist Dana Pe’er combines single-cell and spatial profiling technologies with machine learning approaches to address fundamental questions in gene regulation, cellular plasticity and cell-cell communication in the contexts of cancer, immunity and development.