Fighting Cancer with Machine learning
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Research Focus

We are building the Cancer Dependency Map: an effort to systematically identify the chemical and genetic vulnerabilities of different cancer cells, as well as their predictive biomarkers.

Precision cancer medicine

We build predictive models to predict cancer vulnerabilities from genomic profiles of tumors and cancer cell lines.

Cancer Targets Identification

We integrate functional screening and 'omics data to identify novel cancer targets as well as drugs for repurposing.


We develop computational methods and tools to facilitate the analysis of CRISPR screening in cancer models.

Small-molecule screens

We analyze highly-multiplexed small-molecule screening data from the PRISM platform to discover novel cancer therapeutic leads.

Our Team

Katie Campbell

Director, Cancer Data Science

Philip Montgomery

Associate Director

Mike Burger

Computational Biologist

Josh Dempster

Group Leader, Computational Biology

Mustafa Kocak

Computational Scientist II

Isabella Boyle

Associate Computational Biologist II

Jessica Cheng

Software Engineer

Alexandra Mourey

Software Engineer

Randy Creasi

Senior Software Engineer

Simone Zhang

Bioinformatics Engineer

Nayeem Aquib

Software Engineer

Sarah Wessel

Software Engineer

Sam Maffa

Associate Computational Biologist I

YuhJong Liu

Associate Computational Biologist I

Alison Cameron

Associate Computational Biologist I

Alvin Qin

Leading Computational Biologist


James McFarland

Director of Data Science, Generate Biomedicines

Jérémie Kalfon

Senior Computational Associate

Aviad Tsherniak

Founder of CDS

David Wu

MD-PhD Student, University of Pennsylvania

William Colgan

Granduate Student, Computational Biology, MIT

Andrew Boghossian

Neuroscience, UCSF

Kwabena Ofori-Atta

MD-PhD Student, Tri-Institutional Program

Andrew Tang

Sr. Visual Designer

Han Xu

Associate Professor, MD Anderson

Robin Meyers

Graduate Student, Genetics, Stanford University

Jared Jacobsen

Studying for AI Research

Li Wang

Computational Biologist, 10X Genomics

Jordan Bryan

Graduate Student, Statistics, Duke University

Kailash Nakagawa

Undergraduate Student, Stanford University

Quinton Wessells

Graduate Student, Biomedical Informatics, Stanford University

Remi Marenco

Bioinformation Lead, Cancer Cell Line Factory

Guillaume Kugener

Medical Student, USC

Zandra Ho

Medical Student, Brown

Andy Jones

Graduate Student, Computer Science, Princeton University

Jordan Rossen

Graduate Student, Epidemiology, Harvard University

Vickie Wang

MD-PhD Student, Neuroscience, University of Pennsylvania

Mariya Kazachkova

Graduate Student, Biomedical Sciences, UCSD

Phoebe Moh

Graduate Student, Computer Science, University of Maryland

Allie Warren

Associate Computational Biologist II

Neekesh Dharia

Medical Director, Genentech

Nishant Jha

Software Engineer

Josephine Lee

Senior Software Engineer

Yejia Chen

Senior Software Engineer

Gwen Miller

PhD Student, Bioinformatics and Integrative Genomics, Harvard University

Javad Noorbakhsh

Principal Scientist, Kojin Therapeutics

Josh Pan

Research Scientist, Genomics, DeepMind

Ashir Borah

PhD Student, Biological and Medical Informatics; University of California San Francisco

Lena Joesch-Cohen

Associate Computational Biologist II

CDS Outings

Join Us

The Cancer Dependency Map Project (DepMap), an ambitious collaborative effort launched by the Broad Institute’s Cancer Program, aims to systematically identify the genetic and chemical vulnerabilities across human cancers, along with the specific molecular features that can be used to predict which cancers will respond to a given treatment. Using a host of new technologies like CRISPR-based gene editing, pooled drug screening, and next-generation sequencing, we have deeply characterized over a thousand different cancer models and produced some of the largest cancer datasets in the world, which are now being widely used to identify new vulnerabilities in this devastating disease.

Despite the widespread impact of the DepMap project so far, many unanswered questions and critical challenges remain. Now we are poised to launch an exciting next phase of this effort, leveraging cutting-edge new technologies that can, for example, screen the vast space of perturbation combinations, characterize cellular responses with unprecedented detail and single-cell resolution, and leverage next-generation cancer models. To turn these huge, complex data into progress against cancer, we need computational scientists and software engineers. As a member of our team, you will develop new computational and software tools to empower the whole research community in the battle against cancer.

About the team:

In the Cancer Data Science team we pride ourselves on the quality and rigor of our science, but just as much on our work culture. We believe in a team with a healthy work-life balance and a high degree of psychological safety. We look for candidates with diverse backgrounds, strengths, and perspectives, who are willing to challenge and be challenged. We are also a highly collaborative team, working closely with cancer biologists, experimentalists, project managers, clinicians, and others.

Portal Scientific Lead

Apply here

We are seeking an enthusiastic and talented scientist to lead our efforts to create an exciting next phase of the DepMap Portal. In this role, you will work closely with the DepMap scientific leadership to define the strategic vision for the DepMap portal and help shape its evolution for the benefit of the global scientific community.

Software Engineer

Apply here

We are seeking an exceptional software developer to join the Cancer Data Science group to build tools and portals. In the Cancer Data Science group, we leverage data science approaches to understand cancer biology and support translational efforts.

Associate Computational Biologist

Apply here

We are seeking motivated and talented computational scientists to help analyze and understand a range of new cutting-edge cancer datasets, including multi-modal genomics, high-throughput drug screening, single-cell profiling and functional genomics. Working closely with a diverse range of collaborators, you will use statistical and machine learning tools to identify and understand new cancer vulnerabilities, and help create the datasets and tools powering therapeutic discoveries across the cancer research community.

Computational Genomics Lead

Apply here

We are seeking an enthusiastic and experienced computational scientist to lead genomics analysis efforts in the DepMap project. You will lead a small team of computational scientists focused on generating world-leading genomic characterization of pre-clinical cancer models, and developing tools and methods for integrating these data with clinical cancer genomics.


Selected publications

A first-generation pediatric cancer dependency map

Neekesh V. Dharia et al.
Nature Genetics
March 2021

Integrated cross-study datasets of genetic dependencies in cancer

Clare Pacini et al.
Nature Communications
March 2021

Global computational alignment of tumor and cell line transcriptional profiles

Allison Warren et al.
Nature Communications
January 2021

Defining a Cancer Dependency Map

Aviad Tsherniak et al.
July 2017

Accompanying website:

Other publications

Contact Us

James McFarland

Cancer Data Science
Broad Institute of MIT and Harvard
415 Main Street
Cambridge, MA 02142

Email: jmmcfarl at