Nature + Computing

Established: July 15, 2015

Nature + Computing

The intersection of computer science and the natural sciences

Computer science research and applications have grown increasingly data driven. At the same time, the natural sciences have experienced an explosion of data, and are being increasingly driven by computation. While historically the tools created in these two disciplines have been created in relative isolation of each other, increasingly there is bidirectional flow, with insights from machine learning, system modeling, visualization, and software engineering rapidly advancing the pace of scientific discovery, and the statistical rigor of the empirical sciences, driven by a desire to fundamentally understand how the world works, pushing the computational sciences.

The Nature + Computing team at MSR-Redmond is a diverse collection of researchers drawn from across the organization who develop and apply the tools of data science to scientific data and whose research interests are focused on the study of nature.

Precision Medicine

The broad aim of precision medicine is to understand the causal mechanisms underlying variation in clinical outcomes and to improve the efficiency of all aspects of medicine—from scientific discovery to bedside application and disease prevention. Our group is working on projects ranging from vaccine design, to cancer therapeutics, knowledge extraction and basic science using the tools of computational biology.

Heath & Wellbeing

Our group focuses on improving mental health, developing personalized systems to recommend behavioral changes that affect a wide variety of outcomes, and developing technologies that transform the lives of individuals living with physical impairments.


We are committed to using the tools of computer science and machine learning to tackle some of the hardest challenges in environmental sustainability, including conserving biodiversity, ensuring robust food systems, and mitigating climate change.

Engineering with Biology

The recent advances in biotechnology and the increasing challenges in electronics make this a good time to study how these two areas may interact. We are investigating how to apply biology to address current engineering challenges and how to apply engineering principles to the design and optimization of biological systems.



Selection bias at the heterosexual HIV-1 transmission bottleneck

Jonathan Carlson, Malinda Schaefer, Daniela C. Monaco, Rebecca Batorsky, Daniel T. Claiborne, Jessica Prince, Martin J. Deymier, Zachary S. Ende, Nichole R. Klatt, Charles E. DeZiel, Tien-Ho Lin, Jian Peng, Aaron M. Seese, Roger Shapiro, John Frater, Thumbi Ndung'u, Jianming Tang, Paul Goepfert, Jill Gilmour, Matt A. Price, William Kilembe, David Heckerman, Philip J. R. Goulder, Todd M. Allen, Susan Allen, Eric Hunter

July 2014


A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control

Istvan Bartha, Jonathan Carlson, Chanson J Brumme, Paul J McLaren, Zabrina L Brumme, Mina John, David W Haas, Javier Martinez-Picado, Judith Dalmau, Cecilio López-Galíndez, Concepción Casado, Andri Rauch, Huldrych F Günthard, Enos Bernasconi, Pietro Vernazza, Thomas Klimkait, Sabine Yerly, Stephen J O’Brien, Jennifer Listgarten, Nico Pfeifer, Christoph Lippert, Nicolo Fusi, Zoltán Kutalik, Todd M Allen, Viktor Müller, P Richard Harrigan, David Heckerman, Amalio Telenti, Jacques Fellay

October 2013

HLA Class I-Driven Evolution of Human Immunodeficiency Virus Type 1 Subtype C Proteome: immune Escape and Viral Load

Christine M. Rousseau, Marcus G. Daniels, Jonathan Carlson, Carl Kadie, Hayley Crawford, Andrew Prendergast, Philippa Matthews, Rebecca Payne, Morgane Rolland, Dana N. Raugi, Brandon S. Maust, Gerald H. Learn, David C. Nickle, Hoosen Coovadia, Thumbi Ndung'u, Nicole Frahm, Christian Brander, Bruce D. Walker, Philip J. R. Goulder, Tanmoy Bhattacharya, David Heckerman, Bette T. Korber, James I. Mullins

January 2008

Naturally occurring dominant resistance mutations to hepatitis C virus protease and polymerase inhibitors in treatment-naïve patients

Thomas Kuntzen, Joerg Timm, Andrew Berical, Niall Lennon, Aaron M. Berlin, Sarah K. Young, Bongshin Lee, David Heckerman, Jonathan Carlson, Laura L. Reyor, Marianna Kleyman, Cory M. McMahon, Christopher Birch, Julian Schulze zur Wiesch, Timothy Ledlie, Michael Koehrsen, Chinnappa Kodira, Andrew D. Roberts, Georg M. Lauer, Hugo R. Rosen, Florian Bihl, Andreas Cerny, Ulrich Spengler, Zhimin Liu, Arthur Y. Kim, Yanming Xing, Arne Schneidewind, Margaret A. Madey, Jaquelyn F. Fleckenstein, Vicki M. Park, James E. Galagan, Chad Nusbaum, Bruce D. Walker, Gerond V. Lake-Bakaar, Eric S. Daar, Ira M. Jacobson, Edward D. Gomperts, Brian R. Edlin, Sharyne M. Donfield, Raymond T. Chung, Andrew H. Talal, Tony Marion, Bruce W. Birren, Matthew R. Henn, Todd M. Allen

January 2008

Marked Epitope- and Allele-Specific Differences in Rates of Mutation in Human Immunodeficiency Type 1 (HIV-1) Gag, Pol, and Nef Cytotoxic T-Lymphocyte Epitopes in Acute/Early HIV-1 Infection

Zabrina L. Brumme, Chanson J. Brumme, Jonathan Carlson, Hendrik Streeck, Mina John, Quentin Eichbaum, Brian L. Block, Brett Baker, Carl Kadie, Martin Markowitz, Heiko Jessen, Anthony D. Kelleher, Eric Rosenberg, John Kaldor, Yuko Yuki, Mary Carrington, Todd M. Allen, Simon Mallal, Marcus Altfeld, David Heckerman, Bruce D. Walker

January 2008



Established: January 1, 2011

FaST-LMM (Factored Spectrally Transformed Linear Mixed Models) is a set of tools for performing efficient genome-wide association studies (GWAS) on large data sets. FaST-LMM runs on both Windows and Linux, and has been tested on data sets with over one…