My research interests lie at the intersection of computational biology and machine learning. In particular, my focus is on the development of new machine learning techniques and their application to problems in molecular biology and personalized medicine.


Microsoft Research New England Compbio/ML Research Assistant

Established: October 11, 2016

POSITION: Computational Biology and Machine Learning Research Assistant (RA)   JOB DESCRIPTION: The position is ideal for a recent undergraduate wishing to gain research experience prior to pursuing a PhD. The successful candidate will have the following skill set: proficient in python programming---in particular scientific computing using numpy/scipy/sckit-learn/pandas; familiarity with Git version control, and both Windows and Unix/Linux systems; basic expertise in machine learning (such as would be acquired through an undergraduate course); comfort with…

MSR NE: Post-Doctoral position in Computational Biology

Established: September 27, 2016

A computational biology postdoc position starting July 1st, 2017 is currently open at Microsoft Research New England. Please get applications in by December 1st 2016, although we may consider later applications. Interviews will start in early 2017 and the position will be filled on a rolling basis. Most of our post-docs stay for 2 years but because many already have faculty positions lined up which they defer for a year, they also often stay only one…


Established: September 14, 2016

End-to-end guide design for CRISPR/Cas9 with machine learning Elevation To enable more effective guide design we have developed the first machine learning-based approach to assess CRISPR/Cas9 off-target effects. Our approach consistently and substantially outperformed the state-of the-art over multiple, independent data sets, yielding up to a 6-fold improvement in accuracy. Because of the large computational demands of the task, we also developed a cloud-based service for end-to-end guide design which incorporates our previously reported on-target…

Azimuth: Machine Learning-Based Predictive Modelling of CRISPR/Cas9 guide efficiency

Established: July 1, 2015

Project Summary The CRISPR/Cas9 system provides state-of-the art genome editing capabilities. However, several facets of this system are under investigation for further characterization and optimization. One in particular is the choice of guide RNA that directs Cas9 to target DNA: given that one would like to target the protein-coding region of a gene, hundreds of guides satisfy the constraints of the CRISPR/Cas9 Protospacer Adjacent Motif sequence. However, only some of these guides efficiently target DNA…






A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control
Istvan Bartha, Jonathan M. 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, in eLife, October 29, 2013, View abstract, Download PDF