I’m a research scientist at Microsoft Research in Cambridge, MA. I work at the intersection of machine learning, computational biology and medicine. In particular, I focus on the development of new statistical and computational methods to analyze biological and medical data. In machine learning, I have worked on scalable inference methods for Bayesian nonparametric models and more recently on Bayesian optimization.


News

Machine learning in computational biology workshop at NIPS 2016. Dec 10th in Barcelona. The program is now online

New biorXiv preprint: Machine learning for predicting CRISPR off-target effects

Internships available (apply by Jan 2017). If you are a PhD student with a strong background in machine learning/applied math and interested in an internship working on machine learning and/or computational biology please contact me.

Post-doc positions in computational biology. A computational biology postdoc position starting July 1st, 2017 is currently open. Although interviews may not be scheduled until January 2017, please apply as soon as you are ready to do so as we may fill the position on a rolling basis. For instructions on how to apply, see here, but ignore the deadline written there.

CRISPR predictive modelling paper is now out in Nature Biotechnology. The paper is available here. Press: Microsoft Research blog post Broad Institute blog post

Computational biology seminar series @ MSR. From time to time we host computational biology talks at MSR New England. To subscribe to the talk announcement list, click here

Selected publications

(legend: * = equal contributions, corresponding)

Predicting off-target effects for end-to-end CRISPR guide design
J. Listgarten*, M. Weinstein*, M. Elibol, L. Hoang, J. Doench, N. Fusi*
bioarXiv, 2016

Optimized sgRNA design to maximize activity and minimize off-target effects for genetic screens with CRISPR-Cas9.
J. G. Doench*, N. Fusi*, M. Sullender*, M. Hegde*, E. W. Vaimberg, K. F.* Donovan, I. Smith, Z. Tothova, C. Wilen , R. Orchard , H. W. Virgin, J. Listgarten*, D. E. Root.
Nature Biotechnology, 2016

Warped Linear Mixed Models for the Genetic Analysis of Transformed Phenotypes.
N. Fusi, C. Lippert, N. D. Lawrence and O. Stegle.
Nature Communications, 2014.
Platform presentation at the 2014 American Society of Human Genetics meeting.

Gaussian Processes for Big Data.
J. Hensman, N. Fusi and N. Lawrence.
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, 2013.

Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies.
N. Fusi*, O. Stegle*, and N. Lawrence.
PLoS Computational Biology, 2012.

All publications

(legend: * = equal contributions, corresponding)

Predicting off-target effects for end-to-end CRISPR guide design
J. Listgarten*, M. Weinstein*, M. Elibol, L. Hoang, J. Doench, N. Fusi*
bioarXiv, 2016

Impact of pre-adapted HIV-1 transmission
J. M. Carlson, V. Y. Du, N. Pfeifer, A. Bansal, V. Y.F. Tan, K. Power, C. J. Brumme, A. Kreimer, C. E. DeZiel, N. Fusi, M. Schaefer, M. A. Brockman, J. Gilmour, M. A. Price, W. Kilembe, R. Haubrich, M. John, S. Mallal, R. Shapiro, J. Frater, P. R. Harrigan, T. Ndung’u, S. Allen, D. Heckerman, J. Sidney, T. M. Allen, P. J.R. Goulder, Z. L. Brumme, E. Hunter, P. A. Goepfert
Nature Medicine, 2016.

Leveraging Non-Linear Genetic Effects on Function-Valued Traits for GWAS
N. Fusi* and J. Listgarten*
RECOMB, 2016.

Optimized sgRNA design to maximize activity and minimize off-target effects for genetic screens with CRISPR-Cas9
J. G. Doench*, N. Fusi*, M. Sullender*, M. Hegde*, E. W. Vaimberg, K. F.* Donovan, I. Smith, Z. Tothova, C. Wilen , R. Orchard , H. W. Virgin, J. Listgarten*, D. E. Root.
Nature Biotechnology, 2016.
Press: Microsoft Research blog post Broad Institute blog post
Platform presentation at the 2015 American Society of Human Genetics meeting.

In Silico Predictive Modelling of CRISPR/Cas9 Guide Efficiency
N. Fusi*, Ian Smith, John Doench, J. Listgarten*
bioRxiv, 2015.
Note: This pre-print has been largely (though not entirely) absorbed into the Nature Biotechnology paper above.

Further Improvements to Linear Mixed Models for Genome-Wide Association Studies
C. Widmer, C. Lippert, O. Weissbrod, N. Fusi, C. Kadie, R. Davidson, J. Listgarten, and D. Heckerman
Scientific Reports, 2014.

Warped Linear Mixed Models for the Genetic Analysis of Transformed Phenotypes. N. Fusi, C. Lippert, N. D. Lawrence and O. Stegle.
Nature Communications, 2014.
EBI press release
Platform presentation at the 2014 American Society of Human Genetics meeting.

A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control.
István 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.
eLife, 2013.

Whole genome transcriptome analysis identifies indices of fast and slow disease progression in two mouse models of amyotrophic lateral sclerosis.
G. Nardo, R. Iennaco, N. Fusi, N. D Lawrence, M. Marino, P. Heath, L. Ferraiuolo, P. J Shaw, and C. Bendotti.
Brain, 2013.

Gaussian Processes for Big Data.
J. Hensman, N. Fusi and N. Lawrence.
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, 2013.

Detecting regulatory gene-environment interactions with unmeasured environmental factors.
N. Fusi, C. Lippert, K. Borgwardt, N. Lawrence and O. Stegle.
Bioinformatics, 2013.

Unravelling the enigma of selective vulnerability in neurodegeneration: motor neurons resistant to degeneration in ALS show distinct gene expression characteristics and decreased susceptibility to excitotoxicity.
A. Brockington, K. Ning, P.R. Heath, E. Wood, J. Kirby, N. Fusi, N. Lawrence, S.B. Wharton, P.G. Ince, and P.J. Shaw.
Acta Neuropathologica, 2012.

Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies.
N. Fusi*, O. Stegle*, and N. D. Lawrence.
PLoS Computational Biology, 2012.

Finding topics in diseases through the analysis of RNA-seq data.
N. Fusi and N. Lawrence.
In 22nd Annual Workshop on Mathematical and Statistical Aspects of Molecular Biology, 2012.

Explaining Confounding Factors in eQTL Studies using a Dictionary of Latent Variables.
N. Fusi, O. Stegle, and N. Lawrence.
In NIPS workshop on Machine Learning in Computational Biology, 2010.

Intrusion Detection via Artificial Immune System: a Performance-based Approach.
A. Visconti, N. Fusi, and H. Tahayori.
Biologically-Inspired Collaborative Computing: IFIP 20th World Computer Congress, 2008.

PhD thesis

Probabilistic Latent Variable Models in Statistical Genomics
Advisor: Prof. Neil D. Lawrence.
University of Sheffield, Sheffield, UK, 2014.