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.

Contact: *lastname*@microsoft.com

** New arXiv preprint **: Probabilistic matrix factorization for automated machine learning

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

** 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

** Machine learning in computational biology workshop at NIPS 2016 and 2015**. see programs here (videos for 2015 are up)

(legend: * = equal contributions, __corresponding__)

**Probabilistic Matrix Factorization for Automated Machine Learning**

Nicolo Fusi, Huseyn Melih Elibol

*arXiv*, 2017

**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.

(legend: * = equal contributions, __corresponding__)

Probabilistic Matrix Factorization for Automated Machine Learning

Nicolo Fusi, Huseyn Melih Elibol

*arXiv*, 2017

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.

Probabilistic Latent Variable Models in Statistical Genomics

Advisor: Prof. Neil D. Lawrence.

*University of Sheffield, Sheffield, UK*, 2014.