I’m a General Manager/Partner Research Manager at Microsoft Research in Cambridge, MA, where I oversee multidisciplinary teams of researchers, engineers, and applied scientists specializing in artificial intelligence, biomedical machine learning, and human-computer interaction. Our primary focus is on developing novel generative AI techniques to drive forward scientific discovery. My own expertise lies at the intersection of artificial intelligence and life sciences. In the past, I have developed new models, methods, and frameworks in automated machine learning (leading to the AutoML offering in Azure) and data-centric AI. My contributions in computational biology include developing predictive models for CRISPR/Cas9 gene editing and new methods for statistical genetics.
Contact: lastname@microsoft.com
Automated machine learning in Azure . Joint model selection and hyperparameter tuning as a service is now available in Azure as part of the Azure ML offering. More details on Microsoft’s AI blog and the Azure blog. Also see CNET’s story.
Microsoft Research podcast. I was recently interviewed for the Microsoft Research podcast. The recording is available here and wherever you get your podcasts.
New CRISPR off-target and end-to-end guide design paper :
The paper is now out in Nature Biomedical Engineering. You can also read about it in this Microsoft blog piece, Gizmodo, and Endgadget. This work complements our earlier on-target work published in Nature Biotechnology.
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
(legend: * = equal contributions, corresponding)
Rapid Model Architecture Adaptation for Meta-Learning
Yiren Zhao, Xitong Gao, Ilia Shumailov, Nicolò Fusi, Robert D. Mullins
NeurIPS, 2022
Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach
Syrine Belakaria, Rishit Sheth, Nicolò Fusi, Janardhan Rao Doppa
arXiv, 2022
Convolutions are competitive with transformers for protein sequence pretraining
Kevin K Yang, Alex X Lu, Nicolò Fusi
Machine Learning for Drug Discovery workshop - ICLR, 2022
On Hard Episodes in Meta-Learning
Samyadeep Basu, Amr Sharaf, Nicolò Fusi, Soheil Feizi
arXiv, 2022
LANA: Latency Aware Network Acceleration
Pavlo Molchanov, Jimmy Hall, Hongxu Yin, Jan Kautz, Nicolò Fusi, Arash Vahdat
ECCV, 2022
Dataset Dynamics via Gradient Flows in Probability Space
David Alvarez-Melis, Nicolò Fusi
ICML, 2021
Initialization and Regularization of Factorized Neural Layers
Mikhail Khodak, Neil A. Tenenholtz, Lester Mackey, Nicolò Fusi
ICLR, 2021
Geometric Dataset Distances via Optimal Transport
David Alvarez-Melis, Nicolò Fusi
NeurIPS, 2020
Weighted Meta-Learning
Diana Cai, Rishit Sheth, Lester Mackey, Nicolò Fusi
arXiv preprint, 2020
Differentiable Feature Selection by Discrete Relaxation
Rishit Sheth, Nicolo Fusi
AISTATS 2020 arXiv preprint
Probabilistic Neural Architecture Search
Francesco Paolo Casale, Jonathan Gordon, Nicolo Fusi
arXiv preprint, 2019
Model Compression with Generative Adversarial Networks
Ruishan Liu, Nicolo Fusi, Lester Mackey
arXiv preprint, 2018
Gaussian Process Prior Variational Autoencoders
Francesco Paolo Casale, Adrian V Dalca, Luca Saglietti, Jennifer Listgarten, Nicolo Fusi
NeurIPS, 2018
Probabilistic Matrix Factorization for Automated Machine Learning
Nicolo Fusi, Rishit Sheth, Huseyn Melih Elibol
NeurIPS, 2018
Prediction of off-target activities for end-to-end CRISPR guide design
J Listgarten, M Weinstein, B Kleinstiver, AA Sousa, JK Joung, J Crawford, K Gao, M Elibol, L Hoang, J Doench, N Fusi (equal contributions and co-corresponding)
Nature Biomedical Engineering, (2018)
Orthologous CRISPR–Cas9 enzymes for combinatorial genetic screens
F. J. Najm, C. Strand, K. F. Donovan, M. Hegde, K. R. Sanson, E. W. Vaimberg, M. E. Sullender, E. Hartenian, Z. Kalani, N. Fusi, J. Listgarten, S. T. Younger, B. E. Bernstein, D. E Root & J. G. Doench
Nature Biotechnology, 2017
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.
