Volodymyr Kuleshov

Assistant Professor
Deparment of Computer Science
Cornell Tech

My research focuses on machine learning and its applications in science, health, and sustainability. Some of my projects/interests include:

  • New deep learning and deep generative models for massive scientific datasets that help accelerate research, particularly in the biological sciences and genomics. Nature Medicine 19
  • Machine reading systems that help make scientific knowledge easily accessible to researchers and clinicians Nature Comm. 19 Github
  • New genome sequencing technologies that combine existing wetlab techniques with new statistical methods, thus making them significantly more affordable and accurate Nature Biotech. 14 Nature Biotech. 16

These projects motivate core machine learning research in deep learning, probabilistic methods, approximate inference, decision-making under uncertainty. NeurIPS17 ICML18 ICML19

I am also involved in commercializing my research. I am the co-founder and Chief Technologist at Afresh, a startup that uses AI to automate operations in hundreds of grocery stores across the US, significantly driving down food waste — a major environmental problem. In 2012-2013, I spent a year off as first engineer at Stanford spin-out Moleculo Moleculo, where I developed machine learning algorithms that now power Illumina's genome phasing service.

I obtained my PhD from Stanford, working with Stefano Ermon, Serafim Batzoglou, Michael Snyder, Christopher Re, and Percy Liang, and I was the recipient of the Arthur Samuel Best Thesis Award.


  • CS6785. Advanced Topics in Machine Learning: Deep Probabilistic and Generative Models. (Spring 2021)
  • CS5785. Applied Machine Learning. (Fall 2020)
  • CS6784. Advanced Topics in Machine Learning: Deep Generative Models. (Spring 2020)


Machine learning

Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.
Sawyer Birnbaum*, Volodymyr Kuleshov*, Zayd Enam, Pang Wei Koh, Stefano Ermon.
Neural Information Processing Systems, 2019

Calibrated Model-Based Deep Reinforcement Learning.
Ali Malik*, Volodymyr Kuleshov*, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon.
International Conference on Machine Learning, 2019

Accurate uncertainties for deep learning using calibrated regression.
Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon.
International Conference on Machine Learning, 2018

Adversarial constraint learning for structured prediction.
Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon.
International Joint Conference on Artificial Intelligence, 2018

Learning with weak supervision from physics and data-driven constraints.
Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon.
AI Magazine, 2018

Neural variational inference and learning in undirected graphical models.
Volodymyr Kuleshov and Stefano Ermon.
Neural Information Processing Systems, 2017

Deep hybrid models: bridging discriminative and generative approaches.
Volodymyr Kuleshov and Stefano Ermon.
Uncertainty in Artificial Intelligence, 2017

Audio super-resolution with neural networks.
Volodymyr Kuleshov and Stefano Ermon.
International Conference on Learning Representations (Workshop track), 2017

Estimating uncertainty online against an adversary.
Volodymyr Kuleshov and Stefano Ermon.
Association for the Advancement of Artificial Intelligence, 2017

Calibrated structured prediction.
Volodymyr Kuleshov and Percy Liang.
Neural Information Processing Systems, 2015

Tensor factorization via matrix factorization.
Volodymyr Kuleshov*, Arun Chaganty*, Percy Liang.
Artificial Intelligence and Statistics, 2015

Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration.
Volodymyr Kuleshov.
International Conference on Machine Learning, 2013

Algorithms for multi-armed bandit problems.
Volodymyr Kuleshov and Doina Precup.


A machine-compiled database of genome-wide association studies.
Volodymyr Kuleshov, Jialin Ding, Christopher Vo, Braden Hancock, Alexander Ratner, Yang Li, Christopher Re, Serafim Batzoglou, Michael Snyder
Nature Communications, 2019
Intelligent Systems for Molecular Biology (Bio-Ontologies Track), 2017

A guide to deep learning in healthcare
Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun, Jeff Dean
Nature Medicine, 2019

Lightweight metagenomic species deconvolution using locality-sensitive hashing and Bayesian mixture models.
Victoria Popic, Volodymyr Kuleshov, Serafim Batzoglou, Michael Snyder.
Research in Computational Molecular Biology, 2017

Genome assembly from synthetic long read clouds.
Volodymyr Kuleshov, Serafim Batzoglou, Michael Snyder.
Intelligent Systems for Molecular Biology, 2016

High-resolution structure of the human microbiome revealed with synthetic long reads.
Volodymyr Kuleshov, Chao Jiang, Wenyu Zhou, Fereshteh Jahanbani, Serafim Batzoglou, Michael Snyder.
Nature Biotechnology, 2015 (Advance Online Publication)

Probabilistic single-individual haplotyping.
Volodymyr Kuleshov.
European Conference on Computational Biology, 2014.

Whole-genome haplotyping using long reads and statistical methods.
Volodymyr Kuleshov, Dan Xie, Rui Chen, Dmitry Pushkarev, et al.
Nature Biotechnology, 2014

Algorithmic game theory

Inverse game theory: learning utilities in succinct games.
Volodymyr Kuleshov and Okke Schrijvers.
Web and Internet Economics, 2015
World Congress of the Game Theory Society (Contributed Talk), 2016

On the efficiency of the simplest market mechanisms.
Volodymyr Kuleshov and Gordon Wilfong.
Web and Internet Economics, 2012

On the efficiency of markets with two-sided proportional allocation mechanisms.
Volodymyr Kuleshov and Adrian Vetta.
Algorithmic Game Theory, 2010


Volodymyr Kuleshov
Bloomberg Center, Room 366
2 West Loop Road
New York, NY 10044
E: [last name]@cornell.edu