Volodymyr Kuleshov
Artificial Intelligence Lab
Deparment of Computer Science
Stanford University
Update: I am now at Cornell Tech.
My research focuses on machine learning and its applications in genomics and personalized medicine. Some of my projects/interests include:
- Machine reading systems for scientific literature that help make biomedical knowledge easily accessible to scientists and clinicians ISMB17 Github
- New genome sequencing technologies that combine existing wetlab techniques with new statistical methods, thus making them significantly more affordable and accurate Nat. Biotech. 14 Nat. Biotech. 15
I also work on core machine learning problems such as:
- Uncertainty estimation techniques, particularly in the context of structured prediction and adversarial online learning NIPS15 AAAI17
- Fast approximate inference in probabilistic models, including recent methods based on deep learning ICLR16
I recently completed a post-doc with Stefano Ermon. Previously, I obtained my PhD from Stanford, working with Serafim Batzoglou, Michael Snyder, Christopher Re, and Percy Liang.
In 2012-2013, I spent a year off at Moleculo, where I developed algorithms that now power Illumina's genome phasing service.
Papers
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.
Manuscript
Genomics
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