Volodymyr Kuleshov

Ph.D. Candidate
Deparment of Computer Science
Stanford University

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 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

At Stanford, I work with Stefano Ermon, 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.


Machine learning

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

Neural variational random field learning.
Volodymyr Kuleshov and Stefano Ermon.
International Conference on Learning Representations (Workshop track), 2016

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

Scaling up simultaneous matrix diagonalization.
Volodymyr Kuleshov*, Arun Chaganty*, Percy Liang.
Optimization for Machine Learning Workshop at NIPS, 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.


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
Clark Center, Room S260
318 Campus Drive
Stanford, CA 94305
E: [last name]@stanford.edu
map generatorhttp://www.stromvergleich-uebersicht.de