PeopleofSuperintelligence

Researcher

Volodymyr Kuleshov

Also known as V. Kuleshov, Володимир Кулешов

Volodymyr Kuleshov is a machine learning researcher whose work spans probabilistic methods, diffusion language models and AI for science. At Stanford, he was lead author of a 2015 study that used computational tools and long-read DNA sequencing to analyze bacterial diversity in the human gut; Cornell Tech announced his assistant professorship by July 2018. He coauthored a calibrated regression method and a 2023 text-embedding privacy study that won EMNLP’s Outstanding Paper Award. His later papers explored Block Diffusion and PlantCaduceus, while an October 2026 study he coauthored narrowed a reported pretraining compute gap for diffusion language models. As of October 2026, he is the Joan Eliasoph, M.D. assistant professor of computer science at Cornell Tech and Cornell University, where he studies generative and probabilistic methods for language and biological sequences; he has also co-founded Inception AI and Afresh.

Now at
Cornell University, Inception AI and Afresh
Badges

Timeline

  1. 2015

    Long reads in microbiome research

    In 2015, he is lead author of a Stanford study that uses computational tools and long-read DNA sequencing to analyze bacterial diversity in the human gut microbiome.

  2. July 2018

    Assistant Professor at Cornell Tech

    By July 2018, Cornell Tech announces him as an assistant professor at the Jacobs Technion-Cornell Institute. His education includes a B.S. in mathematics and computer science from McGill and a Ph.D. in computer science from Stanford; earlier roles at Illumina, Moleculo, and Bell Laboratories focus on genome sequencing and algorithmic game theory.

  3. 2018

    Calibrated regression uncertainty

    In 2018, he coauthors Accurate Uncertainties for Deep Learning Using Calibrated Regression, which describes a procedure for calibrating any regression algorithm and producing calibrated uncertainty estimates for Bayesian and probabilistic models.

  4. 2023

    Text embeddings and privacy

    In 2023, he coauthors Text Embeddings Reveal (Almost) As Much As Text, a study of privacy risks in text embeddings. Its Vec2Text method recovers 92% of 32-token text inputs exactly, and the team receives EMNLP’s Outstanding Paper Award in December 2023.

  5. 2025

    Block diffusion and PlantCaduceus

    In 2025, he coauthors Block Diffusion, which interpolates between autoregressive and diffusion language models. He also coauthors PlantCaduceus, a plant DNA language model trained on 16 angiosperm genomes.

  6. October 2026

    Diffusion training compute gap

    In October 2026, he coauthors a study that revisits the reported 16× pretraining compute gap for diffusion language models. Its protocol reports a 7.8× compute multiple with block diffusion training and an encoder-decoder architecture.

  7. As of October 2026

    Cornell Tech and Inception AI

    As of October 2026, he is the Joan Eliasoph, M.D. Assistant Professor of Computer Science at Cornell Tech and Cornell University. His research focuses on generative and probabilistic methods for language and biological sequences; he is also a co-founder of Inception AI and Afresh.

Career

  1. NowCornell UniversityJoan Eliasoph, MD Assistant Professor of Computer Science
  2. NowInception AICo-founder, Inception AI
  3. NowAfreshCo-founder, Afresh
  4. From 2018Cornell UniversityAssistant Professor

Education

  1. McGill UniversityBS in Mathematics and Computer Science
  2. Stanford UniversityPhD in Computer Science

From the biography.

Checked career sources: tech.cornell.edu, tech.cornell.edu, cs.cornell.edu.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. Stunning diversity of gut bacteria uncovered by new approach to gene sequencing devised at Stanford | Stanford University School of Engineering engineering.stanford.edu. Checked 27 October 2026.
  2. 2 Distinguished Professors Join Cornell Tech Faculty tech.cornell.edu. Checked 27 October 2026.
  3. Accurate Uncertainties for Deep Learning Using Calibrated Regression proceedings.mlr.press. Checked 27 October 2026.
  4. Cornell Tech - Researchers Win Award for Study on Text Embedding Privacy Risks tech.cornell.edu. Checked 27 October 2026.
  5. Text Embeddings Reveal (Almost) As Much As Text - ACL Anthology aclanthology.org. Checked 27 October 2026.
  6. [2503.09573] Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models arxiv.org. Checked 27 October 2026.
  7. PlantCaduceus project page plantcad.github.io. Checked 27 October 2026.
  8. [2610.17590] Revisiting the 16x Pretraining Compute Gap between Diffusion and Autoregressive Language Models arxiv.org. Checked 27 October 2026.
  9. Cornell Tech - Volodymyr Kuleshov tech.cornell.edu. Checked 27 October 2026.
  10. Volodymyr Kuleshov | Department of Computer Science | Cornell Bowers cs.cornell.edu. Checked 27 October 2026.