PeopleofSuperintelligence

Researcher

Ziv Goldfeld

Ziv Goldfeld is a Cornell electrical and computer engineering professor whose research develops mathematical foundations for machine learning and AI. He studied electrical and computer engineering at Ben-Gurion, worked at MIT's LIDS, and joined Cornell in 2019. His papers analyze information flow in deep neural networks, examine the information bottleneck, and develop optimal-transport algorithms for aligning data. As of October 2026, he is an associate professor at Cornell and mentors work on causal abstractions inside neural networks.

Works
109
Citations
1,010
h-index
17

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

  1. 2012–2017

    Ben-Gurion degrees and award

    He earns his B.Sc. in electrical and computer engineering at Ben-Gurion University of the Negev in 2012 and his M.Sc. there in 2014, both summa cum laude, then completes a Ph.D. in the same department in 2017 under Haim Permuter. In 2014, he wins the IEEE 28th Convention of Electrical and Electronics Engineers in Israel best student paper award for “Semi-Deterministic Broadcast Channels with Cooperation.”

  2. 2017–2019

    Postdoc at MIT LIDS

    During the 2017–2019 academic years, he works as a postdoctoral research fellow in MIT's Laboratory for Information and Decision Systems (LIDS), within the Department of Electrical Engineering and Computer Science.

  3. July 2019

    Joining Cornell ECE

    In July 2019, he joins Cornell's School of Electrical and Computer Engineering as an assistant professor.

  4. 2019

    Information flow in neural networks

    In 2019, he coauthors the ICML paper “Estimating Information Flow in Deep Neural Networks.” It develops a rigorous framework to estimate mutual information between network inputs and hidden-layer representations.

  5. 2020

    The information bottleneck

    With Yury Polyanskiy, he publishes a 2020 tutorial, “The Information Bottleneck Problem and Its Applications in Machine Learning.” The paper reviews the framework's information-theoretic origins, its implications for deep-learning theory, and practical algorithms inspired by it.

  6. 2021

    An NSF CAREER Award

    In 2021, he receives the National Science Foundation Faculty Early Career Development Award (CAREER).

  7. 2023–2024

    Algorithms for GW alignment

    In 2023, he coauthors “Entropic Gromov-Wasserstein Distances: Stability and Algorithms” with Gabriel Rioux and Kengo Kato. The work derives efficient algorithms with formal convergence guarantees for an entropically regularized Gromov-Wasserstein problem. The Journal of Machine Learning Research publishes the paper in October 2024.

  8. As of October 2026

    Associate professor at Cornell

    As of October 2026, he is an Associate Professor in Cornell's School of Electrical and Computer Engineering. In Spring 2026, he mentors a Math+AI at Cornell project on causal abstractions inside neural networks. His research develops the mathematical and algorithmic foundations of machine learning and AI.

Career

  1. NowCornell UniversityAssociate Professor
  2. From 2019Cornell UniversityAssistant Professor
  3. 2017–2019Massachusetts Institute of TechnologyPostdoctoral research fellow in Laboratory for Information and Decision Systems (LIDS)

Education

  1. 2017Ben-Gurion University of the NegevPh.D. in Electrical and Computer Engineering
  2. 2014Ben-Gurion University of the NegevM.Sc. in Electrical and Computer Engineering
  3. 2012Ben-Gurion University of the NegevB.Sc. in Electrical and Computer Engineering

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

Colleagues

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

Sources and credits

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  1. Ziv Goldfeld - Estimating the Flow of Information in Deep Neural Networks events.cornell.edu. Checked 27 October 2026.
  2. Ziv Goldfeld (Ben-Gurion) bliss.eecs.berkeley.edu. Checked 27 October 2026.
  3. Ziv Goldfeld | Cornell Duffield Engineering duffield.cornell.edu. Checked 27 October 2026.
  4. Estimating Information Flow in Deep Neural Networks mitibm.mit.edu. Checked 27 October 2026.
  5. The Information Bottleneck Problem and Its Applications in Machine Learning arxiv.org. Checked 27 October 2026.
  6. Ziv Goldfeld | Department of Computer Science | Cornell Bowers cs.cornell.edu. Checked 27 October 2026.
  7. Publications | Ziv Goldfeld people.ece.cornell.edu. Checked 27 October 2026.
  8. Entropic Gromov-Wasserstein Distances: Stability and Algorithms arxiv.org. Checked 27 October 2026.
  9. Math+AI at Cornell danielhl.github.io. Checked 27 October 2026.
  10. Ziv Goldfeld people.ece.cornell.edu. Checked 27 October 2026.