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Ziv Goldfeld

Associate Professor

School of Electrical and Computer Engineering

Ziv Goldfeld
Ziv Goldfeld
Graduate Field Affiliations
Applied Mathematics
Computer Science
Data Science (minor)
Electrical and Computer Engineering
Operations Research and Information Engineering
Statistics

Biography

Ziv Goldfeld joined the School of Electrical and Computer Engineering at Cornell University as an Assistant Professor in July 2019. He is a graduate field member of Computer Science, Statistics and Data Science, Operations Research and Information Engineering, and the Center of Applied Mathematics at Cornell University. He is also a member of the Foundations of Information, Networks, and Decision Systems (FIND) group. During the 2017-2019 academic years, he was a postdoctoral research fellow in LIDS of the Electrical Engineering and Computer Science Department at MIT.  Before that, Goldfeld received his B.Sc., M.Sc. (both summa cum laude) and Ph.D. in the Department of Electrical and Computer Engineering at Ben Gurion University of the Negev. Honors include the NSF CAREER Award, the IBM University Award, the Michael Tien ’72 Excellence in Teaching Award, and the Rothschild postdoctoral fellowship.

Research Interests

Goldfeld’s research develops the mathematical and algorithmic foundations of machine learning and AI, targeting principled approaches with guarantees on reliability, scalability, robustness, and steerability. He employs a unifying geometric framework that models diverse learning problems as sequences of operations on probability distributions over high-dimensional manifolds. This abstraction lends itself well to rigorous analysis, integrating ideas from optimal transport theory, information theory, mathematical statistics, optimization, and applied probability. This interdisciplinary nature often reveals new connections that foster cross-field fertilization.

Teaching Interests

  • ECE 3200/5200 Foundations of Machine Learning
  • ECE 4110 Random Signals in Communications and Signal Processing
  • ECE 6630 Information Theory for Data Transmission, Security and Machine Learning
  • ECE 6970 Optimal transport theory and statistical divergences: from foundations to modern applications

Select Publications

  • S. Nietert, R. Cummings, and Z. Goldfeld,
    “Robust estimation under the Wasserstein distance”.
    Journal of Machine Learning Research, December 2025.

  • H. He, C. L. Yu, and Z. Goldfeld,
    “Information-theoretic generalization bounds for deep neural networks”.
    IEEE Transactions on Information Theory, August 2025.

  • Z. Zhang, Z. Goldfeld, K. Greenewald, Y. Mroueh, B. K. Sriperumbudur,
    “Gradient flows and Riemannian structure in the Gromov-Wasserstein geometry”.
    Foundations of Computational Mathematics, July 2025.

  • R. Sadhu, Z. Goldfeld, and K. Kato,
    “Stability and statistical inference for semidiscrete optimal transport maps”.
    Annals of Applied Probability, December 2024.

  • Z. Zhang, Z. Goldfeld, Y. Mroueh, and B. K. Sriperumbudur,
    “Gromov-Wasserstein distances: entropic regularization, duality, and sample complexity”.
    Annals of Statistics, May 2024.

Ziv Goldfeld's Google Scholar

Select Awards and Honors

  • Michael Tien ’72 Excellence in Teaching Award 2023
  • NSF CAREER Award, National Science Foundation 2021
  • NSF CRII Award, National Science Foundation 2020
  • IBM University Award 2020
  • The Rothschild Postdoctoral Fellowship 2017

Education

  • B.S., Electrical and Computer Engineering, Ben Gurion University of the Negev 2012
  • M.S., Electrical and Computer Engineering, Ben Gurion University of the Negev 2012
  • Ph.D., M.Sc. Electrical and Computer Engineering, Ben Gurion University of the Negev 2018
  • Postdoctoral fellow, Laboratory for Information and Decision Systems (LIDS), Electrical Engineering and Computer Science, MIT 2019