Seminars, colloquiums, and tutorials
Z. Goldfeld,
“Gromov-Wasserstein alignment: statistics, computation, and geometry”.
Deep Learning Theory Seminar, Vector Institute, University of Toronto, Toronto, Ontario, Canada, April 2025.
Z. Goldfeld,
“Gromov-Wasserstein alignment: statistical and computational advancements via duality”. [Video]
Given during October 2023 — May 2024 at:
1) Data Science and Machine Learning Seminar, Florida State University, Tallahassee, Florida, US.
2) CAM Colloquium, Center of Applied Mathematics, Cornell University, Ithaca, New York, US.
3) The Kantorovich Initiative, University of Washington, Seattle, Washington, US.
4) Institute for Machine Learning (IFML) Seminar, UT Austin, Austin, Texas, US.
5) North American School of Information Theory (NASIT-2023), University of Pennsylvania, Philadelphia, Pennsylvania, US.
6) Statistics Colloquium, Statistics Department, Penn State University, State College, Pennsylvania, US.
7) Statistics Seminar, SDS Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, US
Z. Goldfeld,
“Gromov-Wasserstein distances: entropic regularization, duality, and sample complexity”.
Information Systems Laboratory (ISL) Colloquium, Stanford University, Stanford, California, US, February 2023.
Z. Goldfeld,
“Statistical and computational aspects of sliced optimal transport”. [Video]
Machine Learning Lunch Seminar, CS Department, Vanderbilt University, Nashville, Tennessee, US, October 2022.
Z. Goldfeld,
“Sliced mutual information: a scalable measure of statistical dependence”. [Video]
Given during May — June 2022 at:
1) IBM Machine Learning Seminar, Online.
2) Workshop on PDE Methods in Data Science and Machine Learning, Fields Institute, Toronto, Canada.
Z. Goldfeld,
“A scalable statistical theory for smooth Wasserstein distances”.
Given during February 2022 at:
1) Statistics Seminar, SDS Department, Cornell University, Ithaca, New York, US.
2) ECE Theory Seminar, ECE Department, UC Santa Cruz, Santa Cruz, California, US.
Z. Goldfeld,
“Scaling Wasserstein distances to high dimensions via smoothing”. [Video]
Given during February — June 2021 at:
1) Math Machine Learning Seminar, Max Planck Institute and UCLA, Los Angeles, California, US.
2) Applied Math Seminar, Beijing International Center for Mathematical Research, Peking University, Beijing, China.
3) ECE Colloquium, ECE Department, University of Iowa, Iowa City, Iowa, US.
4) CS Theory Seminar, CS Department, Columbia University, New York, New York, US.
5) Information Theory Forum, EE Department, Stanford University, Stanford, California, US.
Z. Goldfeld,
“Smoothing probability distributions for high dimensional learning and inference”.
CS Brown Bag Seminar, CS Department, Cornell University, Ithaca, New York, US, December 2020.
Z. Goldfeld,
“Smooth Wasserstein distance: metric structure and statistical efficiency”.
CS Theory Seminar, CS Department, Cornell University, Ithaca, New York, US, April 2020.
Z. Goldfeld,
“Gaussian-smoothed optimal transport: metric structure and statistical efficiency”. [Video]
CAM Colloquium, Center of Applied Mathematics, Cornell University, Ithaca, New York, US, November 2019.
Z. Goldfeld,
“Estimating the flow of information in deep neural networks”.
Given during December 2018 — March 2019 at:
1) ECE Department, Cornell University, Ithaca, New York, US.
2) ECE Department, University of Wisconsin-Madison, Madison, Wisconsin, US.
3) EE Department, Columbia University, New York, New York, US.
4) EECS Department, Hebrew University of Jerusalem, Jerusalem, Israel.
5) EE Department, The Technion, Haifa, Israel.
6) EE Systems Department, Tel Aviv University, Tel Aviv, Israel.
7) ECE Department, Ben Gurion University, Beer Sheva, Israel.
Z. Goldfeld,
“Information-theoretic security”. [Video]
Guest lecture in EE 25N course - Science of Information, EE Department, Stanford University, Stanford, California, US, November 2018.
Z. Goldfeld,
“Estimating the information flow in deep neural networks”. [Video]
Information Theory Forum, EE Department, Stanford University, Stanford, California, US, November 2018.
Z. Goldfeld,
“Semantic security versus active adversariesand wiretap channels with random states”.
Given during December 2016 — February 2017 at:
1) Information Theory Forum, EE Department, Stanford University, Stanford, California, US.
2) Berkeley Laboratory for Information and System Sciences (BLISS) Seminar, EECS Department, UC Berkeley, Berkeley, California, US.
3) Laboratory for Information and Decision Systems (LIDS) Seminar, EECS Department, MIT, Cambridge, Massachusetts, US.
Z. Goldfeld,
“Semantic security in the presence of active adversaries” (Extended).
Given during March — June 2016 at:
1) ECE Department Seminar, Technion, Haifa, Israel.
2) Information Sciences and Systems (ISS) Seminar, EE Department, Princeton University, Princeton, New Jersey, US.
3) ECE Department Seminar, New Jersey Institute of Technology (NJIT), Newark, New Jersey, US.
Z. Goldfeld,
“Semantic security in the presence of active adversaries”.
Advanced Communications Center Annual Workshop (Feder Award presentation), Tel-Aviv, Israel, February 2016.
Z. Goldfeld,,
“On duality between source coding and channel coding in multiuser settings”.
Institute of Communications Engineering Seminar, Institute of Communications Engineering, Technical
University of Munich (TUM), Germany, September 2013.
Invited conferences and workshops talks
Z. Goldfeld,
“Limit laws for Gromov-Wasserstein alignment and testing for graph symmetries”.
Joint Statistics Meeting (JSM-2025), Nashville, Tennessee, US, August 2025.
Z. Goldfeld,
“Gromov-Wasserstein alignment: statistics, computation, and geometry”. [Video]
IPAM Workshop: Statistical and Numerical Methods for Non-Commutative Optimal Transport, UCLA, Los Angeles, California, US, May 2025.
Z. Goldfeld,
“Gromov-Wasserstein alignment: statistics, computation, and geometry”.
Statistics and Optimal Transport Workshop, Department of Statistics, Columbia University, New York, New York, US, March 2025.
Z. Goldfeld,
“Gromov-Wasserstein alignment”. [Video]
Machine Learning and Compression Workshop at NeurIPS 2024, Vancouver, Canada, December 2024. [Keynote]
Z. Goldfeld,
“A statistical and computational theory for Gromov-Wasserstein alignment”.
IMS Conference on Statistics and Data Science (ICSDS-2024), Nice, France, December 2024.
Z. Goldfeld,
“Robust estimation via partial optimal transport”.
Allerton Conference on Communication, Control, and Computing (Allerton-2024), Monticello, Illinois, US, September 2024.
Z. Goldfeld,
“Gromov-Wasserstein alignment: statistical and computational advancements via duality”.
International Zurich Seminar on Information and Communication (IZS-2024), Zurich, Switzerland, March 2024.
Z. Goldfeld,
“Gromov-Wasserstein alignment: statistical and computational advancements via duality”.
Learning and Information Theory Workshop (LITH-2024), École polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, March 2024.
Z. Goldfeld,
“Gromov-Wasserstein distances:
statistical and computational advancements via duality theory”.
London Symposium on Information Theory (LSIT-2023), London, UK, May 2023.
Z. Goldfeld,
“Gromov-Wasserstein distances: entropic regularization, duality, and sample complexity”.
Information Theory and Applications Workshop (ITA-2023), San Diego, California, US, February 2023.
Z. Goldfeld,
“Sliced mutual information: a scalable measure of statistical dependence”.
Information Theory and Applications Workshop (ITA-2022), San Diego, California, US, May 2022.
Z. Goldfeld,
“Information storage in interacting particle systems”. [Video]
Beyond IID in Information Thoery Conference (BIID-2020), Online, November 2020.
Z. Goldfeld,
“Smooth Wasserstein distance: metric structure and statistical efficiency”. [Video]
Coding, Cooperation, and Security in Communication Networks Workshop (COCO-2020), Online, July 2020.
Z. Goldfeld,
“Smooth Wasserstein distance: metric structure and statistical efficiency”.
International Zurich Seminar on Information and Communication (IZS-2020), Zurich, Switzerland, February 2020.
Z. Goldfeld,
“Smooth Wasserstein distance: metric structure and statistical efficiency”.
Information Theory and Applications Workshop (ITA-2020), San Diego, California, US, February 2020.
Z. Goldfeld,
“Differential entropy estimation under Gaussian convolutions”.
Information Theory and Applications Workshop (ITA-2019), San Diego, California, US, February 2019.
Z. Goldfeld,
“Estimating information flow in deep neural networks”.
Allerton Conference on Communication, Control, and Computing (Allerton-2018), Monticello, Illinois, US, October 2018.
Z. Goldfeld,
“Semantic security versus active adversaries and the Gelfand-Pinsker wiretap channel”.
Information Theory and Applications Workshop (ITA-2017), San Diego, California, US, February 2017.
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