Mahdi Haghifam

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Ph.D. candidate,
University of Toronto‬, Vector Institute‬
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Email: mahdi dot haghifam AT

The beginning of knowledge is the discovery of something we do not understand.
— Frank Herbert

About Me

I am a final year PhD candidate at University of Toronto and a graduate student researcher at Vector Institute‬. I am honored to be advised by Prof. ‪Daniel M. Roy‬. I also work closely with Dr. Gintare Karolina Dziugaite‬. I received my B.Sc. and M.Sc. degrees both in Electrical Engineering from Sharif University of Technology.
Previously, I was a research intern at Google Brain where I was extremely lucky to be mentored by Dr. Thomas Steinke and Dr. Abhradeep Guha Thakurta‬ during Summer and Fall 2022‪. I was also a research intern at Element AI‬ in Winter 2019 and Fall 2020. In early 2020, I was a visiting student at Institute of Advanced Study‬ (IAS) for special-year program on Optimization, Statistics, and Theoretical Machine Learning.

Research Interests

My research focuses broadly on statistical learning theory and Differential Privacy. I have been working on several areas of Generalization Theory in Machine Learning with a focus on deriving provable guarantees for Machine Learning methods using information-theoretic tools. I am also interested in statistical inference and learning under privacy constraints. For a complete list of my publications, please visit the Publications page.

I like collaborations; feel free to reach out if you find common interests.

Selected Papers

  • M. Haghifam*, B. Rodriguez-Galvez*, R. Thobaben, M. Skoglund, D. M. Roy, G. K. Dziugaite ‘‘Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization’’, ALT 2023 [paper].

  • M. Haghifam, G. K. Dziugaite, S. Moran, D. M. Roy, ‘‘Towards a Unified Information–Theoretic Framework for Generalization’’, NeurIPS 2021 (Spotlight, <3% of submissions) [paper].

  • M. Haghifam, J. Negrea, A. Khisti, D. M. Roy , G. K. Dziugaite, ‘‘Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms’’, NeurIPS 2020 [paper].