Mahdi Haghifam
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[Google Scholar] [Github] [Twitter] [Linkedin] Email(preferred): haghifam.mahdi@gmail.com Email: mhaghifam@ttic.edu |
About Me
I am a research assistant professor at the Toyota Technological Institute at Chicago (TTIC). I was previously a Distinguished Postdoctoral Researcher at Khoury College of Computer Sciences at Northeastern University, fortunate to be working Jonathan Ullman and Adam Smith. I completed my PhD at University of Toronto and Vector Institute where I was fortunate to be advised by Daniel M. Roy. I also received my B.Sc. and M.Sc. degrees in Electrical Engineering from Sharif University of Technology.
Recognitions of my work include a Best Paper Award at ICML 2024, Simons Institute-UC Berkeley Research Fellowship, as well as several honors for graduate research excellence from University of Toronto, including the Henderson and Bassett Research Fellowship and the Viola Carless Smith Research Fellowship. Additionally, I was recognized as a top reviewer at NeurIPS in 2021 and 2023.
Outside my research activities, I enjoy playing and watching soccer, reading classic literature, and baking.
Industry Internship Experience
Google DeepMind| Research Intern
Mountain View, CA | September 2022 – December 2022
Mentors: Thomas Steinke and Abhradeep Guha Thakurta
- Developed novel second-order privacy-preserving training algorithm achieving 10–40× improvement over baselines and Studied the impact of public data on privacy-preserving training algorithms
- Resulted in publications at NeurIPS 2023 (link) (code)
and ICML 2023 (link)
ServiceNow Research | Research Intern
Toronto, ON | November 2020 – March 2021
Mentor: Gintare Karolina Dziugaite
- Studied the connections between different generalization approaches in ML
- Resulted in publication in NeurIPS 2021 (Spotlight) (link)
ServiceNow Research | Research Intern
Toronto, ON | February 2019 – May 2019
Mentor: Gintare Karolina Dziugaite
- Proposed a new analytical technique that measures algorithmic stability on random subsets of data, creating a tighter and more empirically accurate connection between the training process and generalization performance, NeurIPS 2019 (link)
- Built generalization-prediction tools in TensorFlow for CNNs and MLP on image classification tasks. Our method achieves for the first time non-vacuous generalization bound (code)
Research Overview and Selected Papers
My research focuses on the foundations and principled algorithm design for ML. More broadly, I am interested in statistical learning theory, statistics, and information theory. The central goal of my research is to address practical challenges in ML by developing tools and algorithms with rigorous theoretical guarantees that assess and ensure validity. This work is crucial for building trustworthy ML systems in high-stakes applications, where balancing responsible deployment with strong empirical performance is essential. Some of the questions I have been thinking about: When and how can models generalize beyond their training data? Under what conditions do they memorize sensitive information? And how can we preserve privacy while still learning effectively from sensitive data?
Generalization in Machine Learning:
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates
J. Negrea*, M. Haghifam* , G. K. Dziugaite, A. Khisti, D. M. Roy
Advances in Neural Information Processing Systems (NeurIPS), 2019Towards a Unified Information–Theoretic Framework for Generalization | [Talk video]
M. Haghifam , G. K. Dziugaite, S. Moran, D. M. Roy.
Advances in Neural Information Processing Systems (NeurIPS), 2021 (Spotlight, <3% of submissions)
Memorization and Privacy Attacks:
Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization | [Talk video]
I. Attias, G. K. Dziugaite, M. Haghifam , R. Livni, D. M. Roy (alphabetic ordering)
International Conference on Machine Learning (ICML), 2024 (Oral, Best Paper Award: Top 10 of 10,000 submissions)The Sample Complexity of Membership Inference and Privacy Auditing
M. Haghifam , A. Smith, J. Ullman
Pre-print, 2025On Traceability in
Stochastic Convex Optimization
S. Voitovych*, M. Haghifam* , I. Attias, G. K. Dziugaite, R. Livni, D. M. Roy
Advances in Neural Information Processing Systems (NeurIPS), 2025 (Spotlight, <3% of submissions)
Differential Privacy:
Private Geometric Median
M. Haghifam , T. Steinke, J. Ullman | [Talk video]
Advances in Neural Information Processing Systems (NeurIPS), 2024Faster Differentially Private Convex Optimization via Second-Order Methods
A. Ganesh, M. Haghifam , T. Steinke, A. Thakurta (alphabetic ordering)
Advances in Neural Information Processing Systems (NeurIPS) 2023
Contact Me!
Feel free to reach out if you'd like to discuss research ideas. Also, I'm happy to offer guidance and support to those applying to graduate programs, especially individuals who might not typically have access to such assistance