Saeed Sharifi-Malvajerdi

About Me

I am a Research Assistant Professor at the Toyota Technological Institute at Chicago (TTIC), a philanthropically endowed academic computer science institute. Prior to that, I was a PhD student in the Statistics and Data Science department at the Wharton School of the University of Pennsylvania, where I was advised by Michael Kearns and Aaron Roth. I hold Bachelor degrees in Electrical Engineering and Mathematics, both from Sharif University of Technology.

I am co-organizing the Theory of Computing for Fairness (TOC4Fairness) Weekly Seminar Series. For more information about me, see my CV, and here is a link to my Google Scholar profile.

Contact: saeed@ttic.edu

Research Interests

Publications

Talks and Presentations

Advising

Teaching

Research Interests

My primary interest is in Trustworthy Machine Learning and Data Analysis. More precisely, I study

- Robust Learning in the Presence of Strategic Agents
- Algorithmic Fairness in Machine Learning
- Privacy-preserving Data Analysis
- Algorithms for Machine Unlearning (Data Deletion)
- Adaptive Data Analysis

Publications

*authorship is alphabetical, unless specified otherwise*

Minimax Group Fairness in Strategic Classification [arXiv]

Emily Diana, Saeed Sharifi-Malvajerdi, Ali Vakilian.

IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2025.

Bayesian Strategic Classification [arXiv]

Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl, Ali Vakilian, Juba Ziani.

Conference on Neural Information Processing Systems (NeurIPS) 2024.

Sequential Strategic Screening [arXiv]

Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl, Ali Vakilian, Juba Ziani.

International Conference on Machine Learning (ICML) 2023.

Multiaccurate Proxies for Downstream Fairness [arXiv]

Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi.

Conference on Fairness, Accountability, and Transparency (FAccT) 2022.

Adaptive Machine Unlearning [arXiv]

Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites.

Conference on Neural Information Processing Systems (NeurIPS) 2021.

Appeared at the Workshop on Theory and Practice of Differential Privacy (TPDP) 2021.

Media Coverage: Wired Magazine

Lexicographically Fair Learning: Algorithms and Generalization [arXiv]

Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi.

Symposium on the Foundations of Responsible Computation (FORC) 2021.

Descent-to-Delete: Gradient-Based Methods for Machine Unlearning [arXiv]

Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi.

International Conference on Algorithmic Learning Theory (ALT) 2021.

Appeared at the Workshop on Theory and Practice of Differential Privacy (TPDP) 2020.

Algorithms and Learning for Fair Portfolio Design [arXiv]

Emily Diana, Travis Dick, Hadi Elzayn, Michael Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani.

ACM Conference on Economics and Computation (EC) 2021.

Differentially Private Call Auctions and Market Impact [arXiv]

Emily Diana, Hadi Elzayn, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi, Juba Ziani.

ACM Conference on Economics and Computation (EC) 2020.

A New Analysis of Differential Privacy's Generalization Guarantees [arXiv]

Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld.

Innovations in Theoretical Computer Science (ITCS) 2020.

Selected for a Talk.

Invited to ACM Symposium on Theory of Computation (STOC) 2021.

Average Individual Fairness: Algorithms, Generalization and Experiments [arXiv][Github Repo]

Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi.

Conference on Neural Information Processing Systems (NeurIPS) 2019.

Selected for an Oral Presentation (36/6743 submissions).

Differentially Private Fair Learning [arXiv]

Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman.

International Conference on Machine Learning (ICML) 2019.

Selected for a Long Talk.

Malaria Clearance Rate Regression: an R Software Package for a Bayesian Hierarchical Regression Model [paper][R package]

Saeed Sharifi-Malvajerdi, Feiyu Zhu, Colin B. Fogarty, Michael P. Fay, Rick M. Fairhurst, Jennifer A. Flegg, Kasia Stepniewska, Dylan S. Small.

Malaria Journal 2019 18:4.

Analytical Studies of Fragmented-Spectrum Multi-Level OFDM-CDMA Technique in Cognitive Radio Networks [arXiv]

Farhad Akhoundi, Saeed Sharifi-Malvajerdi, Omid Poursaeed, Jawad A. Salehi.

IEEE Ubiquitous Computing, Electronics, Mobile Communication Conference (UEMCON) 2016.

Talks and Presentations

Bayesian Strategic Classification

(Invited) INFORMS Annual Meeting, October 2024.

(Invited) NSF TRIPODS Meeting, May 2024.

Toyota Technological Institute at Chicago, April 2024.

Machine Unlearning

(Invited) Cornell ORIE Young Researchers Workshop, 2021.

Toyota Technological Institute at Chicago, 2022.

Adaptive Machine Unlearning

Workshop on Theory and Practice of Differential Privacy (TPDP), 2021.

Descent-to-Delete: Gradient-Based Methods for Machine Unlearning

International Conference on Algorithmic Learning Theory (ALT), 2021.

Wharton Department of Statistics and Data Science, 2020.

Differentially Private Call Auctions and Market Impact

INFORMS Annual Meeting, 2020.

A New Analysis of Differential Privacy's Generalization Guarantees

Conference on Innovations in Theoretical Computer Science (ITCS), 2020.

Average Individual Fairness: Algorithms, Generalization and Experiments

Wharton Department of Statistics and Data Science, 2019.

Differentially Private Fair Learning

International Conference on Machine Learning (ICML), 2019.

Wharton Department of Statistics and Data Science, 2018.

Sensitivity Analysis for the Runs Test in Matched-pair Observational Studies

Wharton Department of Statistics and Data Science, 2017.

Advising

Roy Long (UChicago CS), TTIC summer intern working on: Fair Information Design in Strategic Classification.

Teaching

Probability (STAT 430), Wharton Department of Statistics and Data Science, University of Pennsylvania.
Stat Computing with R (STAT 405/705), Wharton Department of Statistics and Data Science, University of Pennsylvania.
Introductory Statistics (STAT 111), Wharton Department of Statistics and Data Science, University of Pennsylvania.
Digital Signal Processing, Electrical Engineering Department, Sharif University of Technology.
Computer Architecture and Lab, Electrical Engineering Department, Sharif University of Technology.
Communication Systems, Electrical Engineering Department, Sharif University of Technology.
Electrical Circuits Theory, Electrical Engineering Department, Sharif University of Technology.