Meisam Razaviyayn

Meisam Razaviyayn

Associate Professor · Andrew & Erna Viterbi Early Career Chair

Industrial & Systems Engineering · Computer Science
Quantitative & Computational Biology · Electrical Engineering

University of Southern California

Associate Director, USC–Meta Center for Research and Education in AI and Learning (REAL@USC)
Research Scientist (part-time), Google Research
Member, USC Machine Learning Center · USC Center for Systems and Control

Meisam Razaviyayn is an Associate Professor (with the Andrew and Erna Viterbi Early Career Chair) of Industrial and Systems Engineering, Computer Science, Quantitative and Computational Biology, and Electrical Engineering at the University of Southern California. He also serves as Associate Director of the USC–Meta Center for Research and Education in AI and Learning (REAL@USC) and is a part-time Research Scientist at Google Research. His research focuses on the design and study of scalable, trustworthy optimization algorithms for modern data science and machine learning applications. Prior to joining USC, he was a postdoctoral research fellow in the Department of Electrical Engineering at Stanford University and a Visiting Scientist at the Simons Institute for the Theory of Computing at UC Berkeley. He received his Ph.D. in Electrical Engineering (minor in Computer Science) from the University of Minnesota, where he also earned his M.Sc. in Mathematics. He is the recipient of the 2022 NSF CAREER Award, the 2022 Northrop Grumman Excellence in Teaching Award, the 2021 AFOSR Young Investigator Award, the 2021 3M Nontenured Faculty Award, the 2020 ICCM Best Paper Award in Mathematics, the 2019 IEEE Data Science Workshop Best Paper Award, and the 2014 IEEE Signal Processing Society Young Author Best Paper Award. He was among the 40 selected attendees of the 2023 German–American Frontiers of Engineering Symposium organized by the National Academy of Engineering, and is a silver medalist of Iran’s National Mathematics Olympiad.

Recent Publications

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Teaching

CSCI 699

Trustworthy Machine Learning from an Optimization Lens

Graduate seminar exploring trustworthiness in machine learning through optimization, covering privacy safeguards (differentially private optimization), robustness enhancement (minimax formulations), and fairness principles. Co-taught with Vatsal Sharan.

Course website →
ISE 633

Large-Scale Optimization for Machine Learning

Advanced graduate course covering optimization methods for modern machine learning, including stochastic methods, min-max optimization, and distributed algorithms.

Course page →
ISE 530

Optimization Methods for Analytics

Graduate-level course on optimization techniques for data analytics, covering linear programming, convex optimization, and applications in data science.

Course page →
ISE 220

Probability Concepts in Engineering

Undergraduate course covering probability theory, random variables, and statistical methods with engineering applications.

Course page →

ODDS Research Group

The Optimization for Data-Driven Science (ODDS) group focuses on designing efficient large-scale algorithms for machine learning.

Current Ph.D. Students

Zeman Li

Zeman Li

Ph.D. in Industrial & Systems Engineering

B.S. Applied Mathematics, Emory University; B.S. Computer Engineering, Georgia Tech

Research interests: machine learning and large-scale optimization, particularly data-efficient and private machine learning.

Devansh Gupta

Devansh Gupta

Ph.D. in Computer Science (co-advised with Vatsal Sharan)

B.Tech Computer Science and AI, Indraprastha Institute of Information Technology, Delhi

Research interests: learning theory, large-scale optimization, and differential privacy.

Mahdi Salmani

Mahdi Salmani

Ph.D. in Computer Science

B.Sc. Computer Engineering, Sharif University of Technology. Silver medalist, Iran’s National Mathematical Olympiad.

Research interests: large-scale optimization, especially in developing trustworthy, privacy-preserving, and robust models.

Selina Xiao

Selina (Yutong) Xiao

Ph.D. in Industrial & Systems Engineering

B.S. Computer Science and Operations Research/Information Engineering, Cornell University

Research interests: machine learning and optimization, particularly large-scale optimization methods, online and adaptive algorithms, and ML systems.

Yuqian Zhang

Yuqian Zhang

Ph.D. in Industrial & Systems Engineering

B.S. and M.S. in Mathematics and Economics, New York University

Research interests: stochastic control, large-scale optimization, and differential privacy.

Jieming Kong

Jieming Kong

Ph.D. in Industrial & Systems Engineering (co-advised with Karthyek Murthy)

M.S. Operations Research, Cornell University

Research interests: online optimization, reinforcement learning, stochastic control, and applied probability.

Ph.D. Alumni

Tianjian Huang (2018–2025) Research Scientist, Pinterest
Ali Ghafelebashi (2018–2024) ML Research Scientist, Meta
Yinbin Han (2021–2024, co-advised) NYU → Stanford
Sina Baharlouei (2017–2023) Applied Scientist, eBay
Andrew Lowy (2019–2023) Postdoctoral Researcher, UW–Madison Assistant Professor, CISPA Helmholtz Center for Information Security
Daniel Lundstrom (2021–2023) Principal Image/Signal Processing Engineer, Northrop Grumman
Babak Barazandeh (2017–2021) Senior Research Scientist, Splunk
Maher Nouiehed (2016–2019) Assistant Professor, American University of Beirut

Postdoctoral Alumni

Xinwei Zhang (2024–2025) Research Scientist, Amazon
Dmitrii Ostrovskii (2019–2021) Assistant Professor of Math and ISyE, Georgia Tech

Visiting & Undergraduate Students

Alex Mulrooney (Summer 2024) — University of Delaware
Musheng Li (Summer 2024) — Tsinghua University
Poornash Anandan Sangeetha (Summer 2024) — IIT Patna
Shivam Patel (Summer 2023) — IIT Mumbai
Vikram Meyer (Summer 2023) — Vanderbilt University
Gaia Dennison (Summer 2022) — Cal Poly Pomona
Shaunak Halbe (Summer 2021) — College of Engineering Pune
Yan Wen (Summer 2021) — Tsinghua University
Eileen Stiles (Spring & Summer 2021) — Johns Hopkins University
Rakesh Pavan (Summer & Fall 2020) — National Institute of Technology Karnataka
Prajwal Singhania (Summer 2018) — IIT Kharagpur
Shahriar Norouzizadeh (Summer 2017) — University of British Columbia