Sai Praneeth Karimireddy
Assistant Professor of Computer Science (& by courtesy, ECE), USC
I work on principled approaches to trustworthy AI — combining optimization, statistics, and economics to design, evaluate, and improve real-world AI systems. Before USC I was an SNSF postdoc with Michael I. Jordan at UC Berkeley, and I completed my PhD at EPFL advised by Martin Jaggi. My work has been deployed at Meta, Google, OpenAI, and Owkin. See the group page for how to work with me.
Research
AI is changing the world in unprecedented ways, turning once-philosophical questions — what is truth, what improves human welfare — into urgent technical ones. I take a principled approach to formalizing, understanding, and answering them. Some things I'm thinking about:
Trustworthy AI. What does it mean for LLMs to be safe or private? How can we evaluate and characterize LLM capabilities and behaviors?
AI ecosystems. How will AI agents interact with humans, society, and each other? How can we understand and shape the resulting emergent phenomena?
AI in health & finance. Can we translate these insights to deploy AI in high-stakes settings such as healthcare and finance?
Selected Work
What are the fundamental limits of black-box auditing?
How do you feed context to an LLM in a differentially-private manner?
How do you model multi-agent cooperation and design incentives without monetary rewards?
What distinguishes important corner-case data from noisy outliers?
News
Serving as an Area Chair at NeurIPS 2026.
Gave a talk at the Google Privacy in ML seminar on privacy-preserving synthetic data generation (video to come).
Gave a talk to Capital One on Private Synthetic Datasets for Enterprise AI. Slides.
Appointed as visiting researcher at Simons Institute, UC Berkeley. Reach out if you are in the Bay Area and want to chat!
Gave a tutorial on Data Valuation and incentives for data sharing, together with Han Shao.
Invited to give a talk at The Nexus of Open Science event in Washington, DC.
Show more news
Serving as an Area Chair at ICLR 2026.
Excited to help run the workshop on Incentives in Data Sharing at TTIC this summer — submissions and registration now open.
Co-organizing a TTIC summer workshop on Incentives in Data Sharing and Collaborative Learning (Aug 13–15). Reach out if you would like to give a talk or take part.
Named a Capital One Fellow along with Robin Jia — thank you for the support!
Received a joint appointment with the Ming Hsieh Department of ECE. You can now apply to work with me through PhD programs in both CS and ECE.
Gave talks in the Chicago area — the CS seminar at the University of Chicago and the LANS seminar at Argonne National Laboratory.
Invited talks at the USC Theory Lunch and INFORMS 2024, plus a guest lecture in USC CSCI 697 on "Building Collaborative Data-Ecosystems".
Serving as an Area Chair at ICLR 2025.
Co-organizing the workshop on Federated Learning in the Age of Foundation Models at NeurIPS 2024.
Appointed co-lead of the Data Quality and Federated Learning working group with Holger Roth as part of MONAI.
At the Foundations of Responsible Computing conference (FORC 2024) in Boston, Jun 12–14.
Awards
- Amazon Center on Secure & Trusted ML Award
- Capital One Fellowship
- SNSF Mobility Fellowship
- Patrick Denantes Memorial Prize — best PhD thesis in computer science, EPFL
- Dimitris N. Chorafas Foundation Prize — exceptional applied research