Salman Avestimehr

I am a professor, serial entrepreneur, and researcher working at the intersection of information theory, machine learning, distributed systems, and trustworthy AI. As the inaugural Director of the USC-Amazon Center on Trustworthy AI, the director of the vITAL Research Lab, and co-director of Falcon AI Lab, I lead research on both the fundamental principles of AI, including trustworthiness, uncertainty, and the limits of learning, and the practical systems needed to build and deploy AI at scale. I am also the co-founder of FedML, TensorOpera AI, ChainOpera AI, and Teamily AI, building platforms that bring AI closer to people and organizations. Our philosophy is to advance the foundations and systems of AI toward intelligent technologies that are trustworthy, collaborative, and capable of amplifying human creativity, scientific discovery, and societal progress.

Dean's Professor at USC Serial Entrepreneur IEEE Fellow PECASE Awardee
Salman Avestimehr

About

Salman Avestimehr is a Dean's Professor of Electrical and Computer Engineering and Computer Science at the University of Southern California, where he serves as the inaugural Director of the USC-Amazon Center on Trustworthy AI. He is also Founding Co-Director of Falcon AI Lab, a collaborative research hub between USC, UCI, and Stanford University for AI-driven chip and analog circuit design.

He is also a serial entrepreneur and co-founder of several leading AI initiatives, including FedML (a widely adopted open-source library for federated machine learning); TensorOpera AI (a full-stack agentic AI platform); ChainOpera AI (a decentralized AI infrastructure platform); and Teamily AI (a human-AI social platform for collaborative intelligence).

Dr. Avestimehr received his Ph.D. (2008) and M.S. (2005) in Electrical Engineering and Computer Science from the University of California, Berkeley. His research focuses on information theory, distributed and federated machine learning, and trustworthy AI systems. His work has received numerous prestigious recognitions, including the Presidential Early Career Award for Scientists and Engineers (PECASE), the James L. Massey Research & Teaching Award from the IEEE Information Theory Society, a Joint Paper Award from the IEEE Information Theory and Communication Societies, and a Young Investigator Program award from the U.S. Air Force Office of Scientific Research. He is also a recipient of the National Science Foundation CAREER Award, the David J. Sakrison Memorial Prize, and multiple best paper awards at leading conferences. Dr. Avestimehr is an IEEE Fellow.

Nine Pillars of Research

Two decades of foundational contributions spanning information theory, distributed systems, trustworthy AI, and AI-driven scientific discovery.

Uncertainty in GenAI

Uncertainty Principles for Generative AI

Developing principled methods for quantifying uncertainty and detecting hallucinations in large language models, including semantic-aware scoring and comprehensive benchmarking.

MARS: Meaning-Aware Response Scoring — ACL, 2024
TruthTorchLM: Predicting Truthfulness in LLM Outputs — EMNLP, 2025
Uncertainty Quantification for Hallucination Detection in LLMs — IEEE BITS, 2026
Trustworthy AI

Trustworthy AI

Advancing privacy, security, and fairness in AI systems through Byzantine-resilient learning, secure aggregation, fair federated learning, and privacy-preserving computation.

FairFed: Enabling Group Fairness in Federated Learning — AAAI, 2023
LOKI: Large-scale Data Reconstruction Attack against Federated Learning — IEEE S&P, 2024
Byzantine-Resilient Secure Federated Learning — IEEE JSAC, 2020
AI Chip Design

AI-Driven Chip & Analog Circuit Design

Applying machine learning to fully automate analog and RF circuit design, from topology selection to layout-constrained parameter optimization and EM-aware physical synthesis. He has co-founded Falcon AI Lab as a research hub between USC, UCI, and Stanford University for this endeavor.

FALCON: Fully Automated Layout-Constrained Analog Circuit Design — NeurIPS, 2025
AICircuit: A Multi-Level Dataset for AI-Driven Analog IC Design — NeurIPS ML4PS, 2024
EM-Aware Physical Synthesis for RF Circuits — arXiv, 2026
Agentic Systems

Agentic Systems

Building the foundations for collaborative AI agent networks, multi-model routing, and human-AI teaming platforms that enable scalable, trustworthy autonomous systems.

TensorOpera Router: Multi-Model Routing — EMNLP, 2024
Toward Super Agent System with Hybrid AI Routers — arXiv, 2025
Leveraging Uncertainty Estimation for Efficient LLM Routing — ICML Workshop, 2025
Fundamental Limits

Fundamental Limits of AI-Driven Scientific Discovery

Establishing the theoretical foundations for understanding how much new knowledge AI systems can discover, using Good-Turing estimation and missing-mass analysis to derive scaling laws.

NOVA: Fundamental Limits of Knowledge Discovery Through AI — arXiv, 2026
NOVA Scientific Companion — Interactive Visual Guide
LANTERN: LNP Transfection Efficiency Prediction — Communications AI and Computing, Nature Portfolio, 2026
Efficient Model Training and Inference

Efficient Model Training and Inference

Developing efficient methods for training and serving large-scale AI models, including pipeline parallelism, federated parameter-efficient fine-tuning, and resource-constrained edge deployment.

PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models — ICML, 2021
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models — arXiv, 2024
Enabling Resource-Efficient On-Device Fine-Tuning of LLMs Using Only Inference Engines — arXiv, 2025
Federated Learning

Distributed & Federated Machine Learning

Developed foundational frameworks for scalable, communication-efficient federated learning, including FedML (open-source library), LightSecAgg, and heterogeneous model training.

FedML: A Research Library for Federated ML — NeurIPS SpicyFL, 2020
LightSecAgg: A Lightweight and Versatile Design for Secure Aggregation in FL — MLSys, 2022
Advances and Open Problems in Federated Learning — Foundations & Trends in ML, 2021 (Field Guide)
Coded Computing

Distributed & Coded Computing

Founded the field of coded computing, using coding theory to inject computation redundancy and mitigate stragglers, communication bottlenecks, and privacy leakage in distributed systems.

Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication — JMLR, 2020
A Fundamental Tradeoff Between Computation and Communication in Distributed Computing — IEEE Trans. IT, 2018
Coded Computing: Mitigating Fundamental Bottlenecks — Foundations & Trends, 2020
Network Information Theory

Network Information Theory

Pioneered the deterministic approach to wireless network information flow, providing tractable capacity approximations for complex multi-hop, multi-flow wireless networks.

Wireless Network Information Flow: A Deterministic Approach — IEEE Trans. IT, 2011
An Approximation Approach to Network Information Theory — Foundations & Trends, 2015
Multihop Wireless Networks: A Unified Approach to Relaying and Interference Management — Foundations & Trends, 2014

Recent Publications

Latest research from Google Scholar, sorted by date.

NOVA: Fundamental Limits of Knowledge Discovery Through AI

S. Avestimehr, K. Duffy, M. Medard

arXiv preprint, 2026

Uncertainty Quantification for Hallucination Detection in Large Language Models

S. Kang, Y.F. Bakman, D.N. Yaldiz, B. Buyukates, S. Avestimehr

IEEE BITS the Information Theory Magazine, 2026

FALCON: An ML Framework for Fully Automated Layout-Constrained Analog Circuit Design

A. Mehradfar, X. Zhao, Y. Huang, E. Ceyani, Y. Yang, et al.

NeurIPS 2025

ATHENA: Adaptive Test-Time Steering for Improving Count Fidelity in Diffusion Models

M.S. Sepehri, A. Mehradfar, B. Tinaz, S. Avestimehr, M. Soltanolkotabi

arXiv preprint, 2026

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework

S. Kang, J. Kim, S. Avestimehr, S. Lee

AAAI 2026

LANTERN: A Machine Learning Framework for LNP Transfection Efficiency Prediction

A. Mehradfar, M.S. Sepehri, J.M. Hernandez-Lobato, et al.

Communications AI and Computing, Nature Portfolio, 2026 (Accepted)

TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs

D.N. Yaldiz, Y.F. Bakman, S. Kang, A. Ozis, et al.

EMNLP 2025

Reconsidering LLM Uncertainty Estimation Methods in the Wild

Y.F. Bakman, D.N. Yaldiz, S. Kang, T. Zhang, B. Buyukates, S. Avestimehr

ACL 2025

Towards Fair Decentralized Benchmarking of Healthcare AI with Federated Tumor Segmentation

M. Zenk, U. Baid, S. Pati, A. Linardos, et al.

Nature Communications, 2025

Hair-Trigger Alignment: Black-Box Evaluation Cannot Guarantee Post-Update Alignment

Y. Bakman, D.N. Yaldiz, S. Avestimehr, S.P. Karimireddy

ICML 2026

Students & Graduates

12 alumni in faculty positions worldwide. Many more in industry at Google, Meta, Amazon, Microsoft, Goldman Sachs, and leading AI companies.

Alumni — Now Professors

Asst. Professor, UIUC (ECE & CS)
PhD 2009-2013. Multi-Hop Multi-Flow Wireless Networks
NSF CAREER, Jack K. Wolf Award Finalist, Simons Fellowship
Gleason Endowed Assoc. Professor, RIT (ECE)
PhD 2009-2013. Imperfect Feedback in Wireless Networks
Best PhD Thesis Award, Qualcomm Innovation Award
Professor, Southeast University (formerly HKUST)
PhD 2012-2018. Coded Computing for Large-Scale ML
EE Fellowship, Qualcomm Innovation Finalist
Asst. Professor, UC Santa Barbara (ECE)
PhD 2015-2020. Coded Computing
Thomas M. Cover Award, Google Fellowship, Jack K. Wolf Award
Asst. Professor, IIT Madras (EE)
PhD 2016-2022. Coding for Large Scale Graph Analytics
Qualcomm Innovation Fellowship, IGB Postdoctoral Fellow (UIUC)
Asst. Professor, DGIST, South Korea (EECS)
PhD 2017-2022. Coding for Privacy-Preserving ML
Best Paper Award NeurIPS 2022 Workshop, Samsung Future Tech Program
Aly El Gamal
Asst. Professor, Purdue University (ECE)
Postdoc 2014-2015
Asst. Professor, UC Riverside
Postdoc 2017-2020
NSF CAREER Award
Asst. Professor, Univ. of Birmingham, UK (CS)
Postdoc 2022-2024
Andrea Goldsmith Young Scholars Award 2024
Aditya Siripuram
Asst. Professor, IIT Hyderabad (EE)
Postdoc 2016-2017
Sunwoo Lee
Asst. Professor, Inha University, South Korea
Postdoc 2020-2022
Navid Naderializadeh
Asst. Research Professor, Duke University
PhD 2011-2016. 5G Architectures
MHI PhD Scholar, Shannon Competition Finalist

Alumni — Industry Leaders

Co-founder of TensorOpera AI, ChainOpera AI, and Teamily AI
PhD 2018-2023. Distributed Learning
Amazon PhD Fellowship, Qualcomm Innovation Fellowship, Best Paper Awards at NeurIPS and AAAI
Research Scientist, Meta
PhD 2018-2025. Privacy-Preserving Machine Learning
Best Poster Award at USC-Amazon Symposium
Ahmed Roushdy Elkordy
Software Engineer, Microsoft
PhD 2019-2024. Federated Learning
Research Scientist, Google Research
PhD 2019-2025. Efficient Machine Learning
WiSE Qualcomm Top-Off Fellowship, NeurIPS 2023 & 2025
Research Scientist, Amazon
PhD 2018-2025. Trustworthy AI & Uncertainty Estimation
Fulbright Scholar, Best Poster at USC-Amazon Symposium
Applied Scientist, Amazon
PhD 2021-2025. Federated Learning & Foundation Models
Sina Lashgari
Program Trading Strategist, Goldman Sachs
PhD 2010-2014. Delay & Deadline in Wireless Networks
Amandy Nwana
Researcher, YouTube
PhD 2010-2014. Social Media Prediction
David Kao
Research Scientist, Google
Postdoc 2013-2015
Ramy Ali
Research Scientist, Samsung
Postdoc 2020-2023
Yahya Ezzeldin
Staff Engineer, MediaTek USA
Postdoc 2021-2023
Mingchao (Fisher) Yu
Co-Founder & CTO, Babylon
Postdoc 2017-2020
Research and Development Engineer, Supra
PhD 2018-2025. Privacy-Preserving Machine Learning & Cryptography

Current PhD Students & Postdocs

PhD Candidate (since 2021)
Federated Graph Learning & AI4Science
2025 Qualcomm Innovation Fellowship Finalist, NeurIPS Top Reviewer
PhD Student (since 2022)
Trustworthy LLMs
CS Fellowship, Capital One Responsible AI Fellowship
PhD Student (since 2022)
Uncertainty Estimation & Continual Learning
PhD Student (since 2021)
Efficient & Private ML Systems, LLM Inference
EE Department Fellowship
PhD Candidate (since 2023)
AI-Driven Molecular Design & Circuit Design
PhD Student
Computer Security, Privacy & High-Performance Computing

Ventures

From research breakthroughs to real-world impact through four venture-backed companies.

Teamily AI

Co-founder

The first human+AI social platform for collaborative intelligence. Teamily brings AI agent teams to human teams, enabling seamless collaboration between people and AI in a social network environment.

TensorOpera AI

Co-founder

A full-stack agentic AI platform providing enterprise-grade AI infrastructure for building, deploying, and managing AI agents at scale. Evolved from the FedML open-source project.

ChainOpera AI

Co-founder

A decentralized AI infrastructure platform built on blockchain Layer 1 technology, enabling trustless, scalable AI computation and model serving across distributed networks.

FedML

Co-founder

A widely adopted open-source library and benchmarking ecosystem for federated machine learning. With 1000+ citations, and usage by 100+ enterprises, FedML has become the standard platform for FL deployment worldwide.

Awards & Honors

2026

Keynote Speaker, FLICS 2026

2nd International Conference on Federated Learning and Intelligent Computing Systems, Valencia, Spain

2025

Capital One CREDIF Award

Research on Deployment of Uncertainty Estimation Methods for Hallucination Detection

2023

Best Paper Award, FL@FM-NeurIPS'23

Federated Learning and Foundation Models Workshop at NeurIPS

2022

Thomas M. Cover Dissertation Award

IEEE Information Theory Society (as advisor to Qian Yu)

2021

Dean's Professor

University of Southern California

2020

IEEE Fellow

For contributions to network information theory and coded computing

2019

James L. Massey Research & Teaching Award

IEEE Information Theory Society

2015

Okawa Foundation Award

Information and Telecommunications

2013

Joint Paper Award

IEEE Information Theory and Communication Societies

2012

Presidential Early Career Award (PECASE)

Awarded by President Obama for outstanding contributions to science and engineering

2011

Air Force Young Investigator Program (YIP)

U.S. Air Force Office of Scientific Research

2010

NSF CAREER Award

National Science Foundation

2008

David J. Sakrison Memorial Prize

Best PhD Thesis Award, UC Berkeley EECS

2017

Jack K. Wolf Best Paper Award

IEEE International Symposium on Information Theory (ISIT)