Welcome to my site!

• I keep looking for passionate Ph.D. students and I plan to hire 1-2 Ph.D. students every year. And currently, the team has one postdoc opening.  Note that at Georgia Tech., it is common to have two advisors co-advise you. If you plan to apply for a Ph.D. position and select me as the unique advisor, you may apply through the machine learning program, and the ECE program, and select ECE as your home unit.  The home unit at Georgia Tech. is not much related to the program/the degree but related to where the advisor is. You may also join other programs such as the CS program or the ISYE program and still would like to work with me.  In these cases, I may work as a co-advisor of you and feel free to reach out to me and let me know who you also plan to work with in those programs. I am happy to help with the coordination. For prospective students/postdocs, please read this page carefully.

My dear wife, Dr. Cong Hao, is also at the school of ECE at Georgia Tech. 

SHORT BIO 

(To introduce Pan before the talk, one may use this short bio for convenience.) 

Pan Li is an associate professor at Georgia Tech. ECE and is also a visiting faculty researcher at Google Research since 2026 May.  He is the faculty lead and plenary speaker for the AI/ML direction of the NSF A3D3 HDR Institute and a key member of the IceCube Neutrino Observatory collaboration. Before that, he was a Research consultant collaborating with Meta Networking Systems team and Meta Core AI team from Fall 2024 to Spring 2026, and was an assistant professor at Purdue CS from Fall 2020 to Fall 2023.  Before joining Purdue, Pan worked as a postdoc in the SNAP group at Stanford for one year, led by Prof. Jure Leskovec.  Before joining the SNAP group, Pan did his Ph.D. in Electrical and Computer Engineering at the University of Illinois Urbana - Champaign (2015 - 2019). His PhD advisor at UIUC was Prof. Olgica Milenkovic. At UIUC, he also worked with several wonderful collaborators including Prof. Niao He, Prof. Arya Mazumdar, Prof. Jiawei Han, Prof. David Gleich, etc. 

Pan Li has received the NSF CAREER award (2023), the Best Paper award from Learning on Graph (LoG) 2022, Nvidia Academic Award (2025, 2026), Amazon Research Award (2023, 2026), Sony Faculty Innovation Award (2021), JPMorgan Faculty Award (2021 & 2023 & 2024), Ross-Lynn Faculty Award (2021). Pan Li has served as a Senior reviewer (Area Chair) in conferences on machine learning and data science such as ICLR, ICML, NeurIPS, WWW, WSDM, and AAAI.

News

• 2026/10 [Talks] Check out my talk at the ASU seminar on Structured Intelligence: How to fill structured data into Modern AI systems.

• 2026/09 [Papers] Two papers accepted by NeurIPS. Big congrats to the relevant students and collaborators!

• 2026/09 [Awards] Got Amazon Research Award for Structure-Guided Agent Safety and Microsoft Azure AI-for-Science Award on Real-time Diffusion Models. 

• 2026/08 [Service] I served as a mentor to emerging AI leaders at the ACM AI Leadership Summit. 

• 2026/06 [Papers] Two papers accepted by COLM. Big congrats to the relevant students and collaborators!

• 2026/05 I started my visiting position at Google Research, working on representation learning for AI agent systems. 

• 2026/04 [Papers] Seven papers (four papers led by our group + 3 collaborative papers) accepted by ICML. Big congrats to the relevant students and collaborators!

• 2026/04 [Talks]  Give a talk on Generalization and Robustness on Graph Machine Learning at Two Sigma

• 2026/04 [Awards] Win Nvidia Academic Award! Thanks Nvidia for the support!

• 2026/03 [Papers]  The paper on Graph State Space Models (code) got accepted by TPAMI. Big congrats to Yinan and Siqi!  

• 2026/03 Got Promoted to (Tenured) Associate Professor! 

• 2026/02 [Papers] The paper on Fourier analysis for the Magnetic Laplacian Matrix got accepted by PNAS! We discovered a fundamental connection between the random walk landing probabilities on directed graphs and the spectrum of the magnetic Laplacian. Big congrats to Yinan and Dr. Gleich! I am very impressed by this kind of fundamental discovery.  

• 2026/01 [Papers] One paper accepted by AISTATS, one paper accepted by WWW, and five papers (three papers led by our group + 2 collaborative papers) accepted by ICLR. Big congrats to relevant students and collaborators!

• 2025/12 Congratulations to Rongzhe Wei for winning the CSIP Outstanding Research Award for 2025! 

• 2025/12 We have got NAIRR computation support! I become an affiliated member of the IceCube Neutrino Observatory.

• 2025/11 Keynote Talk at ICDM on Graph Augmented Language Models: Beyond the Sequence - Challenges and Opportunities in Integrating Graphs and LLMs.

• 2025/10 Invited Talk at INFORMS on Hypergraph Pagerank Diffusion.

• 2025/09 Big congratulations to Peihao Wang on winning this year’s Google Fellowship and the Data Science Rising Star honor—well deserved! Excited to continue our collaboration with Peihao and Atlas. Big congratulations to Shitij Govil on winning President's Undergraduate Research Award 

• 2025/09 Four papers in NeurIPS'25, big congrats to Haoyu, Shikun, Rongzhe, Peihao, Mufei, and all the great collaborators!

• 2025/07 Our group member Dr. Eli Chien is to join Google Research as a visiting scholar and then to join National Taiwan University EE as an Assistant Professor 

• 2025/07 One paper in COLM'25: Privately Learning from Graphs with Applications in Fine-tuning Large Language Models (code)

• 2025/06 Win Nvidia Academic Award! Thanks Nvidia for the support!

• 2025/06 Selected as an IEEE Senior Member

• 2025/05 Congrats to Dr. Haoteng Yin on graduating! Haoteng is to join Google! Wish you a best future!  

• 2025/05 Three papers in ICML'25, big congrats to Haoyu, Shikun, Rongzhe, Jiajun, Peihao, Mufei, and all the great collaborators!

• 2025/03 Received the ECE Outstanding Junior Faculty Award! 

• 2025/03 Give a talk at Umich and MSU on "Towards Efficient and Robust Graph and Geometric Machine Learning". 

• 2025/01 Five papers in ICLR'25, big congrats to Eli, Yinan, Haoyu, Peihao, Jiajun, Mufei, Siqi, Viraj, and all the great collaborators!

• 2025/01 The work on a comprehensive overview of labeling tricks, including multi-node labeling tricks and partial-order labeling tricks, has been accepted by JMLR! Improving Graph Neural Networks on Multi-node Tasks with Labeling Tricks

• 2025/01 Really excited to get the NSF award on the research project Physics-informed GDL for Neutrino Reconstruction! (collaborated with Prof. Ignacio Taboada)

• 2024/12 The paper on understanding confusing data patterns in explainable AI for geometric deep learning is accepted by Nature Machine Intelligence. The formal version is here. Big congrats to the team! 

• 2024/11 Give a talk at Google Deepmind on Graph-based RAG. Thanks for the invitation and looking forward to the follow-up collaboration! The relevant paper is Simple is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation (code) 

• 2024/10 Received 2024 JP Morgan faculty research award! Many thanks for JPMC and my close collaborators in JPMC, Vamsi Potluru, Mohsen Ghassemi, and Eleonora Kreacic. Look forward to continuing our collaboration!

• 2024/10 Organized mini-symposiums at SIAM MDS 2024 (New Frontier of Graph Machine Learning, New Frontier of Privacy in Machine Learning )—Many thanks to all speakers and poster presenters for their contributions! 

• 2024/10 Big congrats to Haoteng, who has won the RCAC Cyberinfrastructure Symposium Best Poster Award ($1k travel grant) for his recent studies on privacy-preserved graph learning and LLM: Privately Learning from Graphs with Applications in Fine-tuning Large Language Models (codes)

• 2024/10 Give a talk at Purdue CS on Model Generalization & Computation for Graph/Geometric ML and Allerton 24 on Differentially Private Graph Diffusion.

• 2024/9 Four papers in NeurIPS'24, big congrats to Eli, Rongzhe, Haoyu, Deyu, Shikun, Siqi, and all the great collaborators.

• 2024/9 Three papers in TMLR, big congrats to Rongzhe, Haoyu, and Mufei, and all the great collaborators.

• 2024/08 Really excited to get the NSF award on the research project "Machine Learning on Graphs with Distribution Shifts"!

• 2024/07-08 Attend ICML and Travel in Asia: Give multiple talks on "Towards Efficient and Robust Graph and Geometric Machine Learning". Many thanks to the organizers!

• 2024/07 Thank OpenAI for providing credits for our LLM research.

• 2024/06 Our survey paper "Exploring the Opportunities and Challenges of Graph Neural Networks in Electrical Engineering" got accepted by Nature Reviews Electrical Engineering. Big congrats to Eli and Mufei, and the whole team!

Past news can be found here.