Welcome to my site!
Pan Li (李 攀 in Chinese)
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.
Our team also provides some short-term research opportunities (approximately half a year to a year) for undergraduates and master's students, mainly focusing on applying graph machine learning methods to specific scientific problems in the fields of particle physics, material science, and astronomy. Excellent students can receive remuneration!
We also have a one-year program for funded post-bachelor students (only for US citizens and permanent residency due to the funding restriction) in the area of graph computation and machine learning.
My dear wife, Dr. Cong Hao, is also at the school of ECE at Georgia Tech. She is looking for excellent students who would like to work in the joint area of hardware design and machine learning.
SHORT BIO
(To introduce Pan before the talk, one may use this short bio for convenience.)
Pan Li joined ECE at Georgia Tech as an assistant professor in 2023 Spring and an on-leave position at Purdue CS. Pan Li joined the Purdue CS department as an assistant professor in 2020 Fall. Before joining Purdue, Pan worked as a postdoc in the SNAP group at Stanford for one year, where he worked in the SNAP group 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, Amazon Research Award (2023), Sony Faculty Innovation Award (2021), JPMorgan Faculty Award (2021 & 2023), 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
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. 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—Many thanks to all speakers and poster presenters for their contributions!
New Frontier of Graph Machine Learning
New Frontier of Privacy in Machine Learning
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, big congrats to Eli, Rongzhe, Haoyu, Deyu, Shikun, Siqi, and all the great collaborators.
Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning (Spotlight)
Differentially Private Graph Diffusion with Applications in Personalized PageRanks
Certified Machine Unlearning via Noisy Stochastic Gradient Descent
GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shift (Benchmark Track)
2024/9 Three papers in TMLR, big congrats to Rongzhe, Haoyu, and Mufei, and all the great collaborators.
Privacy-preserving structured data generation. Big congrats to Rongzhe! This work was also selected to be presented at ICLR 2025 (2024/11).
GraphMaker: Can Diffusion Models Generate Large Attributed Graphs? (codes) Big congrats to Mufei!
DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee (codes). Big congrats to Haoyu!
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!
2024/06 Give a talk at SES AI on Model Generalization and Foundation Models for Graphs.
2024/05 Three papers got accepted by ICML'24. Big congrats to the leading students Siqi, Shikun, Xiyuan, and other collaborators.
2024/04 Invited talk at UIUC CS Colloquium on "Challenges and Opportunities on Graph Machine Learning: A Study on Graph Data Distribution Shifts", many thanks for my great collaborator Han Zhao's invitation! I miss UIUC so much.
2024/04 Big congrats to Haoteng, who has won the Purdue Teaching Academy Graduate Teaching Award for his GTA effort in 2023!
2024/03 Our NSF award project "Privacy-preserving Machine Learning on Graphs" got funded! Many thanks for NSF! I feel excited to work with Prof. Olgica Milenkovic and Prof. Kamarlika Chaudhuri on this project. Also, many thanks to Eli for assisting the team with the proposal writing.
2024/02 Give an invited talk at the Applied and Computational Math Seminar at GT.
2024/02 Invited to give a talk "Machine Learning on Graphs --- Design System-friendly Algorithms" at CRNCH Summit 2024.
2024/01 One paper got accepted by WWW'24. Big congrats to Haoteng, Tianyi, and other collaborators.
2024/01 Three papers got accepted by ICLR'24. Big congrats to Peihao, Yinan and Tianci and other collaborators.
2023/11 Give an invited talk on "The Stability of Positional Encodings for Graphs" at Mathematical Machine Learning Seminar MPI + UCLA.
2023/10 Our new work on graph anomaly detection (code) by leveraging our previous neighborhood reconstruction (code) gets accepted by WSDM'24. Big congrats to Amit and other collaborators!
2023/09 Our work on differentially private GNN (code) gets accepted by NeurIPS' 23. Big congrats to Eli and other collaborators!
2023/08 The work HDE on distance encoding over heterogeneous networks got accepted by TKDE! Thanks for Prof. Chuan Shi leading this project.
2023/08 Received JP Morgan faculty research award! Received Azure credits from GT IDEaS! Many thanks for the support!
2023/06 The paper on SUREL+, which is an upgraded version of SUREL for subgraph representation learning gets accepted by VLDB'23 (codes). Congrats to Haoteng!
2023/05 Give a talk on "Unsupervised learning for combinatorial optimization" at SIAM OP23.
2023/04 One paper gets accepted by ICML'23 (submitted/accepted = 1/1)! Big congrats to the leading student Shikun Liu and other collaborators!
"Structural-reweighting Improves Graph Domain Adaptation" (codes): The project aims to address the generalization issue when we apply graph machine learning methods in science. We observe conditional structural distribution shifts in graph data including social networks and physics data. We give a formal mathematical definition of it and propose an algorithm based on graph structure bootstrapping to address the problem.
2023/02 Give a keynote talk at AAAI DLG'23 workshop on "interpretable and trustworthy graph/geometric learning" (slides).
2023/01 Really excited to get the NSF CAREER award on the research project "Modern Machine Learning on Graphs: From Theory to Practice"!
2023/01 One paper gets accepted by WWW'23 (submitted/accepted = 1/1)! Big congrats to the leading students Susheel Suresh and other collaborators!
2023/01 Three papers get accepted by ICLR'23 (submitted/accepted = 3/3)! Big congrats to the leading students Siqi Miao, Haoyu Wang, Peihao Wang, and other collaborators!
Past news can be found here.