Yijiang Li | Argonne National Laboratory

Yijiang Li is an Assistant Computational Mathematician in the Mathematics and Computer Science Division at Argonne National Laboratory. His research develops distributed optimization methods for large-scale scientific machine learning, spanning federated learning across HPC facilities, efficient training and compression of large language models, and AI for energy systems. He is a developer of APPFL, an open-source framework for privacy-preserving federated learning, and his work has appeared in ICML, NeurIPS, NAACL, and KDD. He received his PhD in Operations Research from the Georgia Institute of Technology in 2023.

News

Sep, 2026	Paper accepted: Scaling Laws for Physics-Aware ACOPF Surrogate Learning. Accepted at SC’26 Workshops (AI4S).
Sep, 2026	Paper accepted: From Pilots to Production: Lessons in Cross-Institutional Federated Training and Artificial Intelligence for Science. Accepted at SC’26 Workshops (Trillion Parameter Workshops).
Sep, 2026	Federated Learning Across HPC: What Deployment Taught Us. Presentation at FPAI-HPC’26 in conjunction with IEEE CLUSTER’26, Alexandria, VA.
Sep, 2026	Federated and Privacy-Preserving AI for HPC. Panelist at FPAI-HPC’26 in conjunction with IEEE CLUSTER’26, Alexandria, VA.
Jul, 2026	Promoted to Assistant Computational Mathematician. Mathematics and Computer Science Division, Argonne National Laboratory.
Jun, 2026	Towards Scalable Federated Learning for Scientific Computing. Presentation at Trillion Parameter Consortium 2026 All-Hands Conference (TPC 26), Baltimore, MD.
Jun, 2026	LUMINA: Graph Foundation Model for Power Grid Applications. Presentation at Trillion Parameter Consortium 2026 All-Hands Conference (TPC 26), Baltimore, MD.
May, 2026	Paper accepted: Towards Systematic Generalization for Power Grid Optimization Problems. Accepted as an oral presentation (top 6.1% of submissions) at KDD 2026, AI for Sciences Track.
May, 2026	Preparing for Job Market (Academia, Industry, and National Labs). Panelist at IISE Annual Conference and Expo 2026, Arlington, TX.
May, 2026	Paper accepted: FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training. Accepted at ICML 2026.
Mar, 2026	Towards Scalable Federated Learning for Scientific Computing. Presentation at LANS Seminar, Argonne National Laboratory, Lemont, IL.
Mar, 2026	Paper accepted: LUMINA: Foundation Models for Topology Transferable ACOPF. Accepted at ICLR 2026 Workshop on Foundation Models for Science (FM4Science).
Mar, 2026	LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning. Presentation at 5th GridFM Workshop, Boston, MA.
Oct, 2025	Scaling Federated Learning to HPC. Presentation at INFORMS Annual Meeting, Atlanta, GA.
Sep, 2025	Paper accepted: Experience Building Enterprise-level Privacy-Preserving Federated Learning to Power AI for Science. Accepted at IEEE TPS-ISA 2025.
Jun, 2025	Scalable Federated Learning Across DOE HPC Clusters. Presentation at Trillion Parameter Consortium 2025 All-Hands Conference (TPC 25), San Jose, CA.
Jun, 2025	FedSpaLLM: Federated Pruning of Large Language Models. Presentation at International Conference on Continuous Optimization (ICCOPT), Los Angeles, CA.
Jun, 2025	Federated Learning Framework for Collaborative Training of Electric Grid Foundation Models. Presentation at Trillion Parameter Consortium 2025 All-Hands Conference (TPC 25), San Jose, CA.
Apr, 2025	Paper accepted: FedSpaLLM: Federated Pruning of Large Language Models. Accepted at NAACL 2025.
Mar, 2025	FedSpaLLM: Federated Pruning of Large Language Models. Presentation at SIAM Conference on Computational Science and Engineering, Fort Worth, TX.
Jan, 2025	Received grant: DOE LDRD Grant Awarded. Foundation Model for Coherent X-ray Imaging, co-PI.
Dec, 2024	Paper accepted: SparseLLM: Towards Global Pruning for Pretrained Language Models. Accepted at NeurIPS 2024.
Oct, 2024	FedSpaLLM: Federated Pruning of Large Language Models. Presentation at INFORMS Annual Meeting, Seattle, WA.