Ang Li

I am currently a tenure-track assistant professor in the Department of Electrical and Computer Engineering at University of Maryland College Park. Before joining UMD, I was a Research Associate in Qualcomm AI Research. I obtained Ph.D. from Duke University under the supervision of Professor Yiran Chen. My research interests lie in the intersection of machine learning and edge computing, with a focus on building large-scale networked and trustworthy intelligent systems to solve practical problems in a collaborative, scalable, secure, and ubiquitous manner.
Openings: I’m looking for highly motivated Ph.D., master students, and research interns to join my group. If you are interested, please send an email to angliece@umd.edu with you CV and transcripts attached.
News
01/22/2025 | Our paper “Towards Counterfactual Fairness thorough Auxiliary Variables” has been accepted by ICLR 2025! |
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01/15/2025 | I have received the Cisco Research Award! I deeply appreciate Cisco’s generous support, which will empower our ongoing research and innovation. |
01/15/2025 | I have been invited to serve as an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS). |
10/16/2024 | Our paper “Moderator: Moderating Text-to-Image Diffusion Models through Fine-grained Context-based Policies” received Distinguished Paper Award at ACM CCS’24. Congrats to all the co-authors! |
09/26/2024 | We have three papers accepted by NeurIPS 2024 (2 Main Conference + 1 Dataset & Benchmark Track)! Congratulations to my students and collaborators. |
08/26/2024 | Invited to serve as area chair for ICLR 2025. |
02/26/2024 | Our research paper titled “MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling” has been accepted by CVPR 2024. |
02/24/2024 | Our research paper titled “SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models” has been accepted by MLSys 2024. |
01/22/2024 | Our research paper titled “FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent” and “Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting” have been accepted by ICLR 2024. |
10/07/2023 | Our research paper titled “FedNAR: Federated Optimization with Normalized Annealing Regularization” has been accepted by NeurIPS 2023. |