Ang Li

prof_pic.jpg

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!
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.

Selected Publications

  1. SEC
    LotteryFL: empower edge intelligence with personalized and communication-efficient federated learning
    Ang Li, Jingwei Sun, Binghui Wang, and 4 more authors
    In 2021 IEEE/ACM Symposium on Edge Computing (SEC), 2021
  2. KDD
    TIPRDC: task-independent privacy-respecting data crowdsourcing framework for deep learning with anonymized intermediate representations
    Ang Li, Yixiao Duan, Huanrui Yang, and 2 more authors
    In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020
  3. MobiCom
    Hermes: an efficient federated learning framework for heterogeneous mobile clients
    Ang Li, Jingwei Sun, Pengcheng Li, and 3 more authors
    In Proceedings of the 27th Annual International Conference on Mobile Computing and Networking, 2021
  4. SenSys
    Fedmask: Joint computation and communication-efficient personalized federated learning via heterogeneous masking
    Ang Li, Jingwei Sun, Xiao Zeng, and 3 more authors
    In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, 2021