Ang Li

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I am currently a Research Associate in Qualcomm AI Research. Before joining Qualcomm, I obtained Ph.D. from Duke University under the supervision of Professor Yiran Chen and Hai “Helen” Li in Duke CEI Lab I will join the Department of Electrical and Computer Engineering at University of Maryland, College Park as a tenure-track assistant professor. 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. and 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

03/15/2023 Our paper “AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving” has been accepted by MobiCom 2023!
10/10/2022 Our paper “FedSEA: A Semi-Asynchronous Federated Learning Framework for Extremely Heterogeneous Devices” has been accepted by SenSys 2022!
10/01/2022 Our paper “GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs” has been accepted by ICDM 2022!
07/15/2022 Very honored to co-chair the 3rd DistributedML workshop co-located with CoNEXT 2022. We welcome works at the intersection of distributed systems, machine learning, and networks, submission date is 09/16/2022. Participation is highly encouraged, see you there!

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
  5. MobiCom
    AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving
    Tianyue Zheng, Ang Li, Zhe Chen, and 2 more authors
    2023