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 firstname.lastname@example.org with you CV and transcripts attached.
|08/14/2023||I am deeply honored and thrilled to share that I have been selected as the recipient of the IEEE Cyber-Physical Systems (TCCPS) Outstanding Ph.D. Dissertation Award!|
|05/08/2023||I am beyond thrilled to share that I have been selected as one of the two recipients for the ECE Department Outstanding Ph.D. Dissertation Award for the academic year 22-23 at Duke University!|
|04/27/2023||Our research paper titled “Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction” has been accepted for presentation at ICML 2023.|
|04/25/2023||I am beyond thrilled to share that I have been selected as one of the two recipients for the ECE Department Outstanding Ph.D. Dissertation Award for the academic year 22-23 at Duke University!|
|03/15/2023||Our paper “AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving” has been accepted by MobiCom 2023!|
- SECLotteryFL: empower edge intelligence with personalized and communication-efficient federated learningIn 2021 IEEE/ACM Symposium on Edge Computing (SEC), 2021
- KDDTIPRDC: task-independent privacy-respecting data crowdsourcing framework for deep learning with anonymized intermediate representationsIn Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020
- MobiComHermes: an efficient federated learning framework for heterogeneous mobile clientsIn Proceedings of the 27th Annual International Conference on Mobile Computing and Networking, 2021
- SenSysFedmask: Joint computation and communication-efficient personalized federated learning via heterogeneous maskingIn Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, 2021
- MobiComAutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving2023