Paper 'Semantic Multi-Resolution Communications' accepted to IEEE Global Communications Conference; 'Personalized decentralized multi-task learning over dynamic communication graph' presented at the IEEE Conference on Information Sciences and Systems; 'Hierarchical over-the-air FedGradNorm' included in the 56th Asilomar Conference on Signals, Systems, and Computers; 'FedGradNorm: personalized federated gradient-normalized multi-task learning' featured in the EEE International Workshop on Signal Processing Advances in Wireless Communications.
Research Experience
Involved in deep learning-based video streaming projects aimed at improving live video quality over wireless networks through real-time adjustment of encoder parameters; also conducted research on semantic multi-resolution communications, developing a novel deep learning multi-resolution joint source-channel coding framework.
Education
5th year Ph.D. candidate in the Electrical and Computer Engineering Department at the University of Maryland, under the supervision of Prof. Sennur Ulukus.
Background
Research interests include wireless communication, federated learning, multi-task learning, semantic communication, and video streaming technologies. Currently working on personalization, statistical heterogeneity (non-I.I.D), and communication efficiency in federated learning; also focusing on end-to-end semantic communication, semantic multi-user communication, and semantic multi-resolution communication.