Tze-Yang Tung is a PhD student at the Intelligent Systems and Networks Group at Imperial College London since September 2019. He previously received his BEng degree from the department of Electrical and Electronic Engineering also from Imperial College London. Subsequently, he received his MSc degree from the University of Southern California in Electrical and Computer Engineering where he researched in molecular communications. His current research is in joint source-channel coding for wireless image and video transmission with emphasis on utilising deep learning techniques as well as context-aware effective communications in multi-agent reinforcement learning. He is interesting in the junction between machine learning and the communication schemes that enable machine learning algorithms to succeed. A tight integration between machine learning and communications is essential for future AI developments.
Below is a list of his publications.
→ T. Tung, and D. Gunduz, SparseCast: Hybrid Digital-Analog Wireless Image Transmission Exploiting Frequency Domain Sparsity, IEEE Communications Letters, Vol. 22 - No. 12, 2018.
→ T. Tung, S. Kobus, J. Roig Pujol, and D. Gunduz, A joint learning and communication framework formulti-agent reinforcement learning over noisy channels, IEEE Journal on Selected Areas in Communications (JSAC), Special Issue on Machine Learning in Communications and Networks, 2021
→ T. Tung, and U. Mitra, Increasing Robustness to Synchronisation Errors in Molecular Communications, International Symposium on Turbo Codes & Iterative Information Processing, Dec., 2018.
→ T. Tung, U. Mitra, Robust Molecular Communications: DFE-SPRTs and Synchronisation, IEEE International Conference on Communications (ICC), May, 2019.