I am a postdoc in the ECE department at Rice University. Generally, I am interested in systems and my research covers wireless communications, signal processing, and machine learning (artifical intelligence). Here are a few topics I recently worked on:
- automomous networking with deep learning on graphs (networks) (arXiv, ICASSP)
- radio frequency machine learning (IEEE JSAC, arXiv, git, post)
- cloud radio access networks (j.adhoc, post)
- dynamic spectrum access (IEEE TVT, post)
- vehicular communications (j.comcom, post)
My current and past work seeks to make wireless communications more alive and funin contrast to merely a cathedral of (dead) standards and protocols. That’s important for the products as well as talents.. I am also interested in deep learning itself and its applications beyond radio.
My current advisor is Santiago Segarra, and I finished my Ph.D. program under the advise of Mehmet C. Vuran (13-19). A few years back, I was advised by Zishu He in a research-based master program (06-09), and by Zhuming Chen in China’s national undergraduate electronic design contest (2005), in which my team was awarded the national 1st-class prize.
- 2021-10-07 "Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks" submitted to ICASSP 2022.
- 2021-10-06 "Delay-Oriented Distributed Scheduling Using Graph Neural Networks" submitted to ICASSP 2022.
- 2021-08-05 Our manuscript entitled "Link Scheduling Using Graph Neural Networks" is submitted to IEEE Journal on Selected Topics in Signal Processing.
- 2021-06-08 "Distributed Scheduling using Graph Neural Networks" is presented at IEEE ICASSP 2021
- 2021-04-22 Our work "Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks" is accepted to IEEE Journal on Selected Areas in Communications.
- 2021-02-07 I recommend this lecture from Stanford SNAP: "Machine Learning with Graphs"
- 2021-01-30 Our paper "Distributed Scheduling using Graph Neural Networks" is accepted by IEEE ICASSP 2021
- 2021-01-03 Our manuscript "Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks" is revised and submitted to IEEE JSAC
- 2020-10-29 Our paper "A City-Wide Experimental Testbed for The Next Generation Wireless Networks" is published on Journal of Ad Hoc Networks
- 2020-07-27 Nebraska Experimental Testbed of Things, known as NEXTT, named finalist for NSF program to expand rural broadband
A little bit more about me
I like finance, sports, and reading.