As a postdoctoral researcher at Rice University, I’m always on a journey of discovery. My current research focuses on developing intelligent wireless networks and networked systems using machine learning, which hopefully can also make wireless research more fun and transferable. I have worked on a range of topics, including autonomous networking, AI radio, cloud radio access networks, dynamic spectrum access, and vehicular communications. Check out the highlights and list of publications to learn more, and feel free to reach out through GitHub, email, or LinkedIn.

Throughout my academic career, I’ve had the privilege of working with esteemed advisors including Santiago Segarra (19-present), Mehmet C. Vuran (13-19), Zishu He (06-09), and Zhuming Chen (04-06). Their mentorship has helped me become the researcher I am today, and I’m always grateful.

Curriculum vitae

Current and recent research topics

• Automomous networking (ICLR’23, IEEE TWC (arXiv), ICASSP’24,23,22a, 22b, 21, CAMSAP’23) – focus on graph (network)-based machine learning for distributed decisions & discrete problems
• AI radio: a neural OFDM receiver (IEEE JSAC, arXiv, git, post)
• Cloud radio access networks (j.adhoc, post)
• Dynamic spectrum access (IEEE TVT, post; M&SOM, manuscript)
• Vehicular communications (j.comcom, post)

News & Blogs

• 2024-03-25 "Exploring the Opportunities and Challenges of Graph Neural Networks in Electrical Engineering" accepted to Nature Reviews Electrical Engineering.
• 2024-03-20 "Biased Backpressure Routing using Link Features and Graph Neural Networks" submitted to IEEE TWC.
• 2024-02-28 "Distributed Link Sparsification for Scalable Scheduling using Graph Neural Networks" submitted to IEEE TWC.
• 2023-12-13 "Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks" accepted to IEEE ICASSP 2024.
• 2023-09-26 "Enhanced Backpressure Routing with Wireless Link Features" accepted to IEEE CAMSAP 2023.
• 2023-09-13 "Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks" submitted to IEEE ICASSP 2024.
• 2023-02-19 "Delay-aware Backpressure Routing Using Graph Neural Networks" accepted to IEEE ICASSP 2023.
• 2023-02-18 Self-organizing Wireless Networks, Part 2: Distributed Link Scheduling
• 2023-02-15 Self-organizing Wireless Networks, Part 1: An Introduction
• 2023-02-13 Explore the Pros and Cons of Barefoot Shoes with ChatGPT

more…

ChatGPT explains machine learning for wireless systems using Tom Scott’s style.

Machine learning has become a crucial player in wireless communications and networking in recent years. Essentially, it helps these systems make predictions and decisions more efficiently, leading to better performance, lower costs, and improved user experiences.

Imagine you’re a wireless network trying to serve a large crowd at a concert. With traditional methods, you might struggle to manage all the devices and data flying around, leading to slow connections, dropped calls, and unhappy customers.

Enter machine learning! By using algorithms that can learn from past data and experiences, the network can better understand how to allocate its resources, prioritize different types of traffic, and optimize signal quality. It’s like having a smart, data-driven traffic cop helping manage the crowd and ensure everyone has a good time.

And the cool thing is, machine learning can keep learning and adapting, meaning it can keep improving over time as it handles more data and encounters new challenges. So, in a nutshell, machine learning helps wireless networks make smarter decisions and provide a better experience for users.

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When not immersed in research, I enjoy reading, finance, and sports. I especially appreciate the Olympic Motto, “Citius, Altius, Fortius,” which means “faster, higher, braver.” I believe it encapsulates the spirit of always striving for improvement and pushing ourselves to be our best.

Thanks for visiting!