Xuchuang Wang

My research builds the algorithmic foundations of two emerging networks:

• quantum networking — networks that distribute qubits and entanglement across distant nodes to enable distributed quantum computing, quantum-enhanced sensing, and provably-secure communication;
• agentic networking — networks of AI/LLM agents that communicate, coordinate, and learn together to act in the world.

A single methodology runs through both: sequential decision-making under uncertainty (online learning, bandits, and reinforcement learning). I use it to design rigorous, communication-efficient, and provably-good algorithms that hold up under realistic, noisy, and even adversarial feedback.

I am an Assistant Professor in the Department of Computer Science at Hong Kong Baptist University. Before joining HKBU, I was a RGC Junior Research Fellow in the Department of Computer Science and Engineering at The Chinese University of Hong Kong. My work has received the Best Paper Award at IEEE Quantum Week (QCE) 2026 and the Best Paper Runner-Up at ACM SIGMETRICS 2025.

Prior to that, I was a postdoctoral researcher for three years in the Manning College of Information & Computer Science at University of Massachusetts Amherst, working with Don Towsley at ACQUIRE Lab and Mohammad Hajiesmaili at SOLAR Lab. I received my Ph.D. in the Department of Computer Science & Engineering at The Chinese University of Hong Kong under the guidance of John C.S. Lui at ANSR Lab. I obtained my B.Eng. with Hons. (now Qian Xuesen Honors College) from Xi’an Jiaotong University.

joining the lab

I am building a research group at HKBU CS at the intersection of learning theory and the two networks described above. Representative questions include:

• routing entanglement through a noisy quantum network, and verifying its health from limited measurements;
• enabling a population of LLM agents to share what they learn while keeping communication overhead low;
• establishing provable guarantees when feedback is adversarial, multi-modal, or quantum.

Students in my group publish at leading venues (NeurIPS, ICML, ICLR, AAAI, SIGMETRICS, INFOCOM) and collaborate with labs at UMass, CUHK, CityU, NJU, and SJTU; recent mentees and their first-author papers appear on the team page.

Beyond Ph.D. positions, I host Research Assistants (remote or onsite at HKBU) on 3- or 6-month terms — suitable for senior undergraduates, master’s students, or recent graduates who wish to gain research experience before applying to a Ph.D. program, or to develop a concrete project toward publication.

Ideal background: strong fundamentals in probability, optimization, or theoretical computer science. Besides computer science, students with a mathematics or physics background are also highly welcome to apply. Prospective applicants should consult the openings page for research directions and application instructions.

research highlights