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.

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. Prospective applicants should consult the openings page for research directions and application instructions.

research highlights

• quantum networking — algorithms for the quantum internet:
  • entanglement routing and resource optimization (INFOCOM’25, INFOCOM’24),
  • quantum network tomography and evaluation (Mergecast Preprint, QCNC’26),
  • quantum-enhanced sequential learning (AAAI’25b).
• agentic networking — learning algorithms for networked AI agents:
  • communication-efficient cooperation across agents (SIGMETRICS’25, NeurIPS’25, ICLR’23, AISTATS’23),
  • heterogeneous and asymmetric cooperation (SIGMETRICS’26, AAAI’25a, UAI’25, UAI’23),
  • scalable agent grouping and shareable policies (Ski-Rental Preprint, ICML’22, IJCAI’22),
  • aligning LLM agents from interactive feedback (AAAI’26).
• foundations — online learning with realistic feedback:
  • robustness to strong-adversary manipulated feedback (ICLR’25, L4DC’25),
  • fusing absolute and relative human feedback (ICML’25),
  • multi-fidelity feedback for sample-efficient learning (NeurIPS’23).