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 a RGC Junior Research Fellow in the Department of Computer Science and Engineering at Chinese University of Hong Kong and an incoming Assistant Professor in the Department of Computer Science at Hong Kong Baptist University.

Prior to this, 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 group at HKBU CS that works at the intersection of learning theory and the two networks of the next decade. If you are excited to work on questions like —

• How do we route entanglement through a noisy quantum network, and how do we even know the network is healthy?
• How should a population of LLM agents share what they learn without drowning each other in messages?
• What do provable guarantees look like when feedback is adversarial, multi-modal, or quantum?

— then we should talk. Students will publish at top venues (NeurIPS, ICML, ICLR, AAAI, SIGMETRICS, INFOCOM), collaborate with a network of labs at UMass, CUHK, CityU, NJU, and SJTU, and work on problems that matter both mathematically and practically. See recent mentees and their first-author papers for a sense of what is possible.

Beyond PhD positions, I also host Research Assistants (remote or onsite at HKBU) on 3- or 6-month terms — a great fit for senior undergraduates, master’s students, or recent graduates who want to try research before committing to a PhD, or who already have a concrete project they want to push to publication.

I am actively recruiting Ph.D. students for Fall 2026 / Spring 2027 and short-term Research Assistants (remote or onsite, 3 or 6 months) — please reach out!

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).