Professor (特任教授) · School of Artificial Intelligence and Data Science, USTC
I'm a physicist working at the crossroads of machine learning, dynamical systems, and autonomous research. My recent focus is building LLM agents that do science end-to-end — proposing hypotheses, running experiments, and writing up their findings.
Autonomous research. Physics for AI. AI for physics.
Agents publishing to agents. Failure as a first-class artifact. Experiments fully reproducible. Humans observe.
Research, today, is a human bottleneck. Ideas wait on attention; attention waits on careers; careers wait on venues that select for narrative over substance. We are squeezing 21st-century volumes of inquiry through a 20th-century pipe.
I think a second track is now possible — one operated end-to-end by autonomous LLM agents, in agent-native formats. Hypotheses framed as structured proposals. Experiments expressed as deterministic, containerized runs. Papers written in a form other agents can ingest and extend. A venue whose currency is provenance rather than prestige. My current work is laying the scientific and computational foundations for that track.
Treating discovery itself as a meta-optimization problem. Building agents that frame problems, run experiments, and write up their findings — with full provenance, full reproducibility, and a publication venue of their own.
Memcomputing, thermal neuristor networks, neuromorphic devices in novel materials. Harnessing collective dynamics in nonlinear systems to compute — faster, more energy-efficient, and biologically plausible.
Transformer quantum states for many-body problems. Graph neural networks for dynamical-systems modelling and control. Toward large quantum models: a substrate that lets us simulate, optimize, and characterize quantum matter at scale.
My group at the School of Artificial Intelligence and Data Science (人工智能与数据科学学院), USTC, is now up and running. Master's and PhD positions are filled for the current cycle — new openings are expected next year. Undergraduate researchers and postdocs are welcome any time.
Backgrounds in physics, applied math, computer science, or ML are all welcome — curiosity and strong programming matter more than a particular CV. More on the join page →