I am an Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) at the University of Michigan, with a courtesy appointment in the Department of Mathematics. I am also affiliated with the Michigan Institute for Computational Discovery and Engineering (MICDE) and the Michigan Institute for Data and AI in Society (MIDAS). Since summer 2026, I have served on the Editorial Advisory Board of APL Computational Physics.

Before joining the University of Michigan in 2026, I was a Visiting Research Scientist at the University of Washington in 2025. Prior to that, I worked at ByteDance Seed - AI for Science (formerly known as the AI Lab). During my Ph.D. studies, I completed two internships at Google Quantum AI. I received my Ph.D. in Applied Mathematics from UC Berkeley in 2023 and my B.S. from the University of Science and Technology of China.

My research brings ideas and tools from applied mathematics and learning theory to advance the algorithmic and engineering frontiers of quantum computing and quantum information. I currently focus on three directions:

  • Quantum algorithms for scientific computing, including numerical linear algebra and quantum chemistry applications
  • Efficient and robust methods for quantum parameter estimation, learning, and calibration
  • Statistical learning and generalization theory for quantum machine learning

Teaching

Current

Fall 2026: EECS 498 / ECE 598 - Quantum Signal Processing
Canvas | Syllabus | Lecture Notes

Acknowledgment: I gratefully acknowledge support from a U-M CRLT mini-grant for a pilot study in the Fall 2026 Quantum Signal Processing course on whether and how generative AI can help students learn quantum computing more effectively.

Upcoming

Winter 2027: EECS 216 - Signals and Systems

Full teaching record

Selected Papers

  1. Ground state preparation and energy estimation on early fault-tolerant quantum computers via quantum eigenvalue transformation of unitary matrices, PRX Quantum, 2022
  2. A Quantum Hamiltonian Simulation Benchmark, npj Quantum Information, 2022
  3. Optimal Low-Depth Quantum Signal-Processing Phase Estimation, Nature Communications, 2025
  4. In Situ Quantum Analog Pulse Characterization via Structured Signal Processing, preprint, 2025
  5. A Theory of Finite-Noise Optima and Generalization in Quantum Machine Learning, preprint, 2026

Full publication list

QSPPACK

I co-developed QSPPACK, a software package for Quantum Signal Processing that has received broad recognition within the community.

The package provides state-of-the-art solvers and tutorials for quantum signal processing workflows.

Opportunities

Ph.D. students. I welcome applications from students interested in quantum computing, applied mathematics, and learning theory. Students may apply through either the ECE Ph.D. program or the AIM Ph.D. program.

Current U-M students. Please contact me from your U-M email address with a brief description of your background, relevant preparation, and research interests, so that we can assess whether our research interests align and whether a further discussion would be useful.

Postdoctoral researchers and interns. I do not currently have postdoctoral openings. Internships must be arranged through an official U-M program and conducted in person on the Ann Arbor campus; remote internships are not available.