I am a Research Scientist at Huawei Noah’s Ark Lab (London Research Center). My research focuses on AI for materials science, with an emphasis on developing algorithms for crystallography and spectroscopy using graph representation learning and Bayesian optimization.

My research interest in this area has been shaped through a combination of interdisciplinary training and research experience across academia and industry. I received my Ph.D. from The Hong Kong University of Science and Technology (Guangzhou Campus), where I worked with Prof. Tong-Yi Zhang. During my Ph.D., I was also a visiting student at City University of Hong Kong, working with Prof. Yang Ren, and gained industry research experience through internships at Shanghai AI Lab and GreenDynamics.

Prior to my Ph.D., I obtained an MPhil in Mechanics from Shanghai University under the supervision of Prof. Tong-Yi Zhang, where I worked on transfer learning for materials science during an internship at Zhejiang Laboratory. I received my BEng in Chemical Machinery from Beijing University of Chemical Technology, with a focus on finite element methods and chemistry.

I have developed a series of machine learning methods for key problems in X-ray diffraction (XRD) and crystal analysis, including large-scale XRD pattern simulation (SimXRD), crystal structure determination (XQueryer), XRD pattern refinement (WPEM), and crystal property prediction (PRDNet). In addition, I developed one of the first Bayesian optimization frameworks for materials discovery, Bgolearn, which has been selected for support under the Open Source Project Program of the Shanghai Municipal Commission of Economy and Informatization.

Outside of research, I enjoy jogging and going to the gym.

I am a strong proponent of open science, advocating for transparency, reproducibility, and community collaboration in computational research.

Due to the large number of messages I receive, I kindly ask that you first take a moment to clarify your questions and review the repository README before reaching out.

As many common questions (e.g., debugging, file paths, resource allocation, or general usage) are already addressed in the documentation, I may be unable to reply to individual emails on these topics. For quicker and more effective support, opening a GitHub Issue is highly recommended.

To help me respond appropriately, please include your name, affiliation, and the purpose of your message. Messages without this information may not receive a reply. For students interested in collaboration, please obtain prior approval from your supervisor and ensure that your supervisor is CC’ed on any correspondence. Thank you very much for your understanding and interest.

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