Chong Sun

Chong is an Assistant Professor in the Department of Chemistry and Chemical Biology. She received her B.A. from Peking University, under the supervision of Profs. Hong Jiang and Wenjian Liu, where she studied spin-crossover materials using density functional theory (DFT) and Monte Carlo simulations. She completed her Ph.D. at Caltech under Prof. Garnet Chan, where she developed classical and quantum algorithms for strongly correlated electrons, including finite-temperature density matrix embedding theory (FT-DMET) and the quantum imaginary time evolution (QITE) algorithm. After her Ph.D., Chong was a postdoctoral researcher with Prof. Alán Aspuru-Guzik at the University of Toronto, where she developed machine learning models for chemical systems, including the neural network quantum state (NNQS) Waveflow and the autoregressive molecular generation model Quetzal. She later worked with Prof. Gustavo Scuseria at Rice University on traditional quantum chemistry methods, where she developed a framework for selected non-orthogonal configuration interaction with single and double excitations (SNOCISD). Chong also has industrial research experience as a scientist at Zapata AI Inc. and Microsoft, where she worked on quantum computing solutions to chemical problems.