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Education

Ph.D. in Chemistry & Ph.D. minor in Machine Learning, Carnegie Mellon University, Pittsburgh
- Aug. 2021 - Present
- Advisor: Dr. Newell R. Washburn
- Thesis (proposed): ML for Chemistry and Formulation: Theory, Modeling, and Automation
B.E. in Materials Science and Engineering, Peking University, Beijing
- Aug. 2016 - Jul. 2020
- Advisor: Dr. Qiang Sun
Global Exchange Program, University of Toronto, Toronto
- Jan. 2018 - May 2018

Experience

R&D Data Scientist, PhD Intern, Procter & Gamble, Mason, OH
- Jul. 2024 - Oct. 2024
- Modeling formulation data to accelerate product development with automated ML modeling.
- Modeling large consumer data to provide business insight with automated analysis and ML modeling.

Publications ( indicates equal contribution)*

Chemistry-Inspired Diffusion with Non-Differentiable Guidance

Shen, Y.*; Zhang, C.*; Fu, S.*; Zhou, C.; Washburn, N.; Póczos, B. Chemistry-Inspired Diffusion with Non-Differentiable Guidance. The Thirteenth International Conference on Learning Representations. ICLR, 2025. 🔗

Yttrium–Sodium Halides as Promising Solid-State Electrolytes with High Ionic Conductivity and Stability for Na-Ion Batteries

Qie, Y.; Wang, S.; Fu, S.; Xie, H.; Sun, Q.; Jena, P. Yttrium–Sodium Halides as Promising Solid-State Electrolytes with High Ionic Conductivity and Stability for Na-Ion Batteries. J. Phys. Chem. Lett. 2020, 11 (9), 3376–3383. 🔗