Home

Education

• Ph.D. in Chemistry & Ph.D. minor in Machine Learning, Carnegie Mellon University, Pittsburgh
  • Aug. 2021 - Present (expected August 2025)
  • Advisor: Dr. Newell R. Washburn
  • Thesis (proposed): ML for Chemistry and Formulation: Theory, Modeling, and Autonomous Research
• 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)

(in preparation) Machine Learning for Predicting the Viscosity of Complex Fluids and Aiding Formulation Design

In preparation, August 2025

Fu, S.; Bruno, C.; Chua, A.; Póczos, B.; Washburn, N. Machine Learning for Predicting the Viscosity of Complex Fluids and Aiding Formulation Design. In preparation, 2025.

(manuscript under review) Autonomous Determination of Hansen Solubility Parameters via Active Learning

Manuscript under review, August 2025

Fu, S.; Wang, D.; Henderiks, H.; Assis, A.; Charron, J.; Washburn, N. Autonomous Determination of Hansen Solubility Parameters via Active Learning. Manuscript under review, 2025.

(manuscript under review) Predictive Modeling of Emulsion Stability via Hierarchical Machine Learning

Manuscript under review, July 2025

Roguski, M.*; Fu, S.*; Walker, L.; Tilton, R.; Washburn, N.; Cochran, B.; Stone, C.; Barker, M.; Jamadagni, S.; Johnson, E. Predictive Modeling of Emulsion Stability via Hierarchical Machine Learning. Manuscript under review, 2025.

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. 🔗

Presentations and Posters

Closed-loop and Autonomous: CMU Cloud Lab for Measuring Hansen Solubility Parameters

June 27, 2024

Talk at Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania

Formulation AI for the Guided Viscosity Design of Complex Fluids

February 12, 2024

Poster at Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania

Molecular Representation for Property Prediction: Wanders in the Chemical Space

December 2, 2022

Talk at Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania

Selected Projects

ML-Based Sustainable Development of Formulation for Stable Emulsions

October 2023

Autonomous Determination of Hansen Solubility Parameters (HSPs) in an Automated Lab

July 2023

Machine Learning for Predicting the Viscosity of Complex Fluids and Aiding Formulation Design

May 2023

Professional Skills

• Programming languages: Python (most experienced with), JavaScript; [basics] C/C++, Mathematica, SQL.
• Libraries and tools: [ML] PyTorch, Scikit-learn; [computational chemistry] xTB, ORCA, Gaussian16.
• Web/software development: Nginx, Flask, Streamlit, React (basics). Full-stack example: EQUUS web app.