Biography
Shengli (Bruce) Jiang is a postdoctoral associate at the Chemical and Biological Engineering Department at Princeton University (supervisor: Michael A. Webb). He received his Ph.D. from the University of Wisconsin-Madison (advisor: Victor M. Zavala). He was a summer intern at Dow Inc. (2022; worked with Ivan Castillo) and Argonne National Laboratory (2019; worked with Prasanna Balaprakash). He was an undergraduate researcher at Zheng Chen Lab at University of California, San Diego. His research interests include molecular modeling, machine learning, and their applications to materials design (such as polymers and proteins). He is a contributor to automated machine learning (AutoML) package DeepHyper.
Email: sj0161@princeton.edu
Office: A326 EQuad, Princeton, NJ 08544
Research Interests
Molecular Modeling
Machine Learning
Materials Design
Education
PhD in Chemical Engineering, 2023
University of Wisconsin-Madison
BSc in Chemical Engineering, 2018
University of California, San Diego
Selected Publications
S. Jiang, N. Bao, A. D. Smith, S. Byndoor, M. Mavrikakis, R. C. Van Lehn, N. L. Abbott and V. M. Zavala. Scalable Extraction of Information from Spatio-Temporal Patterns of Chemoresponsive Liquid Crystals Using Topological Data Analysis. Journal of Physical Chemistry C, 2023, 127, 32, 16081–16098.
N. Bao, S. Jiang, A. D. Smith, J. J. Schauer, M. Mavrikakis, R. C. Van Lehn, V. M. Zavala, and N. L. Abbott. Sensing Gas Mixtures by Analyzing the Spatiotemporal Optical Responses of Liquid Crystals using 3D Convolutional Neural Networks. ACS Sensors, 2022, 7, 9, 2545-2555.
S. Qin, S. Jiang, J. Li, P. Balaprakash, R. C. Van Lehn, and V. M. Zavala. Capturing Molecular Interactions in Graph Neural Networks: A Case Study in Multi-Component Phase Equilibrium. Digital Discovery, 2023, 2, 138.
S. Zinchik, S. Jiang, S. Friis, F. Long, L. Høgstedt, V. M. Zavala, and E. Bar-Ziv. Accurate Characterization of Mixed Plastic Waste using Machine Learning and Fast Infrared Spectroscopy. ACS Sustainable Chemistry & Engineering, 2021, 9, 42, 14143-14151.
S. Jiang, J. Noh, C. Park, A. D. Smith, N. L. Abbott, and V. M. Zavala. Endotoxin Detection Using Liquid Crystal Droplets and Machine Learning. Analyst, 2021, 146, 1224-1233.
A. K. Chew, S. Jiang, W. Zhang, V. M. Zavala, and R. C. Van Lehn. Fast Predictions of Liquid- Phase Acid-Catalyzed Reaction Rates Using Molecular Dynamics Simulations and Convolutional Neural Networks. Chemical Science, 2020, 11, 12464-12476.
Selected Presentations
Data Representations and Transformations in Chemical Engineering
Thesis Defense
May 1, 2023 ⋅ Madison, WI
Real-Time Characterization of Mixed Plastic Waste Using Machine Learning and Infrared Spectroscopy
FOCAPO/CPC 2023
Jan 8 - Jan 12, 2023 ⋅ San Antonio, TX
Characterization of Chemoresponsive Liquid Crystals Using Topological Descriptors and Machine Learning
AIChE Annual Meeting 2022
Nov 13 - Nov 18, 2022 ⋅ Phoenix, AZ
Molecular Property Uncertainty Quantification Using Automatic Graph Neural Architecture Search
AIChE Annual Meeting 2022
Nov 13 - Nov 18, 2022 ⋅ Phoenix, AZ
Rapid and Real-Time Mixed-Plastic Waste Analysis Using Infrared Spectroscopy and Machine Learning
AIChE Annual Meeting 2021
Nov 7 - Nov 11, 2022 ⋅ Boston, MA