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

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.

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