Shengli (Bruce) Jiang

Postdoctoral Associate

Princeton University

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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 Zhenyu Wang) and Argonne National Laboratory (2019; worked with Prasanna Balaprakash). He was an undergraduate researcher at Zheng Chen Lab at the University of California, San Diego. His research interests include molecular modeling, machine learning, and their applications to materials design (polymers and proteins). He is a contributor to automated machine learning (AutoML) package DeepHyper.

Curriculum Vitae (On Tenure-Track Job Market 24-25)

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, A. B. Dieng, and M. A. Webb. Property-Guided Generation of Complex Polymer Topologies Using Variational Autoencoders. npj Computational Materials, 2024, 10, 139.

S. Jiang, S. Qin, R. C. Van Lehn, P. Balaprakash, and V. M. Zavala. Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search. Digital Discovery (accepted), 2024.

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