Biography 

Shengli (Bruce) Jiang is a postdoctoral associate at Princeton University (supervisor: Michael A. Webb), focusing on integrating molecular simulation and machine learning for complex polymer design. He earned his Ph.D. from the University of Wisconsin-Madison (advisor: Victor M. Zavala), specializing in efficient and effective data representation methods. His doctoral research involved extensive collaboration with over 15 research groups on diverse chemical engineering challenges, including liquid crystal sensor design, mixed plastic waste classification, and chemical process monitoring. 

He interned at Dow Inc. (2022, supervisors: Ivan Castillo and Zhenyu Wang), focusing on AI applications in industrial settings, and at Argonne National Laboratory (2020, supervisor: Prasanna Balaprakash), working on scalable AutoML and uncertainty quantification. He was also an undergraduate researcher at University of California, San Diego (advisor: Zheng Chen), working on polymer-based battery design. He is passionate about teaching and has mentored ten junior scholars. His current research interests include data-centric modeling, characterization, and design of complex materials (e.g., polymers and liquid crystals).

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

Email: sj0161@princeton.edu

Office: A326 EQuad, Princeton, NJ 08544

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.

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