Biography 

Shengli (Bruce) Jiang is a postdoctoral associate at Princeton University (supervisor: Michael A. Webb), working on integrating molecular simulation and machine learning to design polymers with tailored properties. He received his Ph.D. in Chemical Engineering at the University of Wisconsin-Madison (advisor: Victor M. Zavala), where he developed new data representation methods. His doctoral research involved collaborations with 15 research groups on topics such as 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), where he contributed to scalable AutoML and uncertainty quantification. He was also an undergraduate researcher at the University of California, San Diego (advisor: Zheng Chen), working on organic electrode design in lithium-ion batteries. He is passionate about teaching, currently co-instructing a machine learning course at Princeton, and has mentored ten junior scholars. 

His research is at the interface of computational materials and process systems engineering, with a particular emphasis on data science. His research interests include data-centric modeling, characterization, and design of complex materials.

Curriculum Vitae (on faculty job market)

Email: sj0161@princeton.edu

Office: A326 EQuad, Princeton, NJ 08544

News

Very excited that our work with Satyen Dhamankar is featured on the cover of Molecular Systems Design & Engineering! He created some impressive visualizations in Blender. The paper is available here.

Phase separation in multicomponent mixtures is of significant interest in both fundamental research and technology. In this paper, we develop a machine-learning workflow that predicts phase coexistence with improved accuracy using physics-informed constraints.

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, 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. *Equal contribution.

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. *Equal contribution.

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