Shengli (Bruce) Jiang
Postdoctoral Associate
Lecturer (Part-Time)
Princeton University
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.
Email: sj0161@princeton.edu
Office: A326 EQuad, Princeton, NJ 08544
Research Interests
Machine Learning
Molecular Modeling
Materials Design
Education
PhD in Chemical Engineering, 2023
University of Wisconsin-Madison
BSc in Chemical Engineering, 2018
University of California, San Diego
News
I will be giving three presentations at the upcoming AIChE Annual Meeting. If you are attending, feel free to stop by!
Data-Centric Modeling, Design, and Synthesis of Complex Materials (4ha).
Meet the Faculty and Post-Doc Candidates Poster Session.
October 27, 2024, 3:30–5:00 PM. San Diego Convention Center, Exhibit Hall GH.Physics-Guided Machine Learning for Transferable Prediction of Polymer Properties (62a).
Faculty Candidates in CoMSEF/Area 1a, Session 1.
October 28, 2024, 8:00–8:10 AM. Hilton San Diego Bayfront Hotel, Sapphire 411 (AB).Property-Guided Design of Topologically Complex Polymers Using Generative Modeling (394i).
Machine Learning for Soft and Hard Materials.
October 29, 2024, 5:06–5:18 PM. Hilton San Diego Bayfront Hotel, Sapphire Ballroom D.
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, 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