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Shuaijun Li (IntlAssoc)

Postdoctoral Associate

Polymers and Complex Fluids Group

shuaijun.li [at] nist.gov

(301) 975-4769

Staff Employment

NIST PREP Postdoctoral Associate (Materials Science and Engineering Division, 2023 –

Staff Education

Ph.D. Mechanical Engineering, City College of New York (2022)

M.S. Mechanical Engineering, City College of New York (2021)

M.S. Geotechnical Engineering, University of Chinese Academy of Sciences (2016)

B.S. Mining Engineering, China University of Mining and Technology (2013)

I received my Ph.D. in Mechanical Engineering from the City College of New York, and I'm currently a research associate at the National Institute of Standards and Technology (NIST). My research focuses on applying machine learning to predict the physical properties of polyolefins, with the goal of advancing near-infrared (NIR)-based polyolefin differentiation. This work directly supports improvements in plastic sorting and recycling, contributing to circular economy efforts. Building on my mechanical engineering background, I have gained extensive expertise in machine learning, large-scale data analysis, dimensionality reduction, predictive modeling, and polymer characterization, significantly expanding my technical skill set.

Current Research Interests:

Enhancing polyolefin classification through near-infrared spectroscopy and machine learning
Developing algorithms to correlate multimodal measurements using machine learning
Analyzing and modeling large datasets for advanced material characterization

Selected Publications:

S. Li, H. Yu, and J. Fan. Modeling Transport of soft particles in porous media, Physical Review E 104, 025112, 2021.

S. Li, H. Yu, T.-D. Li, Z. Chen, W. Deng, A. Anbari, and J. Fan. Understanding transport of an elastic, spherical particle through a confining channel, Applied Physics Letters 116, 103705, 2020.

J. Fan, S. Li, Z. Wu, and Z. Chen. Diffusion and mixing in microfluidic devices, in Microfluidics for Pharmaceutical Applications: From Nano/Micro Systems Fabrication to Controlled Drug Delivery, Elsevier, 2019.