Zheyu Jiang

General Information

Name Zheyu Jiang
Affiliation School of Chemical Engineering, Oklahoma State University

Education

  • 2018
    Ph.D., Chemical Engineering
    Purdue University, West Lafayette, Indiana, USA
  • 2014
    B.Ch.E. (Honors), Chemical Engineering
    University of Minnesota, Twin Cities, Minnesota, USA

Professional Experience

  • 2021 - Present
    Assistant Professor
    School of Chemical Engineering, Oklahoma State University, Stillwater, OK, USA
  • 2019 - 2021
    Process Development Engineer
    Small Molecule Discovery & Development Group, Corteva Agriscience, Indianapolis, IN, USA
  • 2018 - 2019
    Process Development Engineer
    Active Ingredient Process Development Group, Dow Chemical Company, Midland, MI, USA
  • 2016 - 2016
    PhD R&D Intern
    Engineering and Process Sciences, Core R&D, Dow Chemical Company, Midland, MI, USA
  • 2013 - 2013
    Engineering Support Specialist
    Simulation & Tool Development Skill Center, Honeywell UOP, Des Plaines, IL, USA

Honors and Awards

  • 2021 - Present
    • 2025 National Science Foundation CAREER Award
    • 2023 FOPAM 2023 Travel Award for Junior Faculty
    • 2022 Early-career pioneering research featured in 2022 Futures Issue of AIChE Journal
  • 2014 - 2020
    • 2020 People's Choice Award, Corteva Agriscience
    • 2019 People's Choice Award, Corteva Agriscience
    • 2018 Separations Division Graduate Student Research Award, AIChE
    • 2017 Eastman Graduate Travel Grant, Purdue University
    • 2016 Purdue Graduate Student Government Travel Grant, Purdue University
  • 2010 - 2014
    • 2010 - 2014 Global Excellence Scholarship, University of Minnesota
    • 2012 College of Science and Engineering Merit Scholarship, University of Minnesota
    • 2012 Charles A. Mann Award, Department of Chemical Engineering and Material Science, University of Minnesota

Research Areas

  • Industrial Decarbonization
  • Digital Agriculture
  • Sustainability and Food-Energy-Water Nexus

Group Expertise

  • Multi-scale modeling: first-principle based mathematical modeling, neural ODE/PDE solvers, physics-informed machine learning
  • AI for science: digital twin solutions, efficient and scalable algorithm development for inverse problem
  • Explainable AI: new ML architectures with enhanced interpretability/explainability and favorable mathematical properties
  • Optimization: deterministic/stochastic optimization, robust optimization, Bayesian optimization, global optimization
  • Control: Statistical process control, optimal control, (safety-constrained) reinforcement learning, multi-agent systems