Research Interests

Our research in the area of process systems engineering lies at the intersection of chemical engineering and operations research, focusing on computational discovery and decision making in complex process systems. We develop mathematical models and algorithms capable of considering intricate decision processes, incorporating uncertainty and active learning, capturing interactions between multiple agents, and solving large-scale real-world optimization problems.

Our work is highly quantitative and interdisciplinary. We apply our tools to solve a wide range of problems in the design and operation of sustainable energy and process systems, advanced manufacturing, supply chain optimization, data analytics, and systems biology.


  • McKnight Land-Grant Professorship, 2023
  • NSF CAREER Award, 2021
  • W. David Smith Jr. Graduate Publication Award, 2019
  • Mark Dennis Karl Teaching Assistant Award, 2014 & 2016
  • Geoffrey Hewitt Prize, 2012
  • Dr. Jürgen Ulderup Fellowship, 2011
  • DAAD ISAP Fellowship, 2009

Selected Publications

  • Lu, Y.-A., O’Brien, C. M., Mashek, D. G., Hu, W.-S., & Zhang, Q. (2023). Kinetic-model-based pathway optimization with application to reverse glycolysis in mammalian cells. Biotechnology & Bioengineering, 120, 216-229.
  • Rathi, T. & Zhang, Q. (2022). Capacity planning with uncertain endogenous technology learning. Computers & Chemical Engineering, 164, 107868.
  • Gupta, R. & Zhang, Q. (2022). Decomposition and adaptive sampling for data-driven inverse linear optimization. INFORMS Journal on Computing, 34(5), 2720-2735.
  • Allman, A. & Zhang, Q. (2022). Distributed fairness-guided optimization for coordinated demand response in multi-stakeholder process networks. Computers & Chemical Engineering, 161, 107777.
  • Wang, H., Daoutidis, P., & Zhang, Q. (2021). Harnessing the wind power of the ocean with green offshore ammonia. ACS Sustainable Chemistry & Engineering, 9, 14605-14617.
  • Zhang, Q. & Feng, W. (2020). A unified framework for adjustable robust optimization with endogenous uncertainty. AIChE Journal, 66(12), e17047.
  • Allman, A. & Zhang, Q. (2020). Dynamic location of modular manufacturing facilities with relocation of individual modules. European Journal of Operational Research, 286, 494-507.
Qi Zhang in Lab

Email: qizh@umn.edu

Phone: 612/625-0014

Office: 245C Amundson Hall

Group Website

Support Qi Zhang's Research

  • B.S., Mechanical Engineering, RWTH Aachen University, 2011
  • M.S., Chemical Engineering, Imperial College London, 2012
  • Ph.D., Chemical Engineering, Carnegie Mellon University, 2016