Our research is concerned with various aspects of decision making in complex process systems. We develop computational methods for the efficient solution of real-world optimization problems and the data-driven discovery of unknown decision processes. Areas of application include sustainable energy and process systems, advanced manufacturing, supply chain engineering, human-machine systems, and bioengineering.

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Education

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

Honors and Awards

• AIChE CAST Outstanding Young Researcher Award, 2024
• Guillermo E. Borja Career Development Award, 2024
• Junior Sargent Medal, 2024
• Hutchison Medal, 2024
• 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., McCann, M. G., Hu, W.-S., & Zhang, Q. (2024). Multi-cell-line learning for the data-driven construction of mechanistic metabolic models. Biotechnology and Bioengineering, 121, 2833-2847.
• Gupta, R. & Zhang, Q. (2024). Data-driven decision-focused surrogate modeling. AIChE Journal, 70(4), e18338.
• Rathi, T., Gupta, R., Pinto, J. M., & Zhang, Q. (2024). Enhancing explainability of stochastic programming solutions via scenario and recourse reduction. Optimization & Engineering, 25, 795-820.
• Wang, H., Daoutidis, P., & Zhang, Q. (2023). Ammonia-based green corridors for sustainable maritime transportation. Digital Chemical Engineering, 6, 100082.
• 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.