Victor Zavala has been named by the journal Industrial & Engineering Chemistry Research to its 2018 Class of Influential Researchers. The class members, who are in the early stages of their career, are selected on the basis of the quality and impact of their research.
Zavala is a computer scientist in the Mathematics and Computer Science (MCS) division at Argonne National Laboratory and Baldovin-DaPra Associate Professor in the College of Engineering at the University of Wisconsin-Madison. He received an NSF CAREER Award 2018 and a DOE Early Career Award in 2014. His research focuses on the development of mathematical optimization models for optimal control, estimation and system design, as well as the development of scalable computational techniques to solve such models on high-performance computers.
Zavala and his colleagues also contributed to the inaugural virtual special issue of invited research articles from this 2018 Class of Influential Researchers. In their article titled “Scalable solution strategies for chance-constrained nonlinear programs” they analyzed different strategies to solve chance-constrained optimization problems – those in which one is interested in controlling the risk of an event from occurring.
Zavala and his colleagues demonstrated that these challenging problems can be handled in a scalable manner using smooth approximations and moment-matching approaches.
“I am honored to have been selected to join this class of researchers who are seeking to break new ground in solving challenging engineering problems. By devising new optimization methods and applying them to different systems, I hope to help decision-makers better manage conflicting objectives,” Zavala said.
For more information about the 2018 Class of Influential Researchers, see the website: https://pubs.acs.org/doi/10.1021/acs.iecr.8b04315 .
The paper by Zavala et al. is available on the web: Scalable Solution Strategies for Chance-Constrained Nonlinear Programs, Javier Tovar-Facio, Yankai Cao, Jose M. Ponce-Ortega, and Victor M. Zavala, Ind. Eng. Chem. Res., 2018, 57 (23), pp. 7987–7998. DOI: 10.1021/acs.iecr.8b00646.