Danial Davarnia

I am an Associate Professor in the Edwardson School of Industrial Engineering at Purdue University. Prior to this position, I was an Associate Professor in the Industrial and Manufacturing Systems Engineering Department at Iowa State University. I was a postdoctoral fellow in the Tepper School of Business at Carnegie Mellon University from 2016 to 2018 and worked with Gérard Cornuéjols, John Hooker, and Willem-Jan van Hoeve. I received my PhD in Industrial Engineering from University of Florida under supervision of Jean-Philippe Richard and Mohit Tawarmalani. I have been the recipient of the National Science Foundation CAREER Award, the Air Force Office of Scientific Research Young Investigator Program Award, and the INFORMS Computing Society Harvey J. Greenberg Research Award, among others.

Education

PhD, Industrial and Systems Engineering, University of Florida, 2016
MS, Industrial Engineering, Sharif University of Technology, 2011
BS, Industrial Engineering, Sharif University of Technology, 2009

Research Interests

Deterministic Optimization:
- Convexification techniques and solution methods for mixed-integer nonlinear programs
- Global Optimization methods via decision diagrams
- Computationally efficient parallelization frameworks for mixed-integer programs
- Network optimization

Stochastic Optimization:
- Parameter estimation in stochastic optimization under frequentist and Bayesian settings
- Interconnections between machine learning, function approximation, and optimization

Applications:
- Bilevel optimization: Network interdiction
- Transportation and logistics: Unit train scheduling; Unsplittable network flow
- Energy: Unit commitment; Optimal power flow
- Finance: Portfolio optimization

In the News (ISU)

Contact Information

Email: ddavarn@purdue.edu

Phone: (765) 494-3052

Office:

386 Grissom Hall

315 N. Grant Street

West Lafayette, IN 47907-2023

Open positions: I am looking for motivated PhD students and postdoctoral scholars interested in the theory and applications of Operations Research with strong analytical background in relevant fields. Candidates with backgrounds in applied mathematics, computer science, and related disciplines, as well as those with prominent computer programming skills are particularly encouraged to reach out to me.