Learning near-optimal decisions: from SAA to robust optimization
Cristian, R., Perakis, G. (2022). Learning Robust Decisions for Contextual Stochastic Optimization Problems Directly from Data
CV
Education
- B.S. in GitHub, GitHub University, 2012
- M.S. in Jekyll, GitHub University, 2014
- Ph.D in Version Control Theory, GitHub University, 2018 (expected)
Work experience
- Summer 2015: Research Assistant
- Github University
- Duties included: Tagging issues
- Supervisor: Professor Git
- Fall 2015: Research Assistant
- Github University
- Duties included: Merging pull requests
- Supervisor: Professor Hub
Skills
- Skill 1
- Skill 2
- Sub-skill 2.1
- Sub-skill 2.2
- Sub-skill 2.3
- Skill 3
Publications
End-to-end learning via constraint-enforcing approximators for linear programs with applications to supply chains
Cristian, R., Harsha, P., Perakis, G., Quanz, B. L., & Spantidakis, I. (2022). End-to-End Learning via Constraint-Enforcing Approximators for Linear Programs with Applications to Supply Chains.
The role of optimization in some recent advances in data-driven decision-making
Baardman, L., Cristian, R., Perakis, G., Singhvi, D., Skali Lami, O., & Thayaparan, L. (2022). The role of optimization in some recent advances in data-driven decision-making. Mathematical Programming, 1-35.
Talks
Online semigroup queries on arrays and paths on trees
Seminar at MIT Operations Research Center Student Seminar, Cambridge, MA
End-to-end learning for decision optimization via constraint-enforcing approximators
Conference Presentation at 2022 INFORMS Annual Meeting, Indianapolis, IN
Learning near-optimal robust solutions in pricing and beyond
Conference Presentation at 2022 INFORMS Annual Meeting, Indianapolis, IN
Teaching
Service and leadership
- Currently signed in to 43 different slack teams