Education
- Mechanical Engineering B.S., Middle East Technical University, Ankara, Turkey
Bio
Andrew Cinar is currently a Ph.D candidate interested in optimization, game theory, optimal control, dynamic simulation, and robotics. His current research focuses on using bilevel optimization for intelligent autonomous decision-making in multi-agent systems, such as trajectory planning in autonomous racing.
Some of his recent contributions include, “Does bilevel optimization result in more competitive racing behavior?” (2025), where it is demonstrated incorporating different game-theoretic solution concepts can yield more strategic and competitive behaviors in simulated racing scenarios. Furthermore, in “Polyhedral Collision Detection via Vertex Enumeration” (2025), a novel mathematical programming approach for collision detection in robotics is presented where vertex enumeration within polyhedral sets is leveraged to determine polyhedral collision detection. Previously, he also worked on Open-Source Quadruped Trajectory Optimization Stack (2023), a practical end-to-end framework for quadruped robots with state-of-the-art trajectory generation and optimization tools.
