Yigit Korkmaz

Papers

Total Citations

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About

Yigit Korkmaz is a rising researcher in artificial intelligence and machine learning, with a primary focus on imitation learning, reinforcement learning, and real-world control systems. His most notable contribution is the development of MILE (Model-Based Intervention Learning), a novel framework introduced in his 2025 paper that addresses critical limitations in imitation learning. Specifically, MILE tackles the compounding error problem and reduces reliance on human experts providing complete trajectories, making it highly applicable to robotics and autonomous systems. Although early in his career, Korkmaz’s work has already garnered attention, with his flagship paper accumulating 2 citations and sparking interest in more efficient, interactive learning paradigms. His research bridges the gap between theoretical model-based approaches and practical deployment, offering a path toward safer and more adaptable AI agents. As a forward-thinking scholar, Korkmaz continues to explore how machines can learn from limited human guidance, positioning himself as a promising voice in the next generation of AI researchers.