Igor Gilitschenski

Igor Gilitschenski is an Assistant Professor in Computer Science at the University of Toronto, leading the Toronto Intelligent Systems Lab (TISL). He focuses on developing probabilistic and learning-based techniques for robotic perception and decision-making, aiming to enable robust interactive autonomy. Prior to this, he held roles at MIT CSAIL, ETH Zurich's Autonomous Systems Lab, and the Karlsruhe Institute of Technology (KIT). His research interests include autonomous systems, computer vision, and deep learning applied to robotics. He collaborates with institutions like the Vector Institute and is a Vector Research Scholar.

His work spans robot learning, simulation-driven policy training, and safety-critical systems. Notable projects include pseudo-simulation for autonomous driving, vision-language-action models, and neural radiance fields for 3D scene representation. He actively recruits PhD students to work at the intersection of computer vision, deep learning, and robotics, emphasizing controllable simulation engines and safe learning frameworks.

Recent research trends in his articles highlight advancements in generative models, reinforcement learning, and perception systems for dynamic environments. He emphasizes collaboration across disciplines, including event-based vision and language-guided reasoning for robotic tasks.