The seminar series that brings the academic community and industry together to talk about impactful applied research.
The University of Toronto’s MScAC program is located in one of the fastest-growing tech hubs in the world.
MScAC Talks is a yearly speaker series that bridges the academic and professional worlds, highlighting impactful applied research from September to April. The series invites industry and academic leaders to share work that inspires both the university and broader professional community, sparking discussions that deepen appreciation for research and its real-world impact.
By bringing together students, alumni and industry professionals, MScAC Talks fosters meaningful connections and knowledge exchange at the intersection of research and application.
Talk title
Do Androids Dream of Electric Sheep? A Generative Paradigm for Dataset Design
Abstract:
Traditional approaaches for autonomy and AI robotics typically focus either on large-scale data collection or on improving simulation. Although most practitioners rely on both approaches, they are still largely applied in separate workflows and viewed as conceptually unrelated. In this talk, I will argue that this is a false dichotomy. Recent advances in generative models have enabled the unification of these seemingly disparate methodologies. Using real-world data to build data generation systems has led to numerous advances with significant impact in robotics and autonomy, going beyond pure distillation approaches. Unifying creation and curation enables sophisticated automatic labeling pipelines and data-driven simulators. I will present some of our work following this paradigm and outline several basic research challenges and limitations associated with building systems that learn with generated data.
About Igor Gilitschenski
Igor Gilitschenski is an Assistant Professor of Computer Science at the University of Toronto where he leads the Toronto Intelligent Systems Lab. Previously, he was a (visiting) Research Scientist at the Toyota Research Institute. Dr. Gilitschenski was a Research Scientist at MIT’s Computer Science and Artificial Intelligence Lab and the Distributed Robotics Lab (DRL). There he was the technical lead of DRL’s autonomous driving research team. He joined MIT from the Autonomous Systems Lab of ETH Zurich where he worked on robotic perception, particularly localization and mapping. He obtained his doctorate in Computer Science from the Karlsruhe Institute of Technology and a Diploma in Mathematics from the University of Stuttgart. His research interests involve developing novel robotic perception and decision-making methods for challenging dynamic environments. His work has received multiple awards including best paper awards at the American Control Conference, the International Conference of Information Fusion, and the Robotics and Automation Letters.