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About

Ankit Dhall is a leading researcher in autonomous systems, with a primary focus on robust perception and high-speed autonomous navigation. His work addresses the critical challenge of enabling machines to understand complex, dynamic environments under adverse conditions. Dhall’s seminal contribution, **AdapNet**, is a pioneering framework for adaptive semantic segmentation that maintains reliable scene understanding despite drastic changes in weather, lighting, and seasons—a fundamental requirement for safe autonomous driving. This highly influential work has garnered **197 citations**, underscoring its impact on the field of computer vision and robotics. Beyond perception, Dhall made a notable mark on autonomous racing. As a key contributor to the **AMZ Driverless** project, he helped develop the full-stack software architecture for a racecar capable of navigating an unknown track at the limits of handling. This work, which integrates perception, planning, and control, demonstrates the practical application of his research in one of the most demanding real-world scenarios. Dhall’s contributions bridge the gap between robust visual intelligence and high-performance autonomous control.