Khadija Shaheen

National University of Sciences and Technology

Papers

Total Citations

110

H-Index

About

Khadija Shaheen is a leading researcher at the intersection of continual learning and open-world computer vision, addressing the critical challenge of how autonomous systems can adapt to dynamic, non-stationary environments. Her seminal work, “Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks” (2022), has garnered 106 citations, establishing her as a key voice in the field. This comprehensive review systematically categorizes state-of-the-art methods that enable computational models to learn continuously over time, moving beyond frozen pre-trained models to handle real-world data shifts. Shaheen is also a pioneer in Open World Object Detection (OWOD), a paradigm that pushes object detection beyond closed-set assumptions. Her 2023 framework tackles the fundamental problem of detecting both known and novel objects in real-world scenarios, a critical capability for safe autonomous navigation and robotics. By bridging the gap between theoretical continual learning and practical deployment, Shaheen’s work provides essential blueprints for building truly adaptive AI systems. Her research is indispensable for students and engineers seeking to develop robust, lifelong learning agents that can safely operate in the open, unpredictable world.