Assistant Professor Of Computer Science
Joined department: Fall 2021
My research interests lie at the intersection of robotics, machine learning, and artificial intelligence, focusing on scalable motion planning and control, manipulation, navigation, task planning, state-estimation, tactile perception, localization and mapping, active sensing, and sim-to-real transfer. We design, develop, and extend theory from the fields of deep learning, reinforcement learning, optimal control, computer vision, game theory, optimization, graphical models, statistics, information theory, physics, cognitive science, computer graphics, and other related areas.
Ahmed Qureshi, Arsalan Mousavian, Chris Paxton, Michael Yip, Dieter Fox, NeRP: Neural Rearrangement Planning for Unknown Objects, Robotics: Science and Systems, 2021.
Ahmed Qureshi, Jiangeng Dong, Asfiya Baig, Michael C. Yip, Constrained Motion Planning Networks X, IEEE Transaction on Robotics, 2021.
Ahmed Qureshi, Jacob J Johnson, Yuzhe Qin, Taylor Henderson, Byron Boots, Michael C. Yip, Composing Task-Agnostic Policies with Deep Reinforcement Learning, International Conference on Representation Learning (ICLR), 2020.
Ahmed Qureshi, Byron Boots, Michael C. Yip, Adversarial Imitation via Variational Inverse Reinforcement Learning, International Conference on Representation Learning (ICLR), 2019.
Ahmed Qureshi, Yutaka Nakamura, Yuichiro Yoshikawa, Hiroshi Ishiguro, Intrinsically motivated reinforcement learning for human-robot interaction in the real-world, Neural Networks, 2018.