熟女

Mengdi Xu is an Assistant Professor at Tsinghua University’s Institute for Interdisciplinary Information Sciences. Her research focuses on developing scalable, adaptable, and reliable robots that seamlessly interact with humans in everyday activities, with an emphasis on generalizable robot learning and trustworthy embodied AI. She aims to equip robots with the ability to efficiently and robustly solve novel tasks in open-ended environments, spanning in-context robot learning, distributionally robust reinforcement learning, robot tool use, large-scale simulation benchmarks, data generation, and preference-aligned reinforcement learning. She received her Ph.D. from Carnegie Mellon University, completed postdoctoral research at Stanford University, and earned her bachelor’s degree from Tsinghua University. Her work has been published in top venues including ICML, NeurIPS, ICLR, AISTATS, CoRL, IROS, NAACL, and JMLR. She has also conducted research at Google DeepMind, the MIT-IBM Watson AI Lab, and the Toyota Research Institute. Her contributions have been recognized with multiple honors, including RSS Pioneers (2023), EECS Rising Stars (2023), and Computational & Data Science Rising Stars (2023).

Best PhD Dissertation Award 2024, Mechanical Engineering, CMU (2024)

EECS Rising Stars (2023)

Robotics: Science and Systems (RSS) Pioneers (2023)

Rising Stars in Computational and Data Sciences (2023)

Qualcomm Scholarship (2016)

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