Workshop on Robot Learning at Deployment (Learn@Deploy)

Dr. David Fan is CTO, leading innovation and cutting edge technology, at FieldAI. Dr. Fan has a long history in developing autonomous robotic systems. He was the chief technologist for several DARPA program efforts, including the DARPA SubT Challenge and DARPA RACER. With a deep legacy in developing autonomous robotic systems, he is a leading authority and well-rounded technologist in bringing these technologies to the commercial sector.

Prior to FieldAI, Dr. Fan was a research engineer on autonomy at NIWC and a research fellow at NASA-JPL. Dr. Fan earned his PhD from Georgia Tech, where he focused on combining machine learning and classical techniques to enable safe and robust solutions to real-world robotics problems.

Prof. Guanya Shi is an Assistant Professor in the Robotics Institute and School of Computer Science at Carnegie Mellon University, where he leads the Learning and Control for Agile Robotics (LeCAR) Lab. He is also an Amazon Scholar on the Frontier AI & Robotics team. His research combines AI, robotics, and control to develop reliable, adaptive, and efficient methods for agile generalist robots, including humanoids.

He earned his PhD from Caltech in 2022 and his B.E. from Tsinghua University in 2017. Before joining CMU in 2023, he was a postdoctoral researcher at the University of Washington.

Prof. Mengdi Xu is an Assistant Professor at the Institute for Interdisciplinary Information Sciences (IIIS) at Tsinghua University. Her research focuses on building scalable, adaptable, and reliable robots that interact seamlessly with humans in everyday activities.

Previously, she was a postdoctoral researcher at the Stanford Vision and Learning Lab, working with Jiajun Wu and Fei-Fei Li. She earned her PhD from Carnegie Mellon University, advised by Ding Zhao in the SafeAI Lab, and received a Best PhD Dissertation Award for her work on adaptable generalist robots.

Dr. Michelle Lee is the founder and CEO of Medra, which develops AI-powered robotic systems to automate laboratory work and accelerate scientific discovery.

She earned her PhD at Stanford, conducting robotics research in the Stanford AI Lab. She has also worked at NVIDIA, SpaceX, and McKinsey.

Masha Itkina is a Research Lead and Manager for the Trustworthy Learning under Uncertainty (TLU) effort in the Robotics division at the Toyota Research Institute (TRI). Her research focuses on policy evaluation, failure detection and mitigation, and (inter)active and continual learning.

She completed her PhD at Stanford University in the Stanford Intelligent Systems Lab (SISL) on uncertainty-aware perception systems for self-driving cars. Her research has appeared in venues including NeurIPS, RSS, CoRL, ICRA, and Science Robotics. She has also given invited talks at workshops at these conferences and seminars at institutions such as Stanford, UC Berkeley, Cornell, and Princeton.

Victor is a Member of Technical Staff at Physical Intelligence, where he leads efforts to deploy robots into businesses. He previously worked on autonomous vehicles and robotics at Apple, NASA JPL, and startups Ike and Nuro. He holds an M.S. from the Carnegie Mellon University's Robotics Institute.

Joshua Manela is a founding engineer at Bedrock, where he trains and deploys end-to-end (pixel to action) behavior policies onto construction equipment. He brings a decade of experience in the AV industry, including his time at Waymo where he led various perception model development efforts across Waymo Via (long-haul trucking), and ride hailing.