Research: Human-Centered AI and Robotics

My longterm research interest is human-centered artificial intelligence and robotics, inspired by Stanford Institute for Human-Centered Artificial Intelligence (HAI)'s guidelines:

• Human Impact: Studying, forecasting, and guiding the human and societal impact of AI and robotics. We seek to identify tasks that matter to humans, and develop benchmarks and datasets to guide our research toward solving these tasks, as in BEHAVIOR | BEHAVIOR Vision Suite | Embodied Agent Interface
• Human Intelligence: Developing human-level, human-inspired intelligence. We leverage large-scale and diverse human data for robot learning, as in MimicPlay | DexCap | TRANSIC | SEED | AGIL.
  We further try to better understand human brain and behaviors (neuroscience and cognitive science), as in GSM | ModularIRL | VIVR.
• Human Augmentation: Creating AI and robotic applications that augment human capabilities. We aim at widening the communication pipeline between humans and robots, as in NOIR | VoxPoser | ReKep

Publications (show selected / show by date / show by topic)
Research Topics: Datasets and Benchmarks / Robot Learning from Humans / Human-Robot Interaction / Foundation Models for Robotics / Neuroscience and Cognitive Science

Awards

• 2022-24 Wu Tsai Human Performance Alliance Fellowship, Stanford University
• 2019 University Continuing Fellowship, The University of Texas at Austin
• 2019 AAAI/SIGAI Doctoral Consortium Scholarship
• 2018 General International Student and Scholar Services Financial Aid Award, The University of Texas at Austin
• 2017-18 Google AR/VR Research Award
• 2014-17 College Continuing Fellowship, The University of Texas at Austin
• 2011 PSI CHI International Honor Society in Psychology
• 2008-12 Rhodes College Grants
• 2009-10 Rhodes College Honor Roll
• 2008-09 Rhodes College Dean’s List

Service and Teaching

• Organizer, ICCV, ECCV, NeurIPS workshops
• Conference review, CoRL, RSS, IROS, ICRA, NeurIPS
• Journal review, Frontiers in Artificial Intelligence
• Journal review, Journal of Vision
• Book review, Chapman and Hall/CRC Press

• Instructor, CS231n: Deep Learning for Computer Vision 2023, Stanford University
• Teaching Assistant, Computational Brain 2019, The University of Texas at Austin
• Teaching Assistant, Machine Learning 2015, The University of Texas at Austin
• Teaching Assistant, Discrete Mathematics (Honor) 2014, The University of Texas at Austin
• Teaching Assistant, Discrete Mathematics 2013, The University of Texas at Austin
• Teaching Assistant, Logic, Sets, and Functions 2013, The University of Texas at Austin
• Teaching Assistant, Qualitative Methods 2012, Rhodes College

Mentoring

• Yingke Wang (Master, Stanford University)
• Tasha Kim (Master, Stanford University)
• Ryan Tan (Master, Stanford University → Glean)
• Sharon Lee (Master, Stanford University → PhD, Stanford University)
• Shuojia Fu (Master, Stanford University → PhD, Stanford University)
• Yilun Hao (Master, Stanford University → PhD, MIT)
• Jialu Gao (Undergrad, Tsinghua University → Master, Carnegie Mellon University)
• Wensi Ai (Undergrad, UCLA → Master, Stanford University)
• Emily Jin (Undergrad, Stanford University → Master, Stanford University)
• Zhuoyi Huang (Master, Stanford University → Microsoft AI)
• Minjune Hwang (Master, Stanford University → PhD, USC)
• Ayano Hiranaka (Master, Stanford University → PhD, USC)
• Dhruva Bansal (Master, Stanford University → Google DeepMind)
• Sihang Guo (Master, The University of Texas at Austin → PhD, The University of Texas at Austin)
• Dawei Liang (Master, The University of Texas at Austin → PhD, The University of Texas at Austin)
• Lin Guan (Undergrad, The University of Texas at Austin → PhD, Arizona State University)
• Yuezhang Liu (Undergrad, Tsinghua University → PhD, The University of Texas at Austin)
• Luxin Zhang (Undergrad, Peking University → Master, Carnegie Mellon University)
• Zhuode Liu (Master, The University of Texas at Austin → Google)

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