Human-Robot Interaction (HRI) · Robotics · AI · Augmented Reality (AR)
Developing capable robotic systems & understandable robot interactions
*Shown is a mobile manipulation testbed to evaluate my research
Meet Zhao Han
he/him · /’jau̇-‘hän/ · 韩昭
Zhao Han is an incoming Assistant Professor of Computer Science and Engineering at the University of South Florida, joining the faculty in August 2023. He is currently a Post-Doctoral Fellow at Colorado School of Mines.
His research lies broadly in human-robot interaction (HRI), robotics, AI, and augmented reality (AR). He focuses on designing, developing, and evaluating novel robotic systems and interactions, for embodied robots to be more capable and understandable while interacting and collaborating with humans.
To advance this work, Dr. Han takes an interdisciplinary and human-centered approach, developing broad expertise in explainable AI (robot explanations) for trust, projector-based and head-worn AR for communication, mobile manipulation for real-world evaluation, cognitive status-informed references, robot failures for robustness, and more.
Still in his early career, Dr. Han has already published 30 peer-reviewed papers, collaborating across universities like CMU and fields like Psychology. He received the best long-paper award at INLG 2022, the best late-breaking report third prize at HRI 2022, and a best late-breaking report nominee at HRI 2023. He also led teams to win multiple robot competitions.
He believes service is beneficial to academic life and society, fostering friendship, collaboration, and communities. Dr. Zhao Han is a Publications Chair of HRI 2024, a Program Committee (PC) member of HRI 2023, and a General Co-Chair of the 2022 AI-HRI symposium. He has also co-organized multiple workshops and chaired paper sessions.
Recognized with a university-wide Diversity, Equity, and Inclusion (DEI) Award, Dr. Han takes action on DEI. He founded Mines Asian Community Alliance and co-organized the Inclusive HRI Workshop. He is also active in outreach to engage under-represented groups.
With a strong DEI component, Dr. Han’s Augmented Reality course was rated 4.72/5.0, confirming his real-world and student-focused approach. He has also mentored 27 students, including 13 underrepresented, 12 female, and four graduate students.
Dr. Han holds a Ph.D. in Computer Science from UMass Lowell, advised by AAAI Fellow Dr. Holly Yanco. Previously, he received his M.S. and B.S. degrees in Computer Science from the University of Manitoba in Canada.
from Zhao Han
Multiple fully funded Ph.D. positions are available →
Apr 6, 2023
[CogSci '23] Our paper "Evaluating Cognitive Status-Informed Referring Form Selection for Human-Robot Interactions" was accepted with full paper publication to CogSci 2023!
Mar 10, 2023
[HRI '23 Best LBR Nominee] 7 out of 139 (5%)! Our late breaking report (LBR) "Towards Improved Replicability of Human Studies in Human-Robot Interaction: Recommendations for Formalized Reporting" is one of the 7 out of 139 (5%) Best LBR Nominees!
Mar 7, 2023
Our second edition of the workshop on Affective Human-Robot Interaction (AHRI) was accepted to ACII 2023, the annual International Conference on Affective Computing & Intelligent Interaction!
Feb 14, 2023
I just uploaded the video presentation for our HRI 2023 paper Crossing Reality: Comparing Physical and Virtual Robot Deixis!
Jan 11, 2023
[HRI '23 LBR] Our late breaking report (LBR) "Towards Improved Replicability of Human Studies in Human-Robot Interaction: Recommendations for Formalized Reporting" was accepted to HRI 2023! This is an important work to improve the validity of HRI study and results.
Dec 21, 2022
[AR Course Evaluation] Students rated 4.72/5.0 for their learning experience in my Fall 2022 Augmented Reality (AR) course! It is consistent across all sub-scales.
Evaluating Cognitive Status-Informed Referring Form Selection for Human-Robot Interactions
Inclusive HRI II: Equity and Diversity in Design, Application, Methods, and Community
Towards Improved Replicability of Human Studies in Human-Robot Interaction: Recommendations for Formalized Reporting