Ruohan Zhang

I am a postdoctoral researcher at Stanford Vision and Learning Lab (SVL), as well as a Wu Tsai Human Performance Alliance Fellow. I work on robotics, human-robot interaction, brain-machine interface, cognitive science, and neuroscience.

I am currently working with Prof. Fei-Fei Li, Prof. Jiajun Wu, and Prof. Silvio Savarese. I received my Ph.D. from The University of Texas at Austin, advised by Prof. Dana Ballard and Prof. Mary Hayhoe.

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Research

My research focus is human-centered artificial intelligence: Understanding human intelligence for developing biologically-inspired AI algorithms, as well as making AIs more compatible with humans. Please see my research summary for more details.


clean-usnob Understanding Human Intelligence in the Era of Artificial Intelligence.
Ruohan Zhang
Research Summary, 2020
paper
clean-usnob Interaction Modeling with Multiplex Attention
Fan-Yun Sun, Isaac Kauvar, Ruohan Zhang, Jiachen Li, Mykel Kochenderfer, Jiajun Wu, Nick Haber
Advances in Neural Information Processing Systems (NeurIPS), 2022
link | code
clean-usnob How to Train your Decision-Making AIs?
Ruohan Zhang, Dhruva Bansal
The Gradient, 2022
link | paper
clean-usnob Selective Visual Attention during Public Speaking in an Immersive Context
Mikael Rubin, Sihang Guo, Karl Muller, Ruohan Zhang, Michael Telch, Mary Hayhoe
Attention, Perception, & Psychophysics, 2022
link | paper | VSS2021 abstract |
clean-usnob Machine versus Human Attention in Deep Reinforcement Learning Tasks
Sihang Guo, Ruohan Zhang, Bo Liu, Yifeng Zhu, Mary Hayhoe, Dana Ballard, Peter Stone
Advances in Neural Information Processing Systems (NeurIPS), 2021
link | paper | arxiv | VSS2021 abstract
clean-usnob Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation
Lin Guan, Mudit Verma, Sihang Guo, Ruohan Zhang, Subbarao Kambhampati
Advances in Neural Information Processing Systems (NeurIPS), 2021   (Spotlight)
link | paper | arxiv | talk
clean-usnob Recent Advances in Leveraging Human Guidance for Sequential Decision-Making tasks
Ruohan Zhang*, Faraz Torabi*, Garrett Warnell, Peter Stone. (*equally contributed)
Autonomous Agents and Multi-Agent Systems (JAAMAS), 2021
link | paper | arxiv
clean-usnob A Modular Attention Hypothesis for Modeling Visuomotor Behaviors
Ruohan Zhang
The University of Texas at Austin Ph.D. Dissertation, 2021
paper | slides
clean-usnob Efficiently Guiding Imitation Learning Algorithms with Human Gaze
Akanksha Saran, Ruohan Zhang, Elaine Schaertl Short, Scott Niekum
Autonomous Agents and Multi-Agent Systems (AAMAS), 2021
paper | arxiv | bibtex | code | slides | media
clean-usnob The Hierarchical Evolution in Human Vision Modeling
Dana H. Ballard, Ruohan Zhang
Topics in Cognitive Sciences, 2021
link | paper
clean-usnob Human Gaze Assisted Artificial Intelligence: A Review
Ruohan Zhang, Akanksha Saran, Bo Liu, Yifeng Zhu, Sihang Guo, Scott Niekum, Dana Ballard, Mary Hayhoe
International Joint Conference on Artificial Intelligence (IJCAI) Survey Track, 2020
paper
clean-usnob Parallel Neural Processing with Gamma Frequency Latencies
Ruohan Zhang, Dana H. Ballard
Neural Computation, 2020
link | paper
clean-usnob Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset
Ruohan Zhang, Calen Walshe, Zhuode Liu, Lin Guan, Karl S. Muller, Jake A. Whritner, Luxin Zhang, Mary M Hayhoe, Dana Ballard
AAAI Conference on Artificial Intelligence (AAAI), 2020
link | paper | arxiv | dataset | code | poster | AAAI2020 RLG Workshop talk
clean-usnob An Initial Attempt of Combining Visual Selective Attention with Deep Reinforcement Learning
Liu Yuezhang, Ruohan Zhang, Dana Ballard
arxiv, 2020
arxiv
clean-usnob Leveraging Human Guidance for Deep Reinforcement Learning Tasks
Ruohan Zhang, Faraz Torabi, Lin Guan, Dana Ballard, Peter Stone
International Joint Conference on Artificial Intelligence (IJCAI) Survey Track, 2019
paper | arxiv
clean-usnob AGIL: Learning Attention from Human for Visuomotor Tasks
Ruohan Zhang, Zhuode Liu, Luxin Zhang, Jake Whritner, Karl Muller, Mary Hayhoe, Dana Ballard
European Conference on Computer Vision (ECCV), 2018
link | paper | arxiv | dataset | code | VSS2018 abstract | VSS2018 talk | CCN2017 version
clean-usnob Modeling Sensory-Motor Decisions in Natural Behavior
Ruohan Zhang, Shun Zhang, Matthew Tong, Yuchen Cui, Constatin Rothkopf, Dana Ballard, Mary Hayhoe
PLoS Computational Biology, 2018
link | paper | VSS2017 abstract | VSS2017 talk | NETI2016 poster |
clean-usnob Model Checking For Safe Navigation Among Humans
Sebastian Junges, Nils Jansen, Joost-Pieter Katoen, Ufuk Topcu, Ruohan Zhang, Mary Hayhoe
International Conference on Quantitative Evaluation of SysTem (QEST), 2018
link | paper
clean-usnob Fast and Precise Black and White Ball Detection for RoboCup Soccer
Jacob Menashe, Josh Kelle, Katie Genter, Josiah Hanna, Elad Liebman, Sanmit Narvekar, Ruohan Zhang, Peter Stone
RoboCup Symposium, 2017
paper | code
clean-usnob Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain
Xiangru Huang, Ian E.H. Yen, Ruohan Zhang, Qixing Huang, Pradeep Ravikumar, Inderjit S. Dhillon
Artificial Intelligence and Statistics (AISTATS), 2017
paper | code
clean-usnob Participatory Art Museum: Collecting and Modeling Crowd Opinions
Xiaoyu Zeng, Ruohan Zhang
AAAI Conference on Artificial Intelligence (AAAI) Student Abstract, 2017
link | paper
clean-usnob UT Austin Villa: Project-Driven Research in AI and Robotics
Katie Genter, Patrick MacAlpine, Jacob Menashe, Josiah Hanna, Elad Liebman, Sanmit Narvekar, Ruohan Zhang, Peter Stone
IEEE Intelligent Systems 31(2), 2016
link | code
clean-usnob Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain
Ian E.H. Yen, Xiangru Huang, Kai Zhong, Ruohan Zhang, Pradeep Ravikumar, Inderjit S. Dhillon
Advances in Neural Information Processing Systems (NIPS), 2016
link | paper
clean-usnob Decision-Making Policies for Heterogeneous Autonomous Multi-Agent Systems with Safety Constraints
Ruohan Zhang, Yue Yu, Mahmoud El Chamie, Beh├žet A├žikmese, and Dana H. Ballard
International Joint Conference on Artificial Intelligence (IJCAI), 2016
paper
clean-usnob Maximum Sustainable Yield Problem for Robot Foraging and Construction System
Ruohan Zhang, Zhao Song
International Joint Conference on Artificial Intelligence (IJCAI), 2016
paper
Misc.

clean-usnob Cortical Spikes use Analog Sparse Coding
Dana Ballard, Ruohan Zhang, Luc Gentet
bioRxiv, 2020
paper
clean-usnob Attention Guided Imitation Learning and Reinforcement Learning
Ruohan Zhang
AAAI Conference on Artificial Intelligence (AAAI) Doctoral Consortium, 2019
paper | NETI2019 poster
clean-usnob Learning Attention Model From Human for Visuomotor Tasks
Luxin Zhang, Ruohan Zhang, Zhuode Liu, Mary Hayhoe, Dana Ballard
AAAI Conference on Artificial Intelligence (AAAI) Student Abstract, 2018
link
clean-usnob Visual Attention Guided Deep Imitation Learning
Ruohan Zhang*, Zhuode Liu*, Luxin Zhang, Karl Muller, Mary Hayhoe, Dana Ballard. (* equally contributed)
NIPS Cognitively Informed Artificial Intelligence Workshop, 2017
paper
clean-usnob UT Austin Villa 2017 Team Description Paper for the Standard Platform League
Katie Genter, Josiah Hanna, Josh Kelle, Elad Liebman, Jacob Menashe, Sanmit Narvekar, Ruohan Zhang, Peter Stone
RoboCup Symposium, 2017
paper
clean-usnob UT Austin Villa 2016 Team Description Paper for the Standard Platform League
Katie Genter, Josiah Hanna, Josh Kelle, Elad Liebman, Jacob Menashe, Sanmit Narvekar, Rishi Shah, Ruohan Zhang, Peter Stone
RoboCup Symposium, 2016
paper
clean-usnob UT Austin Villa 2015 Team Description Paper for the Standard Platform League
Katie Genter, Josiah Hanna, Elad Liebman, Jacob Menashe, Sanmit Narvekar, Jivko Sinapov, Ruohan Zhang, Peter Stone
RoboCup Symposium, 2015
paper
clean-usnob Global Policy Construction in Modular Reinforcement Learning
Ruohan Zhang, Zhao Song, Dana Ballard
AAAI Conference on Artificial Intelligence (AAAI) Student Abstract, 2015
paper

Template from Jon Barron's website.