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Kobus Barnard Home Page

Best URL for linking to this page:    http://kobus.ca

Email:     kobus AT cs DOT arizona DOT edu
Office:   Gould-Simpson 708                            

Lab : Interdisciplinary Visual Intelligence (IVILAB)

Teaching: Current [ CS 535 ]   |   All courses taught

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Kobus Barnard outdoor photo

I am a professor of computer science at the University of Arizona. My primary appointment is with Computer Science. I also have an appointment with Electrical and Computer engineering (ECE), and serve as faculty for Cognitive Science, Statistics, and BIO5, Before coming to Arizona, I was a post doctoral fellow in computer vision at the University of California at Berkeley. I did my Ph.D. in computer science at Simon Fraser University, specializing in colour constancy. My research interests include image and video understanding, learning and fitting models of biological form, the application of computer vision to the organization and effective use of large image collections, and physics based vision problems such as understanding scene illumination.

IVILAB News

Recent IVILAB news

  • November, 2019

    ToMCAT. A collaboration between the Information School (INFO), Computer Science (CS), and Family Studies and Human Development (FSHD) has been awarded a large grant to develop a theory of mind-based cognitive architecture for teams (ToMCAT). The grant ($7.5M, for 48 months) is part of the DARPA Artificial Social Intelligence for Successful Teams (ASIST) program. The PI/Co-PIs collaborating on this project are: Adarsh Pyarelal (PI), Kobus Barnard, Emily Butler, Clayton Morrison, Rebecca Sharp, Mihai Surdeanu, and Marco Antonio Valenzuela-Escarcega. Data collection for the project will take place in the Lang Laboratory, housed in the Frances McClelland Institute for Children, Youth and Families in the Norton School of Family & Consumer Science.

    The goal of the project is to build artificially intelligent agents that understand both the social and goal-oriented aspects of teams in mission-like scenarios (e.g., search-and-rescue missions), and are able to reason about possible interventions. The agent, ToMCAT, needs to model human players' affect and beliefs about the situation and about each other's affect and beliefs (theory of mind). We will ground this work in extensive measurements of humans interacting in small teams, that will include audio, video, eye tracking, electrocardiography (EKG), electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and self report. The participants will execute missions within a Minecraft environment with one, two, three, or four human players interacting with the ToMCAT agent.

    Research areas. One unique aspect of this project is that we will use simultaneous EEG and fNIRS brain recording from all human team members to further our understanding of social coordination in teams. We expect the series of experiments will provide a large amount of very unique data. ToMCAT's evolving theories of mind will be implemented using dynamic Bayesian networks interacting with latent low-level data representation provided by neural networks. In addition, we will need to understand dialogue as indicative of affect, plans, and mission goals. Finally, ToMCAT will need to both understand team plans and also create its own plans.

    Further information is available on the project web site ml4ai.github.io/tomcat . This project started Nov 1, 2019. As we move forward, we will update this website regularly.

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