Alyssa Kubota

I am a PhD student with the Contextual Robotics Institute in the Computer Science and Engineering Department at UC San Diego. I am advised by Dr. Laurel Riek in the Healthcare Robotics Lab. My research unites robotics and artificial intelligence to create technology that helps people in need.

[CV]

Publications

Kubota, A., Pourebadi, M., Banh, S., Kim, S., and Riek, L. D. "Somebody That I Used to Know: The Risks of Personalizing Robots for Dementia Care". In Proceedings of We Robot, 2021. [Acceptance Rate: 15%]
Kubota, A. and Riek, L. D. "Methods for Robot Behavior Adaptation for Cognitive Neurorehabilitation". Annual Review of Control, Robotics, and Autonomous Systems, 2021.
Kubota, A. and Riek, L. D. "Behavior Adaptation for Robot-Assisted Neurorehabilitation". In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI) Pioneers Workshop, 2021. [Acceptance Rate: 37%] [PDF]
Banh, S., Zheng, E., Kubota, A., and Riek, L. D. "A Robot-based Gait Training System for Post-Stroke Rehabilitation". In Companion of the ACM/IEEE International Conference on Human-Robot Interaction (HRI Companion) Late Breaking Reports Workshop, 2021. [PDF]
Kubota, A., Peterson, E. I. C., Rajendren, V., Kress-Gazit, H., and Riek, L. D. "JESSIE: Synthesizing Social Robot Behaviors for Personalized Neurorehabilitation and Beyond". In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, 2020. [Acceptance Rate: 24%] [PDF]
Taylor, A., Lee, H. R., Kubota, A., and Riek, L. D. "Coordinating Clinical Teams: Using Robots to Empower Nurses to Stop the Line". In Proceedings of the ACM Conference on Computer-Supported Cooperative Work (CSCW), 2019. [Acceptance Rate: 30%] [PDF]

Best Paper Award Honorable Mention (Top 5% of submissions)

Kubota, A., Iqbal, T., Shah, J. A., and Riek, L. D. "Activity recognition in manufacturing: The roles of motion capture and sEMG+inertial wearables in detecting fine vs. gross motion". In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2019. [PDF]
Frank, A. E., Kubota, A., and Riek, L. D. "Wearable activity recognition for robust human-robot teaming in safety-critical environments via hybrid neural networks". In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019. [PDF]

Research

Long-term Robot Learning in Real-World Settings

UC San Diego
We are developing social robots to support people with dementia and their caregivers. As these robots will be placed in homes and interacting with people over long periods of time, they need to adapt to the user's preferences and needs over time. Thus, we are developing preference learning algorithms to keep people interested and engaged. We are engaging with members of this community to co-design and evaluate these robots.

Human-Robot Teaming in Safety Critical Environments

UC San Diego
Through this project, we explored multi-human, multi-robot collaboration in safety-critical environments such as emergency departments and manufacturing settings. These environments contain privacy-sensitive information, so placing robots with visual sensors in these areas may introduce security and privacy concerns. However, robots still need the capability to perceive and understand activity in these environments in order to interact meaningfully. Therefore, we explored the integration of non-visual wearable sensors with machine learning algorithms in order to perform human activity recognition and model workflow.

Computing for Active Transportation

Harvey Mudd College
While working together with members of the surrounding community, I developed a “walking school bus” routing program designed to help students safely and easily meet to walk to school together. The program integrates Google Maps and OpenStreetMaps APIs to automatically determine where students should meet to minimize travel time for students and parents. By comparing expected travel times with and without the program, I found that this program would have many benefits: it reduces the time parents spend driving their children to school, helps improve the health of students who participate, and reduces traffic and thereby air pollution around the school.

Education

University of California, San Diego

Doctor of Philosophy, Computer Science

Anticipated Graduation: June 2022

University of California, San Diego

Master of Science, Computer Science

December 2020

Harvey Mudd College

Bachelor of Science, Computer Science

May 2017

Let's connect!

Send me an email: akubota [at] eng.ucsd.edu
And find me at the following sites: