Scholar
Mariah Schrum
Google Scholar ID: QuzrQzIAAAAJ
Research Scientist, Toyota Research Institute
Citations & Impact
Citations
673
H-index
14
i10-index
18
Publications
20
Co-authors
15
list available
Publications
Resume (English only)
Academic Achievements
- - Paper accepted: Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation, International Conference on Machine Learning
- - Paper accepted: MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving, IEEE Transactions on Robotics, to be presented at IROS
- - Paper accepted: Mixed-Initiative Multiagent Apprenticeship Learning for Human Training of Robot Teams, Neurips '23
- - Paper nominated for Best Student Paper: Investigating the Impact of Experience on a User's Ability to Perform Hierarchical Abstraction, RSS '23
- - Paper accepted: Privacy and Personalization: Transparency, Acceptance, and the Ethics of Personalized Robots, HRI Social Robots Personalisation Workshop
- - Paper accepted: Reciprocal MIND MELD: Improving Learning From Demonstration via Personalized, Reciprocal Teaching, CoRL
- - Paper accepted: Concerning Trends in Likert Scale Usage in Human-Robot Interaction: Towards Improving Best Practices, Transactions on Human-Robot Interaction (tHRI)
- - Paper presented: Meta-Active Learning in Probabilistically Safe Optimization, IROS
Research Experience
- - 2023 - Present: Postdoctoral Researcher, InterACT Lab, UC Berkeley
- - 2022: Intern, Toyota Research Institute, investigating a data-driven approach for optimizing autonomous vehicle driving style
- - 2021: Intern, Inuitive Surgical, worked on their new Ion surgical robot
Education
- - Ph.D.: 2023, Robotics, Georgia Institute of Technology, Advisor: Matthew Gombolay
- - M.S.: 2020, Computer Science, Georgia Institute of Technology
- - B.S.: 2018, Biomedical Engineering, Johns Hopkins University
Background
- Currently a postdoc at UC Berkeley working with Anca Dragan. Research interests include reinforcement learning applications in the real world, particularly RL for deep brain stimulation. During her PhD, she focused on developing deep learning algorithms to personalize autonomous systems, accounting for heterogeneity in human-machine interaction.
Miscellany
- Recipient of the Accessibility, Rehabilitation, and Movement Science Fellowship
