Maryam "Mary" Pourebadi

Contextual Robotics Institute, University of California San Diego
9500 Gilman Drive, La Jolla, CA 92093–0436
(707) 709-MARY

Maryam Mary Pourebadi is a Ph.D. candidate in the Computer Science and Engineering department, at the University of California San Diego, with a joint appointment as a neurosciences researcher in the Department of Neurosciences at UC San Diego, and is affiliated with the Contextual Robotics Institute at the Jacobs School of Engineering. She is a senior graduate researcher in the Healthcare Robotics Lab advised by Dr. Laurel Riek.

Mary's work is at the intersection of assistive robotics, artificial intelligence, computer vision, and health informatics. Trained as both a scientist and an engineer, she designs and develops interactive expressive robots that automatically learn from and interact with humans in real-world dynamic environments. Her goal is to leverage these robots’ automation and control modalities to impact human health and safety.

Mary currently works on building the next generation of autonomous, interactive physical robots and virtual avatars, that can realistically express human-like expressions and neurological impairments. To expand her research, she designs this technology as a personalized anti-bias educational intervention that can depict diverse social identities, simulate real clinical scenarios, and interact with clinical learners in real time. These systems are co-designed in close collaborations with stakeholders (e.g., neurocritical care specialists, people with neurological disorders, and medical educators) and through engaging in extensive data collection and modeling efforts.

Mary has received the ACM Student Research Competition Certificate of Recognition, UC San Diego Graduate Association Award for Outstanding Graduate Leader, Jacobs Graduate Student Council Award, Grace Hopper Celebration of Women in Computing (GHC) award, ACM Richard Tapia Celebration of Diversity in Computing (Tapia) award, and GPSA Award for Computing Research Association Women (CRA-W).

Mary received her M.Sc. degree in Computer Science from Kent State University in 2017, where she developed deep learning approaches for unsupervised image quality assessment. She received her B.Sc. degree in Computer Engineering in 2015, working on developing AI algorithms for cancer detection.

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