Maryam "Mary" Pourebadi

Franklin Antonio Hall, 3rd floor, UC San Diego, La Jolla, CA 92093 ·
(707) 709-MARY ·

Maryam Mary Pourebadi is a Ph.D. candidate in the Computer Science and Engineering department, at University of California, San Diego, with a joint appointment as a neurosciences researcher in the Department of Neurosciences at UC San Diego School of Medicine, 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.

Maryam's research focuses on robotics, social robots, personalized robots, anthropomorphic design, human-computer interaction, artificial intelligence, computer vision, health technology, educational intervention, intersectional bias, and ethics. Trained as both a scientist and engineer, her research explores the use of expressive interactive social robots in the real-world environment settings. She 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, leveraging robots’ automation and control modalities to impact human health and safety. She explands her research by designing 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.

Maryam 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, GPSA Award for Computing Research Association Women (CRA-W).

Maryam received a Master's in Computer Science from Kent State University in 2017. As a graduate researcher in the Computer Vision and Image Processing Lab, she worked on developing deep learning approaches for improving the state-of-the-art of unsupervised image quality assessment. She also received a B.Sc. in Computer Engineering in 2015, working on developing AI algorithms for cancer detection.

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