Marjorie Xie
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Mail code: 6002Campus: Tempe
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Dr. Marjorie Xie is a postdoctoral research scholar in AI & Society at Arizona State University’s School for the Future of Innovation in Society, in a fellowship joint with the New York Academy of Sciences.
Marjorie’s long term goal is to advance mental health and education through human-centered design and technology. Her postdoctoral research seeks to make therapies for mental disorders more effective and accessible. She is building systems for clinicians and patients to monitor symptoms in real time, and to provide feedback and interventions in a timely manner. To this end, she combines state-of-the-art tools from AI and the mind and brain sciences, including models of human reinforcement learning, and virtual reality (VR) for game design. Marjorie is particularly interested in “AI-in-the-human-loop” systems, which integrate AI into human learning processes. This research is done in collaboration with the labs of Angela Radulescu and Xiaosi Gu at Mt. Sinai Center for Computational Psychiatry in New York City,
Marjorie envisions a role for AI not only in the treatment of mental disorders, but also in facilitating the learning of social and emotional skills in children, which are critical for flourishing in life as adults. These skills include emotional awareness, empathy, and the ability to infer one's own needs. To bring her research from the lab to the real world, Marjorie intends to collaborate with clinicians, teachers, coaches, parents, and business leaders.
Prior to her postdoc, Marjorie did an internship at Basis Research Institute, building models of avian cognition and social behavior. Marjorie completed her Ph.D. in Neurobiology & Behavior at Columbia University, where she used AI tools to build interpretable models of neural systems in the brain. Before her PhD, she designed her own major in computational neuroscience at Princeton University, where she also studied philosophy, literature, and history.
- Ph.D. in Neurobiology & Behavior, Columbia University, New York
- A.B. in Computational Neuroscience (independent major), Princeton University, New Jersey
Affective Computing, Computational Psychiatry, Human-Computer Interaction
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Task-dependent optimal representations for cerebellar learning. M. Xie, S. P. Muscinelli, K. D. Harris & A. Litwin-Kumar. eLife 12: e82914 (2023).
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Specific connectivity optimizes learning in thalamocortical loops. K. Lakshminarasimhan, M. Xie, J. D. Cohen, B. Sauerbrei, A. W. Hantman, A. Litwin-Kumar & S. Escola. bioRxiv, 2022.09. 27.509618 (2022).
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FlpStop, a tool for conditional gene control in Drosophila. YE Fisher, HH Yang, J Isaacman-Beck, M Xie, DM Gohl, TR Clandinin. eLife 6, e22279 (2017).
- Sensorimotor transformations underlying variability in song intensity during Drosophila courtship. P Coen, M Xie, J Clemens, M Murthy. Neuron 89 (3), 629-644 (2016).
Volunteer coordinator for the development of a COVID-19 home viral test. Lab in the Time of Coronavirus: “Making a COVID-19 Home Test” | Columbia | Zuckerman Institute (2020)
Research Scientist Intern, Basis Research Institute, NY (Jan - Sept 2023)