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This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Dr. Danny Huang is a neurosurgery resident and post-doctorate researcher in the Department of Neurosurgery at Stanford University. As a physician-scientist with a focus on signal processing and neural electrophysiology, he is interested in building network-based brain computer interface (BCI) for neuropsychiatric disorders. He received his BSc at University of Alberta and his MD at Stanford University.
Deep Brain Stimulation (DBS) for chronic pain remains an off-label intervention with suboptimal outcomes, currently limited by a "one-size-fits-all" approach and approximately 50% efficacy. This talk proposes a transition from empirical, open-loop stimulation to a platform for adaptive, circuit-modifying neuromodulation built on three scientific pillars. First, we address the challenge of precision targeting by defining therapeutic circuits. We present a rapid, scalable stereoelectroencephalography (sEEG) mapping protocol that identifies components of the human thalamocortical circuit that rapidly alleviated intractable chronic facial pain. Second, we discuss the development of multimodal biomarkers for closed-loop control. Using a deep learning approach, we demonstrate that pain states can be decoded from a combination of intracranial neural features and automated facial dynamics, providing an objective surrogate for subjective pain reporting. Finally, we explore moving beyond symptom suppression toward disease modification. We present evidence that theta-burst direct electrical stimulation can induce lasting remodeling of human brain networks. By designing spatially and temporally selective stimulation patterns, we aim to reverse maladaptive circuit changes and drive network reorganization. By integrating these computational tools directly into the functional neurosurgical workflow, we move closer to a personalized, "biologically informed" treatment for intractable brain disorders.
Seminars attendees are invited to stay for a happy hour reception after the seminar to continue the conversation with the speaker.
For additional information and upcoming events, please visit the seminar series page
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