School of Medicine
Showing 1-6 of 6 Results
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Austen Brooks Casey
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioAusten Brooks Casey, PhD, is a postdoctoral scholar in the Department of Anesthesiology, Perioperative and Pain Medicine (advisor: Boris Dov Heifets, MD, PhD). He originates from western North Carolina, and has had a long-standing interest in psychiatric drug discovery, which was invigorated by initial coursework in organic chemistry and biochemistry. Austen trained at Northeastern University (advisor: Raymond G. Booth, PhD) where he studied the medicinal chemistry and molecular pharmacology of novel ligands targeting serotonergic G protein-coupled receptors. As a postdoc, he is investigating the the precise cell-type(s) and circuitry engaged by MDMA that evoke social approach and fear extinction retention in mice. His long-term goal is to perform circuit-based drug discovery targeting neural circuits underlying the symptoms and treatment of psychiatric disorders.
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Xiangjun Chen
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioDr. Xiangjun Chen is a Postdoctoral Scholar in the Department of Anesthesiology, Perioperative, and Pain Medicine at Stanford University. He earned his Ph.D. in Materials Science from UC San Diego, where he also completed a postdoctoral training prior to joining Stanford. His work focuses on engineering soft wearable systems for healthcare monitoring, AI-driven human-machine interfaces, and advanced actuators and sensors for soft robotics.
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Bernard Mawuli Cobbinah
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioCobbinah Bernard Mawuli is a Postdoctoral Scholar at Stanford University in the Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine. He is passionate about the intersection of AI and medicine, focusing on developing robust and effective approaches for preventive and predictive healthcare. His research aims to deepen the understanding of high-dimensional multi-omics medical data using advanced machine learning techniques. By exploring innovative ways to analyze this data, his work contributes to improved treatments and enhanced patient care. Through the analysis of large patient datasets, his goal is to create tools that empower clinicians to make more informed decisions, ultimately improving healthcare outcomes for all.
Prior to joining Stanford, he pioneered robust federated learning techniques for evolving data streams and developed methods to reduce multi-center MRI variability in diagnosing brain disorders.
