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AIMI NextGen Tech Talks (Episode 3): Machine Learning in Medical Imaging

Event Details:

Monday, April 29, 2024
5:00pm - 5:45pm PDT

AIMI NextGen Tech Talks is a live online webinar series tailored towards high school students eager to explore the field of AI in medicine and health. Hear from renowned experts in the field as they share their professional journeys shaping the future of healthcare with technology! Attendees will have an opportunity to participate in a live webinar Q&A with the speakers. 

Speakers:

Speaker Bios:

Dr. Akshay Chaudhari is an Assistant Professor in the Integrative Biomedical Imaging Informatics at Stanford (IBIIS) section in the Department of Radiology and (by courtesy) in the Department of Biomedical Data Science. He leads the Machine Intelligence in Medical Imaging research group at Stanford and has a primary research interest that lies at the intersection of artificial intelligence and medical imaging. His group develops new techniques for accelerated MRI acquisition and downstream image analysis, extracting prognostic insights from already-acquired CT imaging, and developing new multi-modal deep learning algorithms for healthcare that leverage computer vision, natural language, and medical records. Dr. Chaudhari has won the W.S. Moore Young Investigator Award and the Junior Fellow Award from the International Society for Magnetic Resonance in Medicine. Dr. Chaudhari has also been inducted into the Academy of Radiology’s Council of Early Career Investigators in Imaging program. He also serves as the Associate Director of Research and Education at the Stanford AIMI Center and is an advisory board member of the Precision Health and Integrated Diagnostics Center.

Dr. Sergios Gatidis completed his medical training at the University of Tuebingen / Germany and received his Diploma in Mathematics from from the Universities of Tuebingen and Hagen / Germany. His research is focused on multiparametric oncologic medical imaging including hybrid imaging as well as on methods and applications of machine learning for medical image analysis.

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