AIMI AI Happy Hour Episode 12: Unglamorous Aspects of AI and Medicine

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Open to all

Link to join or set a reminder: http://bit.ly/aimihh12

Hear perspectives from a diverse expert panel of AI clinicians, researchers, computer scientists, and industry leaders:

• Matthew Lungren, MD, MPH (Stanford): Dr. Lungren is an Associate Professor of Radiology and the Co-Director of the Stanford Center for Artificial Intelligence in Medicine and Imaging. His leading research interest is in the field of machine learning and deep learning in medical imaging and clinical informatics. He is primary or senior author on many medical imaging machine learning projects including work on deep learning techniques to classify clinical radiology images and report data. He holds an MPH which focuses on his primary interest in biostatistical modeling in epidemiology and health policy in the national healthcare landscape, and he has extensive applied experience in both statistical as well as machine learning applications to medical imaging problems. In his role as PI on several grant funded projects he has managed graduate students and postdoctoral fellows and collaborated with colleagues across disciplines and collaborators at other institutions to successfully complete the aims of his research. His record of accomplished and productive research projects in an area of high relevance and expertise and experience put him in position to support the proposed project with a strong team of collaborators.
• Monica Agrawal, PhD Student (MIT): Monica is a 3rd year computer science PhD student at MIT in the Clinical Machine Learning group. Her research develops machine learning methods to comprehend longitudinal clinical notes and improve the clinical documentation process. Previously, she earned her BS and MS in computer science at Stanford, where she researched biological networks in the SNAP group.
• Pranav Rajpurkar, PhD Candidate (Stanford): Pranav Rajpurkar is a final year PhD candidate at Stanford in Computer Science, and an incoming Assistant Professor at Harvard in the Department of Biomedical Informatics. Pranav works on building reliable artificial intelligence (AI) technologies for medical decision making. Pranav’s work has been published in 30+ peer-reviewed publications in both scientific journals and AI conferences (receiving over 7000 citations) and has been covered by media outlets including NPR, The Washington Post, and WIRED. Pranav founded the AI for Healthcare Bootcamp at Stanford, where he has worked closely with and mentored over 100 Stanford students and collaborated with 18 faculty members on various research projects. He designed and instructed the Coursera course series on AI for Medicine, now with 40,000+ students. Pranav’s PhD was jointly advised by Dr. Andrew Ng and Dr. Percy Liang at Stanford University, where Pranav also received both his Bachelors and Masters Degrees in Computer Science.
• Adriel Saporta, MBA/MS Computer Science Student (Stanford): Adriel Saporta is earning her Masters in Computer Science at Stanford, where she currently conducts research at the intersection of AI and healthcare in Dr. Andrew Ng’s Stanford Machine Learning Group. She has held engineering and product roles across both big tech (Apple, Amazon) and start-ups (SeatGeek, Common). Adriel began her career as Anna Wintour’s executive assistant at Vogue (remember The Devil Wears Prada?), after which she spent time working in the theater industry in New York City. She holds an MBA from the Stanford Graduate School of Business and a BA in Comparative Literature from Yale University. Born and raised in Brooklyn, Adriel is proud to be half-Cuban and half-Greek.
• Adam Yala, PhD Candidate (MIT): Adam Yala works in the intersection of Machine Learning and Oncology. He's developed algorithms to both predict future cancer risk and design personalized screening policies. His tools have been clinically implemented at MGH and used to interpret hundreds of thousands of mammograms.
• Chloe O'Connell, MD, MS (MGH): Chloe O'Connell is a second year Anesthesiology resident at Massachusetts General Hospital (MGH) in Boston. Prior to residency, she earned her MD and MS in Biomedical Informatics at Stanford University. While working on her master's degree, she developed an interest in deep learning applications to healthcare. As part of Andrew Ng's "AI for Healthcare Bootcamp", she worked on a project investigating whether assistance from a deep learning algorithm was able to improve physicians' ability to diagnose tuberculosis in patients with HIV, based on both chest x-rays and clinical information. As a member of MGH's PRIME research track, her current research is focused on developing a machine learning-based decision support tool that is able to predict hypotension following induction of anesthesia.

This live panel discussion is part of the AI Happy Hour series, brought to you by Stanford AIMI and friends. We cover hot topics in AI in medicine as well as live questions & comments from attendees.

It's casual, insightful, and open to all!