John Kang, MD, Ph.D. is an assistant professor of Radiation Oncology and Biomedical Informatics Lead at the University of Washington in Seattle. His research interests include the application of Natural Language Processing (NLP) to examine trends in the MaML space. He is a physician-data scientist passionate about uncovering the complex interactions underneath large datasets. He has over 10 years of experience in the novel applications of computational modeling and machine learning in biology systems.
Host: David Wu
Twitter: @davidjhwu
Audio Producer: Aaron Schumacher
Twitter: a_schu95
Video Editor + Art: Saurin Kantesaria
Instagram: saorange314
Social Media: Nikhil Kapur
00:45 Could you tell us about your journey to the intersection of medicine and machine learning
07:40 Balancing Residency Training and staying caught up on research in the machine learning space
16:00 Using machine learning to understand biostatistics
18:12 How would you describe the research that you find the most exciting / Unsupervised learning
23:00 Overview of Word Embedding and addressing potential bias
29:25 Dr. Kang’s application of word embedding for research funding
42:52 The intersection of artificial intelligence and human intelligence
45:35 T-SNE / T-Distributed Stochastic Neighbor Embedding in grant analysis
50:50 Has T-SNE helped guide Dr. Kang’s research and grant writing
57:00 The future of creativity and ChatGPT
01:02:30 Fear vs Hope in the Medicine and Machine Learning space
01:07:00 What do you think is the future of the MaML space in the next 10-20 years?
01:11:02 What advice would you give yourself as you were finishing medical school?
