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?