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?

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Introduction

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