C8 Predicting antibiotic resistance in Mycobacterium tuberculosis with genomic machine learning

Chang Ho Yoon’s training in internal medicine has taken him from London to Auckland. His interest in the application of computer technology in clinical practice spurred him to lead the development and research of a smartphone app for antibiotic guidelines at an academic hospital. As a Fulbright Scholar, BEST Scholar, and a Gavin & Ann Kellaway Medical Research Fellow, he hopes to study machine learning and large-scale data integration during the master’s program in order to segue into a PhD. Dr. Yoon's research interests include machine learning, data science, smartphone healthcare apps, cancer genomics, epidemic prediction, and technology in medical education.

Anna Green is a computational biologist who loves to think about the evolution of bacterial genomes. She did her PhD in Debora Mark's lab, where she studied the evolution and specificity of protein interactions, and developed methods to detect interactions from genomic sequences. Anna is working to integrate techniques for phenotype prediction for protein and genome sequences, and applying these methods to understand the evolution and pathology of Mycobacterium tuberculosis.

A bioinformatician who fell in love with evolutionary biology. He enjoys using cutting-edge computational tools to study how microbial genomes work and evolve. Further, he loves to teach! He joined the Farhat lab in 2017 and he is currently studying the role of insertions and deletions on the evolution and pathogenicity of Mycobacterium tuberculosis.

Andrew Beam is an assistant professor in the Department of Epidemiology at the Harvard T.H. Chan School of Public Health, with secondary appointments in the Department of Biomedical Informatics at Harvard Medical School and the Department of Newborn Medicine at Brigham and Women’s Hospital. His research develops and applies machine-learning methods to extract meaningful insights from clinical and biological datasets, and he is the recipient of a Pioneer Award from the Robert Wood Johnson Foundation for his work on medical artificial intelligence. Previously he was a Senior Fellow at Flagship Pioneering and the founding head of machine learning at Generate Biosciences, Inc., a Flagship-backed venture that seeks to use machine learning to improve our ability to engineer proteins. He earned his PhD in 2014 from N.C. State University for work on Bayesian neural networks, and he holds degrees in computer science (BS), computer engineering (BS), electrical engineering (BS), and statistics (MS), also from N.C. State. He completed a postdoctoral fellowship in Biomedical Informatics at Harvard Medical School and then served as a junior faculty member.

Maha Farhat holds an MD from the McGill University Faculty of Medicine and a MSc in biostatistics from the Harvard Chan School of Public Health. She is also a practicing physician at the Massachusetts General Hospital Division of Pulmonary and Critical Care Medicine. Dr. Farhat’s research focuses on the development and application of methods for associating genotype and phenotype in infectious disease pathogens, with a strong emphasis on translation to better diagnostics and surveillance in resource-poor settings. To date, Farhat’s work has focused on the pathogen Mycobacterium tuberculosis and spans the spectrum from computational analysis to field studies. She is PI and Co-Investigator on several large projects funded by NIH including the NIAID and the BD2K initiative.