David Knowles is an Assistant Professor at the Data Science Institute at Columbia University, focusing on computer science and its applications in genomics. He completed his undergraduate studies in Natural Sciences with a specialization in Information Engineering at the University of Cambridge, followed by a Master's degree in Bioinformatics and Systems Biology at Imperial College London. He pursued his PhD at the Cambridge University Engineering Department, where he was part of the Machine Learning Group under Zoubin Ghahramani. His research primarily involves Bayesian nonparametric models, factor analysis, hierarchical clustering, and (stochastic) variational inference. Before joining Columbia, he was a post-doctoral researcher at Stanford University in collaboration with esteemed faculty in Computational Systems Biology and Genetics. As a core faculty member at the New York Genome Center, his lab develops and applies statistical machine learning methods to compute genomics, addressing the challenges posed by the exponential growth of genomic data and the complexities of data management in biomedical research. Dr. Knowles' work aims to enhance data analysis pipelines, mitigate errors, and improve the usability of genomic datasets in research.