PhD Candidate: Michael Murphy

Research Advisors: Prof. Ernest Fraenkel, Prof. Stefanie Jegelka

Date:  Monday, Oct 23, 2023

Time: 11 AM-12 PM

Room: 68-180

Title: Machine Learning Methods for High Throughput Biological Data

Abstract: 

Machine learning is becoming a pivotal tool in the analysis of datasets generated from high-throughput biological omics experiments. However, omics data introduces distinctive algorithmic challenges that set it apart from other domains where machine learning is applied. These challenges encompass issues such as limited data availability, complex noise, ambiguities in representation, and the absence of definitive ground truth for validation. In this thesis, I present three examples of machine learning applications to different omics modalities in which I address these challenges. In my...