Apr 2023
Kaust Zoom meeting ID: 968 3265 8219 (Requires fast registration)
Or go directly to: https://kaust.zoom.us/meeting/register/tJIldOuuqzMoG913NaIqUf8w-5gnSphrhVLD
Machine learning (ML) is a rapidly growing field with applications in many areas. ML algorithms can be used to analyze large amounts of data, identify patterns, and make predictions. Consequently, ML models can be used to automate or optimize tasks that usually involve human perception, e.g., vision, reading, abstraction, etc. In this seminar, we will explore, through a series of use cases, the way in which my group is addressing key challenges in machine learning, including, the need for large amounts of data, prohibitive human intervention, explainability with sophisticated black-box algorithms, and the imperative to address biases and ethical concerns. Despite its challenges and limitations, ML is and has an inmense potential for improving our lives in many ways, thus it is important to continue to extend its boundaries and explore its applications in diverse fields.
Ricardo Henao is an Associate Professor for Bioengineering at the BESE division at KAUST, and for Biostatistics and Bioinformatics, and Electrical and Computer Engineering at Duke University. He is a member of the Smart Health Initiative and the Computational Biology Research Center at KAUST. He previously worked as a Postdoctoral associate at Duke University and the University of Copenhagen. The theme of Professor Henao's research is the development of novel statistical methods and machine learning algorithms primarily based on probabilistic modeling. His methods research focuses on hierarchical or multilayer probabilistic models to describe complex data, such as that characterized by high-dimensions, multiple modalities, more variables than observations, noisy measurements, missing values, time-series, multiple modalities, etc., in terms of low-dimensional representations for the purposes of hypothesis generation and improved predictive modeling. His recent work has been focused on the development of sophisticated machine learning models, including deep learning approaches, for the analysis and interpretation of clinical and biological data with applications to predictive modeling for diverse clinical outcomes.