Meeting Recording:

https://web.microsoftstream.com/video/c84847fc-b1d2-4ec2-a7c3-5243f063cc3f

Talk Title:

Learning Interpretable Models on Complex Medical Data

Abstract:

Machine learning as a field has become more and more important due to the ubiquity of data collection in various disciplines. Coupled with this data collection is the hope that new discoveries or knowledge can be learned. My research spans both fundamental research in machine learning and their application to biomedical imaging, health, science and engineering. Multi-disciplinary research is instrumental to the growth of the various areas involved. In many applications, data is often complex, high-dimensional and multi-faceted, where multiple possible interpretations are inherent in the data. Fortunately, domain scientists often have rich knowledge that can guide data driven methods. Thus, it is important to enable incorporation of domain input into the design of algorithms. Furthermore, for clinicians and domain scientists to trust and use the results of learning algorithms, not only are models necessary to be accurate but it is also imperative for learning models to be interpretable. In this talk, I highlight these challenges through our experience in collaborative research working on discovering disease subtypes and then provide examples of how these challenges led to innovations in machine learning and to new discoveries. I will then provide an overview of our other novel machine learning algorithm development projects that are inspired by complex medical data.