UMN Machine Learning Seminar: How to improve healthcare AI? Incorporating multimodal data and domain knowledge

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Thursday from 12 p.m. - 1 p.m. during the Fall 2021 semester.

This week's speaker, Irfan Bulu (UnitedHealth Group), will be giving a talk titled "How to improve healthcare AI? Incorporating multimodal data and domain knowledge."

Abstract

Healthcare data is special. Its complex nature is a double-edged sword, possessing great potential but also presenting many difficulties to overcome. For example, administrative claims data —in contrast to the common data types (text, vision, audio) where AI has made eye-popping advances—is multi-modal (i.e., consisting of distinct data types including medical claims, pharmacy claims, and lab results), asynchronous (medication histories and diagnosis histories need not be aligned in time), and irregularly sampled (we only collect data when an individual interacts with the system). Along with such rich and complex data, there is a great deal of domain knowledge in various forms in the healthcare field. In this talk, I will present our work on deep learning architectures for incorporating multimodal data and domain knowledge into models.

Biography

I received a Ph.D. in Physics from Bilkent University in 2007. The focus of my Ph.D. work was novel structures such as photonic crystals, plasmonic devices, and metamaterials for controlling the flow of light. I joined Prof. Marko Loncar’s lab at Harvard University for postdoc after completing Ph.D. There, I tackled problems and challenges in communication security and communication bandwidth using diamond nano-photonic structures. In 2013, I took a career in industrial research at Schlumberger, the largest oil field services company, which started an exciting journey for me in taking innovations from lab to products at the hands of customers. For example, our team invented a new nuclear magnetic resonance logging tool, which improved logging speed by an order of magnitude, thereby addressing an important challenge for our customers in adopting nuclear magnetic resonance measurements. This work also led me to a career in machine learning as both the design of the instrument and interpretation of various measurements in oil field benefited from advances in deep learning. I joined United Health Group in 2018, where I research machine learning algorithms for healthcare applications.