I am a Director of Data Science in payment integrity for commercial clients at Optum, a part of UnitedHealth Group. This means that I help create models to detect and help prevent fraud, waste, abuse, and error in medical billing for large healthcare clients. Since labeled data can be limited, I am interested in combining unsupervised, semi-supervised, and supervised methods to achieve the best possible results. More generally, I am interested in solving urgent, real world problems by applying state-of-the-art machine learning algorithms, inventing them when needed.
One recent project was the creation of a supervised model that led to tens of millions of dollars in savings. In other work, I modernized a large legacy code base consisting of tens of thousands of lines of PL/SQL code by creating a modern Python and PySpark implementation. I also created a real-time version of the model by breaking out the computation into periodic computations and real-time look-ups. I have planned and am executing on a roadmap of modeling improvements to include deep learning components within a model explainable in medical/clinical terms.
In addition to my technical role, I also lead a small team of data scientists, directing their work, mentoring their technical development, and evaluating their performance.
In the recent past, I worked on cyber security and cyber-physical security. Cyber systems have peculiarities that often require new algorithms. For example, much of the data collected is computer-generated, discrete, and structured. Also, many of the underlying structures are or occur within networks. Furthermore, these systems must operate in adversarial scenarios where simple fault tolerance and reliability analyses cannot properly account for the planning and intelligence of adversaries. I explored the development of new algorithms that exploit the particulars of cyber problems to improve defensive and offensive capabilities. My later focus was on situation awareness from network sensor data and protection of the power grid.
I worked at ORNL between 2009 and 2017. In addition to being a research scientist, I was also the team lead for research-operations integration where I supported the operational deployment of research results. Previously, I worked as a cryptologic researcher for over 10 years. I earned my Ph.D. in Mathematics from the University of Michigan, Ann Arbor in 2003 (defending my thesis just hours before the Northeast blackout).
My research focus is in the effective use of machine learning methods to address real, operational problems. Many amazing results have been achieved recently using deep learning, but most of these are within the domains of images, voice, and text. In many applications, such as medical claims data and cyber security, the idiosyncrasies of the data render inappropriate the routine application of advanced algorithms. I am especially interested in determining the best analytics for addressing any given problem, whether it be from deep learning, other machine learning methods, optimization, probabilistic modeling, or game theory.