About

I am a systems theorist and machine learning researcher. I develop and apply systems theory to bridge systems engineering and artificial intelligence. I graduated with my Ph.D. in 2021 ( thesis ) and my B.Sc. in 2018, both in systems engineering, both from the University of Virginia.

I am currently an Assistant Research Professor and the Head of the Responsible General Intelligence (RGI) Lab at Virginia Tech.

Please see my Google Scholar for an updated list of publications or my LinkedIn for an updated professional history.

For fun, I play tennis, ski, and play guitar.

Skiing near Seattle, Washington
(March 2022)

Research and Engineering

I use abstract systems theory as a meta-theory for learning in order to study phenomena of interest in general learning processes. My motivation is to ellicit principles for design and operation by (1) studying learning systems in general terms and (2) by connecting learning theory to the broader body of systems research.

My research is applied to machine prognostics, telecommunications, computer networks, fraud detection, and computer vision, among other areas, following themes of change and reuse, lifecycles, and iterated games.

I posit that abstract systems theory offers an advantageous means of considering learning without explicit reference to solution methods, and, moreover, that it is able to stratify the assumptions underlying learning phenomena into appreciable levels of abstraction (link).

The general systems nature of learning cannot be escaped, solution methods necessarily cannot escape it, and so my motivation, in essence, follows Capablanca's advice...

"In order to improve your game, you must study the endgame before everything else, for whereas the endings can be studied and mastered by themselves, the middle and the opening must be studied in relation to the endgame."
- Jose Raul Capablanca

At the National Academy of Science for an invited round-table on AI advancements (October 2023)

Invited Talks

Selected Publications