I'm the founder and CEO of Epistamai (the Greek root of epistemology), an early-stage AI startup that is focused on leveraging systems thinking and causal inference to build AI systems for high-stakes decision making in highly regulated industries.

Previously, I worked as a data scientist at the Federal Reserve Bank of Chicago, where I was doing research on algorithmic bias in credit decisions. Through this research, I identified the potential for using causal inference and system dynamics to model and correct for biases in the data generating process. This could give us insights into how to build fair machine learning systems that will not cause illegal discrimination (e.g. disparate treatment and disparate impact).

My current AI research focus is on understanding how to build responsible AI systems by bridging the gap between the social sciences and computer science. In particular, I am interested in using systems thinking to understand the emergent behavior between machine learning, causal inference, and system dynamics. By unifying these previously independent analytical techniques, we may be able to solve AI problems that are currently intractable.

I hold degrees in computer science, electrical engineering, and business from the University of Pennsylvania, Columbia University, and Northwestern University (Kellogg).