At Dataminr, I created unsupervised methods for workflow analysis and deployed NLP algorithms that are used for the intelligent allocation of tasks to human analysts, in addition to deploying an algorithm for detecting misinformation across multiple domains, such as COVID and Elections. Finally, I was responsible for deriving and evaluating models for estimating the risk associated with physical assets, given real-time alerting information.

I was the Founder of the BrainCallus Gaming Project and was responsible for all technical and business aspects of the company. The BrainCallus Gaming Project was an effort seeking to improve psychiatric decision-making by leveraging a combination of computational gaming, machine learning and cognitive science. I developed the Brain Barn Series of games that contain modules capable of quantifying player perception, decision-making and movements. These metrics are useful for quantifying symptoms of psychiatric disorders and can be used as features for machine learning algorithms that predict clinically relevant information.

During my tenure as founder at C2-g, I gained attention for leveraging multifidelity methods and computational gaming to design decision-support systems. I also developed a novel model to improve the prediction of NFL outcomes by exploiting oddsmaker decision biases.

I returned to the University of Minnesota to conduct research in the HumanFIRST Lab where I developed machine learning algorithms (e.g., Bayesian Networks, Support Vector Machine regression, Binary classification with Lasso) to predict human driving behavior. These models were then used to estimate the risk associated with candidate transportation technology by using the predictive models in multifidelity simulation.

At Medtronic, I was part of a team that was responsible for developing next-generation Deep-Brain-Stimulation devices to help treat diseases, such movement disorders.

I was a researcher at MIT Lincoln Laboratory conducting research related to national security. I was responsible for developing a novel model to predict the decisions of interacting humans. The model defined a quantitative method to combine the results from low-fidelity simulations (e.g., novice in an online simulator) with high-fidelity simulations (e.g., expert in an immersive simulator) to evaluate when inexpensive low-fidelity data can be used to as a proxy for expensive high-fidelity simulations. Moreover, I was part of an effort to use Serious Games as a means to develop quantitative models of operational decision-making.

I was a Cognitive Scientist at Aptima and led several SBIR and STTR efforts on projects related to national security. In my brief time at Aptima, I was awarded one OSD contract for a biologically-inspired approach to automated scene estimation (BIS-E), in addition to successfully securing one patent for quantifying human reactions to communications.

While a postdoctoral researcher between Harvard University and Caltech, I developed a low-fidelity game to quantitatively investigate human decision-making in a competitive (zero-sum) task. This research received an enormous amount of public interest and has been covered by several major media outlets and resulted in a publication that was rated in the Top 5 Percent of all research output according to metrics by Altmetric.

I completed a doctoral degree in Cognitive and Brain Sciences with a minor in Human Factors. My thesis was titled, "Statistical Decision-Theory for Human Perception-Action Cycles."

I completed my B.S. degree with a major in Psychology and Minor in Biology.