Erik J Schlicht

Erik J Schlicht, PhD

Data Scientist

My research leverages techniques from AI, machine learning, and computational cognitive science to predict human behavior under risk and uncertainty. Explore this site to learn about my experience in academia and industry. Thanks for visiting.


General Information

Please feel free to contact me with questions about my research or if you're interested in collaborating.

  • Address : St. Paul, MN
  • Email :
  • Phone : 1.617.999.9031

My Skills

  • Data Science

  • Machine Learning

  • Artificial Intelligence

  • Data Analysis

  • Statistical Programming

Research Interests

Decision Support

I am interested in improving decision-making through decision support systems.


I have experience developing computational games that quantify human performance.


I enjoy developing machine learning and AI algorithms across several domains.


I have a keen interest in modeling human performance under uncertainty and risk.

Invited Talks


This page contains a summary of my experience across industry and academia.



  • Founder - BrainCallus Gaming Project


    Dr. Schlicht is currently working as the Founder of the BrainCallus Gaming Project and is responsible for all technical and business aspects of the company. The BrainCallus Gaming Project is an effort seeking to improve psychiatric decision-making by leveraging a combination of computational gaming, machine learning and cognitive science. He is currently developing the Brain Barn Series of games that contain modules capable of quantifying player perception, decision-making and movements.

  • Founder - Computational Cognition Group, LLC

    2016 - 2019

    Dr. Schlicht was the Founder of the Computational Cognition Group (C2-g), LLC and was responsible for all technical and business aspects of the company. During his tenure as founder, he gained attention for leveraging multifidelity methods and computational gaming to design decision-support systems. He also developed a novel model to improve the prediction of NFL outcomes by exploiting oddsmaker decision biases.

  • Researcher - University of Minnesota

    2015 - 2016

    Dr. Schlicht returned to the University of Minnesota to conduct research in the HumanFIRST Lab where he 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.

  • HF Engineer - Medtronic

    2014 - 2015

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

  • Technical (Research) Staff - MIT Lincoln Laboratory

    2011 - 2014

    Dr. Schlicht was a researcher at MIT Lincoln Laboratory conducting research related to national security. He 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, he was part of an effort to use Serious Games as a means to develop quantitative models of operational decision-making.

  • Cognitive Scientist - Aptima

    2010 - 2011

    Dr. Schlicht was a Cognitive Scientist at Aptima and led several SBIR and STTR efforts on projects related to national security. In his brief time at Aptima, he 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.

  • Postdoctoral Associate - Harvard University and Caltech

    2007 - 2010

    While a postdoctoral researcher between Harvard University and Caltech, Dr. Schlicht 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 (see list below), and resulted in a publication that was rated in the Top 5 Percent of all research output according to metrics by Altmetric.

  • Course Instructor - Harvard, Wellesley, UMN

    Various Dates

    Dr. Schlicht has instructed several undergraduate courses at the University of Minnesota, Wellesley College, and Harvard University. In 2009, he was awarded the Certificate of Teaching Distinction from Harvard University.



This page contains an assortment of publications and preprints of my work. See my CV for a full list.

  • Predicting NFL Outcomes by Exploiting Decision Bias

    Schlicht, E.J. (2017). Exploiting oddsmaker bias to improve the prediction of NFL outcomes. arXiv: Statistical Applications.

  • Multifidelity Simulation for Transportation

    Schlicht, E.J. & Morris, N. (2017). Estimating the risk associated with candidate transportation technology through multifidelity simulation. arXiv: Statistical Applications.

  • Multifidelity Simulation for Aerospace

    Schlicht, E.J., Lee, R., Wolpert, D., Kochenderfer, M. , & Tracey, B. (2012). Predicting the behavior of interacting humans by fusing data from multiple sources. In the Proceedings of the Twenty-Eighth Conference of Uncertainty in Artificial Intelligence, (UAI-2012). [30% Acceptance Rate]

  • Behavioral Economics: Redefining a Poker Face

    Schlicht, E.J., Shimojo, S., Camerer, C., Battaglia, P.R., & Nakayama, K. (2010). Human wagering behavior depends on opponents faces, PLoS ONE, 5(7): e11663. doi:10.1371/journal.pone.0011663.

  • Statistical Models for Natural Movements

    Schlicht, E.J., & Schrater, P.R. (2007). Impact of coordinate transformation uncertainty on human sensorimotor control. Journal of Neurophysiology, 97(6), pp. 4203-14.

Media Coverage (Abridged)

Scientific American

Scientific American

New York Times - Freakonomics

NY Times

Wellesley Patch

Wellesley Patch

Polygon (Gaming)

Polygon Gaming

ML Conference Interview


Digital Journal

Digital Journal

Market Watch

market watch