My research leverages techniques from AI, machine learning, and computational cognitive science to predict human behavior under risk and uncertainty.
This page details some of my experience across academia and industry.
This award was granted to Erik Schlicht for achieving a 4.9/5.0 overall student rating for a Research Methods course he instructed at Harvard University.
Methods for quantifying an entity's reaction to one or more communication signals by quantifying a probabilistic relationship...
Dr. Schlicht is currently working as a Sr. Research Scientist at Dataminr, developing algorithms that improve the efficiency of HiTL operations. More specifically, he created unsupervised methods for workflow analysis and deployed NLP algorithms that are used for the intelligent allocation of tasks to human analysts. Currently, he is developing algorithms for identifying and mitigating the impact of disinformation, in addition to deriving and evaluating models for estimating the risk associated with physical and human assets, given real-time alerting information.
Dr. Schlicht 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. He was developing the Brain Barn Series of games that contain modules capable of quantifying player perception, decision-making and movements.
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.
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.
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.
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.
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.
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, and resulted in a publication that was rated in the Top 5 Percent of all research output according to metrics by Altmetric.
Major: Cognitive and Brain Sciences; Minor: Human Factors
Major: Psychology; Minor: Biology