Jesse is a research scientist in ACTNext’s AI and Machine Learning team. His research is focused on developing machine learning models to assess Collaborative Problem Solving (CPS) skills, using data gathered from a wide array of sources like audio, video, and educational games.

Jesse has cultivated interests in a wide array of subjects. In 2013, he graduated from the University of Missouri – Kansas City with his BA in Mathematics, and minors in both Computer Science and French. From there, he attended the University of Iowa where he obtained his PhD. in Mathematics in Fall 2018. His dissertation work was in low-dimensional topology and knot theory, where he developed methods for detecting certain properties of knots called positivities. During Summer 2018, he was an NSF Mathematical Sciences Graduate Intern at Fermi National Accelerator Laboratory (Fermilab), where he studied the architecture of certain computer vision models called convolutional neural networks. Following that, he was a Summer 2019 Insight Data Science fellow, where he applied his knowledge of Natural Language Processing (NLP) to explore topic modeling pipelines on legal documents.

Jesse is excited to be working at ACTNext, where he can leverage his diverse background in mathematics and machine learning, as well as his curious spirit, to make a meaningful difference on the cutting edge of education.