His research focuses on the interaction between how learners use machine learning and AI systems to make decisions and its impact on educational outcomes. He has experience with understanding learners' privacy perceptions surrounding educational technologies and large data sets, and is interested in how transparency, in terms of what data is collected and how it is used in models, can influence the way people think. Through this work, he has developed and deployed student-facing tools such as open learner models and reflection activities for AI-generated feedback in order to support metacognition. He is also an educator who has taught multiple programming courses and developed data science curricula for both residential and online degree programs.

Warren holds a Bachelor of Arts in Mathematics and a Bachelor of Science in Physics from the University of Missouri - St. Louis. He recently completed his Ph.D. at the University of Michigan in the School of Information and was a National Science Foundation (NSF) Graduate Fellow.