# Bio (short)

Conrad Borchers is a PhD student at the Human-Computer Interaction Institute (HCII) at Carnegie Mellon University, School of Computer Science, advised by Vincent Aleven and Ken Koedinger. His research advances intelligent systems that support learner persistence, assessment, and educational outcomes through human-centered design and learning analytics. He holds an MSc in Social Data Science from the University of Oxford and a BSc in Psychology from the University of Tübingen.

# Bio (long)

Conrad Borchers is a PhD student at the Human-Computer Interaction Institute (HCII) at Carnegie Mellon University, School of Computer Science, advised by Vincent Aleven and Ken Koedinger. His research advances intelligent systems that support learner persistence, assessment, and educational outcomes through human-centered design and learning analytics. He focuses on AI-supported self-regulated learning in middle and high school students and data-informed course selection in higher education, using methods from machine learning, data mining, and natural language processing to inform scalable educational interventions.

His research has been published in journals and conferences such as Computers & Education, PloS One, The Internet and Higher Education, and the proceedings of the ACM Learning@Scale and Learning Analytics and Knowledge Conferences. His work has received multiple accolades, including three best paper awards.

Borchers holds an MSc in Social Data Science from the University of Oxford, where his thesis on labor market analysis using Stack Overflow data earned the OII Thesis Prize for Best MSc Dissertation, and a BSc in Psychology from the University of Tübingen, Germany. He has also worked as a research intern at the University of California, Berkeley, where he studied data-driven course workload estimations using LMS records and higher education enrollment data under Zachary A. Pardos.