Daniel Molitor

Daniel is a PhD student in Information Science. His interests lie in the intersection of Machine Learning and High Performance Computing, particularly as they pertain to Causal Inference and their applications in public policy. Prior to Cornell, he received his B.A. in Mathematics and Economics at Bethel University and worked as a Research Fellow at tech-for-social-impact nonprofit Research Improving People's Lives (RIPL).

Areas of Interest: Machine Learning, Causal Inference, Data Science