Kevin Ellis is a computer scientist working in artificial intelligence and program synthesis, with the goal of better combining reasoning and learning.
Broadly, I’m motivated by the goals of building machine learning systems that generalize strongly (extrapolating rather than interpolating); while requiring less data (greater sample efficiency); and which acquire interpretable knowledge that humans can understand and build on. I draw on ideas and techniques from machine learning, artificial intelligence, programming languages, and cognitive science. More specifically, I’ve investigated the hypothesis that some progress on these fronts can come from program induction. Program induction systems represent knowledge in the form of symbolic code, and treat learning as a kind of program synthesis.
- BS (Physics), Massachusetts Institute of Technology, 2014
- Ph. D. (Computer Science), Massachusetts Institute of Technology, 2020