Justin Eldridge is a PhD candidate in machine learning at the Ohio State University, advised by Mikhail Belkin and Yusu Wang. His research interests lie in the theory of clustering. In particular, his recent work has focused on identifying the "correct" clustering of a model, defining what it means for a method to properly recover this clustering from data, and demonstrating that such correct clustering algorithms exist. He is the recipient of the best student paper award at the 2015 Conference on Learning Theory, and his recent work with Belkin and Wang on graphon clustering was awarded a full oral presentation at NIPS 2016. Eldridge completed his undergraduate studies at Ohio State, where he received degrees in physics and applied mathematics.
- Foundations of Machine Learning, Spring 2017. Visiting Graduate Student.