Hello, I'm Junier, a 2nd year Ph.D. student at Carnegie Mellon's Machine Learning Department co-advised by Barnabás Póczos and Jeff Schneider.
Currently I'm working on performing machine learning tasks when inputs, and possibly outputs, are complex objects like sets, distributions, or functions. Furthermore, I'm interested in analyzing massive datasets, both in terms of instances and covariates. This work will help us solve problems like predicting whether a Twitter trending topic will go viral, or predicting the risk of disease given a person's functional brain data, or predicting the future distribution of dark matter particles.
I have preference for simple estimators that make few assumptions; in particular I'm interested in (frequentist) non-parametric methods. One very interesting challenge is scaling up non-parametric methods to huge datasets and high dimensions.
Prior to beginning my Ph.D. program, I received my B.S. and M.S. in Computer Science from Carnegie Mellon University. I also spent a year as a software engineer for Yahoo!.
Areas and interests:
Nonparamatric Methods, High-dimensional Data, Large-scale Learning, Functional Analysis