Addison Hu

cv: pdf
github: repositories


I am a final-year PhD student pursuing a joint degree in the Departments of Statistics and Machine Learning at Carnegie Mellon University. I am lucky to be advised by Ryan Tibshirani.

I am currently also a visiting student researcher in the Department of Statistics at UC Berkeley.


My work touches on statistical methodology, nonparametric theory, and optimization. My main focus at this time is understanding the use of total variation penalties in the scattered data, d-dimensional setting, which can be thought of as a multivariate generalization of locally adaptive regression splines or trend filtering. My collaborators and I have provided a treatment of the zeroth-order case, and we are currently working on the first-order case. In each of these projects, I consider the estimation problem from different angles: statistical rates, efficient computation, practical usage.

I also have an applied interest in computational epidemiology. At the beginning of the Covid-19 pandemic, I joined CMU's Delphi group on an emergency basis to help produce real-time Covid-19 indicators and forecasts. I found the work compelling enough to continue working on related problems, including forecasting influenza for CDC FluSight.


Ordered by time of completion.

* denotes equal contribution.


I am grateful to be supported by an NSF GRFP award in Mathematical Statistics.


I have served as a referee/reviewer for the Annals of Statistics; Journal of Machine Learning Research; Journal of Computational and Graphical Statistics; and Neural Information Processing Systems.

Professional Experience

Data Scientist, Facebook | July 2017 - August 2018

During a pre-grad-school sojourn, I worked on Search Core Relevance, specifically an element of the Facebook search engine responsible for retrieving specific posts previously seen by a user.