Addison Hu

About

I am a fifth-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.

Beginning Fall 2022, I am a visiting student researcher at the Department of Statistics at UC Berkeley.

Publications

• Addison Hu, Alden Green, and Ryan Tibshirani. The Voronoigram: Minimax estimation of bounded variation functions from scattered data. Submitted, 2022.

• Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Addison Hu, and Ryan Tibshirani. Multivariate trend filtering for lattice data. Submitted, 2021.

• Daniel McDonald, Jacob Bien, Alden Green, Addison Hu, et al.
  Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction? Proceedings of the National Academy of Sciences, 2021.

• Reinhart et al. An open repository of real-time COVID-19 indicators. Proceedings of the National Academy of Sciences, 2021.

• Cramer et al. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.. Proceedings of the National Academy of Sciences, 2022.

• Addison Hu, Mikael Kuusela, Ann Lee, Donata Giglio, and Kimberly Wood. Spatio-temporal methods for estimating subsurface ocean thermal response to tropical cyclones. Submitted, 2020.

• Addison Hu and Sahand Negahban. Minimax estimation of bandable precision matrices. Advances in Neural Information Processing Systems, 2017.

Awards

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

Projects

Epidemic tracking and forecasting

In March 2020, I joined the Carnegie Mellon University's Delphi group on an emergency basis to aid in Covid-19 tracking and forecasting. During that time, I helped develop and wrote core pipelines for real-time Covid-19 indicators, which were made available to the general public. We also used these indicators to produce Covid-19 forecasts.

Since Summer 2021, I have stepped back into a contributor role, occasionally assisting with projects. In Fall 2021 I helped produce influenza forecasts for the 2021-2022 flu season, which we submitted to CDC FluSight.

newton-sketch

An implementation of Newton Sketch: A Linear-time Optimization Algorithm with Linear-Quadratic Convergence in NumPy and Spark.

Professional Experience

Data Scientist, Facebook Seattle | July 2017 - August 2018

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