contact: mail@huisaddison.com
cv: download pdf
github: repositories
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.
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.
I am grateful to be supported by an NSF GRFP award in Mathematical Statistics.
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.
An implementation of Newton Sketch: A Linear-time Optimization Algorithm with Linear-Quadratic Convergence in NumPy and Spark.
I worked on Search Core Relevance, specifically an element of the Facebook search engine responsible for retrieving specific posts previously seen by a user.