Addison Hu

About

I am a Senior Data Scientist at Waymo, where I work on Safety Evaluation. Our team's role is to bring formal statistical rigor to the release process for the Waymo Driver.

Previously, I was Senior Scientist at Latitude AI. In November 2023, I received a PhD in Statistics and Machine Learning from Carnegie Mellon University, where I was lucky to be advised by Ryan Tibshirani. I spent the final year and a half of graduate school as a visiting researcher at the UC Berkeley Department of Statistics. Prior to all this, I received a BS in Statistics from Yale University and spent some time working at Facebook.

Interests

My work at Waymo involves elements of rare event modeling, uncertainty quantification, and causal inference.

My primary focus as a graduate student was to understand 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 provided a comprehensive treatment of the zeroth-order case. Follow-up projects driven by my collaborators examine the first-order case (forthcoming) and higher-order cases (aspirational). In each of these projects, we consider the estimation problem from the key angles: statistical theory, efficient computation, practical usage.

My secondary focus in graduate school was 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 and continued to work on related problems, including forecasting influenza for CDC FluSight.

Papers

Ordered by time of completion.

Jeremy Goldwasser, Addison Hu, Alyssa Bilinski, Daniel McDonald, and Ryan Tibshirani. Estimating time-varying epidemic severity rates with adaptive deconvolution. To appear in the Annals of Applied Statistics, 2026.
Jeremy Goldwasser, Addison Hu, Alyssa Bilinski, Daniel McDonald, and Ryan Tibshirani. Challenges in estimating time-varying epidemic severity rates from aggregate data. 2024.
Mathis et al. Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations. Nature Communications, 2024.
Addison Hu, Alden Green, and Ryan Tibshirani. The Voronoigram: Minimax estimation of bounded variation functions from scattered data. 2022.
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Addison Hu, and Ryan Tibshirani. Multivariate trend filtering for lattice data. Annals of Statistics, 2024.
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. Advances in Statistical Climatology, Meteorology and Oceanography, 2024.
Addison Hu and Sahand Negahban. Minimax estimation of bandable precision matrices. Advances in Neural Information Processing Systems, 2017.

* denotes equal contribution.

Awards

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

Service

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.

Personal

I am personally partial towards Robert Tibshirani's philosophy of life.