## About

I am a Senior Data Scientist at [Waymo](https://waymo.com), 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](https://lat.ai).
In November 2023, I received a PhD
in [Statistics](https://www.cmu.edu/dietrich/statistics-datascience/index.html)
and 
[Machine Learning](https://www.ml.cmu.edu) from Carnegie Mellon University,
where
I was lucky to be advised by [Ryan Tibshirani](https://www.stat.berkeley.edu/~ryantibs/).
I spent the final year and a half of graduate school as a visiting researcher
at the
[UC Berkeley Department of Statistics](https://statistics.berkeley.edu). 
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](pdfs/AddisonHu_Thesis.pdf),
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](https://arxiv.org/abs/2212.14514).  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](https://delphi.cmu.edu) on an emergency basis
to help produce [real-time Covid-19
indicators](https://www.pnas.org/doi/full/10.1073/pnas.2111452118)
and [forecasts](https://zoltardata.com/model/307).  I found the work
compelling and continued to work on related problems, including [forecasting
influenza](https://github.com/cdcepi/Flusight-forecast-data/tree/master/data-forecasts/CMU-TimeSeries)
for [CDC
FluSight](https://www.cdc.gov/flu/weekly/flusight/index.html).

## 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.](https://arxiv.org/pdf/2510.16180)
  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.](https://www.medrxiv.org/content/10.1101/2024.12.27.24319518v1.full.pdf)
  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.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10760285/)
  _Nature Communications_, 2024.

* Addison Hu, Alden Green, and Ryan Tibshirani. 
  [The Voronoigram: Minimax estimation of bounded variation functions from scattered data.](https://arxiv.org/abs/2212.14514)
  2022.

* Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Addison Hu, and Ryan Tibshirani. 
  [Multivariate trend filtering for lattice data.](https://arxiv.org/pdf/2112.14758) 
  _Annals of Statistics_, 2024.

* Daniel McDonald, Jacob Bien\*, Alden Green\*, Addison Hu\*, et al.  [Can
  auxiliary indicators improve COVID-19 forecasting and hotspot 
  prediction?](https://www.pnas.org/doi/full/10.1073/pnas.2111453118) 
  _Proceedings of the National Academy of Sciences_, 2021.

* Reinhart et al.  [An open repository of real-time COVID-19 
  indicators.](https://www.pnas.org/doi/full/10.1073/pnas.2111452118)
  _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.](https://www.pnas.org/doi/10.1073/pnas.2113561119)
  _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.](https://arxiv.org/abs/2012.15130)
  _Advances in Statistical Climatology, Meteorology and Oceanography_, 2024.
  
* Addison Hu and Sahand Negahban.  [Minimax estimation of bandable precision
  matrices.](https://arxiv.org/abs/1710.07006)  _Advances in Neural
  Information Processing Systems_, 2017.

\* denotes equal contribution.

## Awards

I am grateful to have been supported by an [NSF GRFP](https://www.nsfgrfp.org/)
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](https://tibshirani.su.domains/wisdom.html).