I'm the Executive Director of Covid Watch, an open source nonprofit I founded in February 2020 with a mission -- to empower people with technology to fight the pandemic and defend digital privacy. Our team has more than 650 volunteers from around the world including advisers in public health, epidemiology, privacy, policy, and law from universities like Stanford, Waterloo, UWash, UCSF, and Berkeley.

The Covid Watch team was the first in the world to publish a white paper, develop, and open source anonymous exposure alert technology using Bluetooth in early March. Our TCN Protocol received significant news coverage and was followed by the development of very similar decentralized protocols in early April like DP-3T, PACT, and Google/Apple exposure notification. Covid Watch volunteers and nonprofit staff also built a fully open source mobile app solution for sending anonymous exposure alerts.

Covid Watch started with a forum post on the Effective Altruist Forum that I made in the early days of the pandemic. Prior to founding Covid Watch, I was involved in Stanford's Effective Altruism and AI Safety discussion groups.

I care about aligning technology with human rights and values.

Technical Projects

Covid Watch puts the power to stop Covid-19 in the palm of your hand

The mission of our open source nonprofit is to empower people with technology to fight the pandemic and defend digital privacy. Covid Watch started as an independent research collaboration between Stanford and Waterloo, and was the first in the world to publish a white paper, develop, and open source an anonymous, decentralized Bluetooth protocol. Covid Watch was also the first in the U.S. to pilot an app using Google/Apple exposure notification framework. covidwatch.org link Covid Watch github github TCN Protocol github github

A demo webpage rating button to reward content creators and build an ad-free ecosystem

This is a browser extension that lets you rate any webpage and an associated search engine with confidence scores based on positive and negative emotional valence. I used the same scoring algorithm as reddit's "top" score plus noise to help elevate new webpages. This is an alternative to click-based models to help combat the excessive rewarding of polarizing content. A paid version could reward content creators you have rated positively to help reduce reliance on ads. Demo at Upvote.vote. link

Research Background

I originally worked in aerospace engineering, completing my M.S. at the University of Arizona. While at Honeywell Aerospace, I invented and patented a method for eliminating transonic flutter in turbomachinery, influencing the design of next generation jet engines. After spending several years in industry, I decided to pursue a PhD at Stanford supported by a Department of Energy Computational Science Graduate Fellowship (DOE CSGF).

At Stanford, I focused on courses in computer science and machine learning. I developed two novel methods for faster and more accurate reduced order models and I designed neural network architectures for approximating the solutions to partial differential equations by breaking them into parts. My long term research goal is to understand how to automatically draw boundaries in spatial and temporal data in a way that is useful for prediction and explanation.

Research Highlights

Publications, Patents, and Technical Reports

White, Cristina. Comparing Reduced Order Models and Neural Networks for Predictions from Constrained Aerodynamics Datasets. Stanford. Dissertation In Progress.

Reed, H., et al. "Individual Data Rights for Exposure Notification." Data Rights Framework at exposurenotification.org. TCN Coalition White Paper, April 2020. link

Petrie, J. & White, C., et al. "Slowing the Spread of Infectious Diseases Using Crowdsourced Data." Stanford and Waterloo Independent Research Project. Covid Watch White Paper, March 2020. paper link link

White, Cristina, et al. "Fast Neural Network Predictions from Constrained Aerodynamics Datasets." AIAA Scitech Forum, 2020. American Institute of Aeronautics and Astronautics, AIAA, 2020. arxiv link paper poster github

White, Cristina R. A neural network for breaking time dependent problems into parts. Technical report. CS379C. Department of Mechanical Engineering, Stanford University (2018) paper github

White, C., & Farhat, C. & Avery, P. A Spatial Clustering Algorithm for Constructing Local Reduced order Bases for Nonlinear Model Reduction. Presentation at the annual meeting of U.S. National Congress on Computational Mechanics, USNCCM14, Montreal, Canada. July 2017. talk poster talk paper github

White, Cristina R. A neural network architecture for reduced order modeling of PDEs. Technical report. CS221. Department of Mechanical Engineering, Stanford University (2016) paper poster github

White, Cristina R. Unsupervised Learning of Time-Dependent CFD Solutions using LSTMs. Technical report. CS231N. Department of Mechanical Engineering, Stanford University (2016) paper poster github

White, Cristina R. A reduced order modeling method for improving online computation time and accuracy using mesh coarsening. Technical report. AA290. Department of Mechanical Engineering, Stanford University (2016) paper talk github

White, Tina R. A Clustering Algorithm for Reduced Order Modeling of Shock Waves. Technical report. CS229. Department of Mechanical Engineering, Stanford University (2015) link paper github

White, Cristina. Flutter-resistant transonic turbomachinery blades and methods for reducing transonic turbomachinery blade flutter. EP 2907972A1, European Patent Office, 19 August 2015. link patent

White, Cristina. Flutter-resistant transonic turbomachinery blades and methods for reducing transonic turbomachinery blade flutter. US 20150233390A1, United States Patent and Trademark Office, 14 February 2014. link patent

Seele, Roman, et al. "Some effects of blowing, suction and trailing edge bluntness on flow separation from thick airfoils; computations & measurements." 29th AIAA Applied Aerodynamics Conference. 2011. link paper

White, Cristina. Application of Computational Fluid Dynamics on a Blunt Elliptical Airfoil. Master's Thesis. University of Arizona, 2009. thesis talk

Zakharin, Boris, et al. "The utility of hysteresis for closed-loop control applications that maintain attached flow under natural post stall conditions on airfoils." 4th Flow Control Conference. 2008. link paper

Talks, Panels, and Interviews

UArizona testing anonymous app that notifies people exposed to COVID-19. 3TV/CBS 5 Interview By Emma Lockhart. July 23rd, 2020. audio

University Of Arizona Alum Develops Confidential Contract Tracing App. KJZZ Interview By Lauren Gilger. July 22nd, 2020. audio

Decentralized exposure alert protocols for protecting communities from COVID-19. Toronto Machine Learning Series (TMLS). May 28th, 2020. video

Warning Tools for COVID Exposure: Telephone Town Hall. Sen. Glazer Town Hall. May 21st, 2020. audio

Sophia Life: Interview with AI Researcher Tina White. Sophia The Robot. May 7th, 2020. video

This App Protects Privacy While Tracing Covid-19 Infections. Reason Interview with Nick Gillespie. May 6th, 2020. audio

The Role of Technology in Stopping the Spread of Covid-19. Tony Blair Institute for Global Change. April 30th, 2020. video

Privacy Preserving Contact Tracing for COVID-19. Foresight Institute. April 2nd, 2020. video

Tracking COVID-19 Using Crowdsourced Data. Stanford HAI's COVID-19 and AI Virtual Conference. April 1st, 2020. video

Tracking COVID-19 with an App? Radio Sputnik Coronavirus Updates. March 13th, 2020. video


Woman of Influence. Silicon Valley Business Journal. 2020. link link

Early Response Prize for Fighting Covid-19 and Improving App Privacy. Emergent Ventures. 2020. link

Computational Science Graduate Fellowship (DOE CSGF). US Department of Energy. 2016-2020. link link link

Research and Teaching Assistantships. Stanford University. 2015 - 2016.

National Technical Achievement Award. Honeywell Aerospace. 2015.