×
About Highlights Nonprofit Media In the News Research Papers Awards

About

I started my career in aerospace engineering. While a PhD candidate at Stanford, I studied artificial intelligence and developed neural network architectures for scientific simulations. During the pandemic, I ran a nonprofit to help fight covid with decentralized technology. I care about aligning technology with human rights and values.

Highlights

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 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 to help reduce reliance on ads. Demo at linkUpvote.vote.

Nonprofit

I was the Executive Director of Covid Watch, a nonprofit I started in February 2020 with an EA Forum post. Our mission was to improve the privacy of the contact tracing apps that were being released in response to the pandemic.

We were the first team in the world to publish a white paper, develop, and open source a decentralized exposure notification protocol using Bluetooth communication in March 2020. Within apps that implement the security protocol, data is stored locally on personal devices, and it stays anonymous throughout the notification process. The system is designed to prevent corporations and governments from using the apps for the centralized collection of personally identifiable information. Our TCN Protocol received significant news coverage and was followed by the development of similar privacy-preserving protocols in early April like DP-3T, PACT, and Google/Apple GAEN.

In April 2020, our volunteers and nonprofit staff helped develop an early data rights framework for exposure notification, and released a free and open source mobile app with development costs funded by prizes and donations. In August 2020, we collaborated with the University of Arizona on research to improve the estimation of infection risk from decentralized Bluetooth data to better inform private quarantine recommendations.

At the end of 2020, the Covid Watch nonprofit closed, but the open source Covid Watch app continues to be implemented for universities and public health departments by WeHealth, a public benefit corporation.

Download Whitepaper

Media

In the News

Research

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 reducing the risk of transonic flutter in turbomachinery, improving the safety 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.

Space-local reduced-order bases for accelerating reduced-order models through sparsity

A space-local reduced-order basis is developed, which introduces sparsity in projection-based model order reduction (PMOR) by partitioning the computational domain rather than, or in addition to, the solution manifold. Acceleration factors of 1.5 relative to traditional PMOR and CPU time speedup factors of several orders of magnitude relative to high-dimensional models are shown for two turbulent computational fluid dynamics problems. IJNME Conference Paper, Pending 2022. paper

Quantifying SARS-CoV-2 infection risk within the Google/Apple exposure notification framework to inform quarantine recommendations

Most Bluetooth-based exposure notification apps use three binary classifications to recommend quarantine following SARS-CoV-2 exposure. Instead, we model uncertainty in the shape and orientation of an exhaled virus-containing plume and in inhalation parameters, and measure uncertainty in distance as a function of Bluetooth attenuation. U. Arizona and Covid Watch Risk Analysis Paper June 17, 2021. paper

Publications, Patents, and Technical Reports

Anderson, S., White, C., and Farhat, C. "Space-local reduced-order bases for accelerating reduced-order models through sparsity." International Journal for Numerical Methods in Engineering, Pending 2022. IJNME

Wilson, A.M., Aviles, N., Petrie, J.I., Beamer, P.I., Szabo, Z., Xie, M., McIllece, J., Chen, Y., Son, Y.-J., Halai, S., White, T., Ernst, K.C. and Masel, J. "Quantifying SARS-CoV-2 Infection Risk Within the Google/Apple Exposure Notification Framework to Inform Quarantine Recommendations." doi: https://doi.org/10.1111/risa.13768 , Risk Analysis. June 2021. medRxiv

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, January 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

Awards

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

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

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

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

National Technical Achievement Award. Honeywell Aerospace. 2015.