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.
There are GPU-accelerated fluid dynamics apps to play with now. I wanted one that allows you to upload your own images as dye ink to create fluid dynamic artwork, so I modified a repository to easily make flowing artwork. Play here. It's free to play and make fluid dynamic art. Works on desktop and mobile. Example artwork on github. github
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 Upvote.vote. link
For flutter caused by a transonic shockwave, adding local positive camber to the blade design near the predicted shockwave location is found to eliminate flutter with negligible impact on aerodynamic efficiency. "Flutter-resistant transonic turbomachinery blades and methods for reducing transonic turbomachinery blade flutter." Honeywell Aerospace Patent. link patent
I was the Executive Director of Covid Watch, a nonprofit I started in February 2020 with a mission 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 also released a free and open source mobile app for sending anonymous exposure notifications with development costs funded entirely by prizes and donations. In August 2020, we collaborated with the University of Arizona on research to improve the estimation of infection risk from anonymous 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.
The Role of Technology in Stopping the Spread of Covid-19. Tony Blair Institute for Global Change. April 30th, 2020.
Warning Tools for COVID Exposure: Telephone Town Hall. Sen. Glazer Town Hall. May 21st, 2020.
Tracking COVID-19 Using Crowdsourced Data. Stanford HAI's COVID-19 and AI Virtual Conference. April 1st, 2020.
Sophia Life: Interview with AI Researcher Tina White. Sophia The Robot. May 7th, 2020.
Decentralized exposure alert protocols for protecting communities from COVID-19. Toronto Machine Learning Series (TMLS). May 28th, 2020.
Approximating Solutions to Fluid Dynamics Problems & A Mobile App Intervention for COVID-19. Department of Energy CSGF Program Review. July 14th, 2020. link
Privacy Preserving Contact Tracing for COVID-19 | Peter Eckersley. Foresight Institute. March 28th, 2020.
Coronavirus Updates | Tracking COVID-19 with an App? w/ Tina White. Populist TV Radio Sputnik. March 13th, 2020.
University Of Arizona Alum Develops Confidential Contract Tracing App. KJZZ Interview By Lauren Gilger. July 22nd, 2020.
This App Protects Privacy While Tracing Covid-19 Infections. Reason Podcast with Nick Gillespie. May 6th, 2020.
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've 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 interpret and understand how to automatically draw boundaries in data in a way that is useful for prediction and explanation.