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.
Pandemic mitigation is fundamentally an optimization problem. Mobile technologies can provide instantaneous and high accuracy exposure alerts, even between strangers at low social and economic cost. We propose novel technology that performs automatic, anonymous, and decentralized exposure alerts using Bluetooth proximity networks. CEN/TCN Protocol White Paper from March 20, 2020. paper
The cluster network automatically breaks problems into parts while learning to make aerodynamics predictions from very few examples. The datasets are designed to be typical of aerospace industry datasets, which are highly constrained by the number of examples. "Fast Neural Network Predictions from Constrained Aerodynamics Datasets." AIAA Scitech Conference Paper. arxiv link paper poster github
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 aerodynamic artwork, so I modified a repository to easily make flowing art. Play here. It's free to play and make aero/fluid dynamic art. Works on desktop and mobile. github Example artwork on 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 linkUpvote.vote.
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." linkHoneywell Aerospace Patent.
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. If an app implements the security protocol, it ensures that data is stored locally on personal devices, and 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
In the News
Surveillance, AI, and saving lives top agenda at coronavirus conference. Khari Johnson. April 2nd, 2020.
Behind the global efforts to make a privacy-first coronavirus tracking app. David Ingram and Jacob Ward. April 7th, 2020.
Clever Cryptography Could Protect Privacy in Covid-19 Contact-Tracing Apps. Andy Greenberg. April 8th, 2020.
Stanford researchers help develop privacy-focused coronavirus alert app. Tom Abate. April 9th, 2020.
ACLU White Paper – Principles For Technology-Assisted Contact-Tracing. Daniel Kahn Gillmor. April 16th, 2020.
Digital Contact Tracing and Alerting vs Exposure Notification. Harper Reed. April 22nd, 2020.
Latest weapon in tracing and tracking coronavirus infections: your smartphone. Carolyn Said. April 22nd, 2020.
Here come COVID-19 tracing apps - and privacy trade-offs. Matt O'Brien and Christina Larson. May 5th, 2020.
Emergent Ventures prize winners, third cohort. Tyler Cowen. May 18th, 2020.
Digital Contact Tracing for Pandemic Response: Ethics and Governance Guidance. JHPEGDCT and Jeffery Kahn. May 26th, 2020.
Demonstrating 15 contact tracing and other tools built to mitigate the impact of COVID-19. Andy Moss, Connor Spelliscy, and John Borthwick. June 5th, 2020.
America Is Reopening. Coronavirus Tracing Apps Aren't Ready. Rolfe Winkler. June 22nd, 2020.
NPR | Arizona Illustrated | Reopening Safer. Bryan Nelson. August 3rd, 2020.
Smartphone app alerts University of Arizona students if they are exposed to COVID-19. Teri Whitcraft, Matt Gutman, and Alyssa Pone. August 25th, 2020.
COVID-19 tracking apps, supported by Apple and Google, begin showing up in app stores. Rob Pegoraro. August 26th, 2020.
How (Not) to Fight COVID-19. Peter Singer and Joanna Masel. September 3rd, 2020.
New COVID App Gives Health Officials Dials to Send Tailored Alerts. Michelle Quinn. October 5th, 2020.
How a Smartphone App and Contact Tracing Helped Keep UArizona Open and Curb COVID-19 Spread. Mikayla Mace. December 16th, 2020.
This App Protects Privacy While Tracing Covid-19 Infections. Nick Gillespie. May 6th, 2020.
University Of Arizona Alum Develops Confidential Contract Tracing App. Lauren Gilger. July 22nd, 2020.
Tracking COVID-19 Using Crowdsourced Data. Stanford HAI's COVID-19 and AI Virtual Conference. April 1st, 2020.
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.
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
The University of Arizona launches contact tracing app 12 News. August 20th, 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 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.