I'm currently at the University of California San Diego at the Halıcıoğlu Data Science Institute, where I explore the foundations of machine learning, advised by Mikhail Belkin.
I'm excited about the boundless potential AI & ML has to affect global change, as well as how we can make these algorithms more generally intelligent. To that end, I'm interested in ways to imbue algorithms with notions of "learning" more similar to ours, and elucidate why these methods work so well.
Research
In my undergraduate study at HDSI, I undertook research advised by Justin Eldridge in a fellowship position dedicated to novel uses of AI & ML. I explored these cutting edge technologies to find ways they could be used to improve society on a scale impossible without today's technology. I pursued projects involving the applications of ML & AI to fighting COVID-19, sentiment analysis of online political activity during the 2020 election, and other work in a similar vein. Responsible data science and fair and transparent algorithms are also important facets of my work as AI and ML are increasingly used in ways that can affect millions of people. I'm excited to continue working with these technologies at the forefront of the field as I believe they will only continue to play a more ubiquitous role in the global society of the future. My research has focused on using AI & ML to tackle some of the most pressing problems in today's society.
In 2018, I analyzed the dynamics of online communities and identified specific factors that resulted in positive conversations in those communities. I expanded this research in 2019 and built models to identify astroturfing—conversations that are deliberately manipulated by bad actors—in various online forums. Later that year, I researched ad spending on Snapchat to find areas of anomalous spending and developed algorithms to predict these patterns. In the midst of the COVID-19 pandemic, I shifted my focus to the ways that AI could be applied to fighting the virus. After the 2020 US election, I examined the patterns of sentiment in online political communities and found marked differences between political affiliations. In 2021, I used AI to analyze the enormous Accellion UC data breach and worked to allow those affected to be notified of their breached data. In 2022, I collaborated with Intel's DCA team to develop deep learning models to predict user behavior, aimed at improving the user experience for those on outdated or lower-powered hardware.
This site contains information about my work in addition to links to paper versions and code behind specific research projects. You'll also find personal thoughts on the world of technology and AI and ML in particular, as well as recognition I've received from things like awards or being featured in an article.