I am a computational social scientist and PhD candidate at Columbia Business School, interested in the role of context in technology-mediated behavior and human-AI interaction. In my research, I explore how people interact with their environments through technology and how we can use data collected through mobile devices and online social platforms to better predict and understand human behavior at scale. For example, I have analyzed the relationships between peoples' mobility profiles and various behavioral outcomes and developed context-aware modeling approaches to better predict online social behaviors (e.g., user engagement and instant messaging on Snapchat). In my most recent work, I have explored the capacities of large language models to infer psychological variables from people's digital footprints. The nature of my research requires me to integrate methods from the social sciences and computer science. This has led me to pursue projects aimed at the development of new methods. For example, I have contributed to the data analysis baseline library (dabl), an open-source Python library for automated machine learning. I am also a core contributor at the AI Model Share Initiative, a new lab for machine learning innovation at Columbia University. Here, I have played a key role in the development of an open-source academic repository for machine learning model metadata and prediction APIs, enabling researchers to tackle computational problems collaboratively.
Aside from my academic endeavors, I am passionate about music production and combat sports.