In hindsight, things often look a lot more linear than they seemed at the time. Same goes for my career trajectory. Motivated primarily by curiosity, I pursued an undergraduate degree in physics and mathematics, before getting a PhD in statistical and computational physics. After graduating at the start of the pandemic, I was lucky enough to get a prestigious fellowship to pursue my own research project at NIST. After a couple of years doing so, I realized I wanted to try pursuing other paths, due to both a frustration with academia and an interest in other fields.
Transitioning into data science in 2022, I’ve ended up working at the intersection of web3, cryptography, game theory, and network science, bringing rigorous quantitative research to practical applications in multiple fields. I am excited to be somewhere with opportunities for me to learn and grow. I love coming up with quantitative solutions to tough problems and figuring out how complex systems work, but I’m realizing that I’ve just only started to explore the countless domains where that is possible.
For more details on my career path, see my LinkedIn page.
As of March 2023, I joined a new start-up, Valence (now merged with Precise). We started building a decentralized system for identity/data verification, data exchange, and data valuation. The goal was to incorporate cryptographic verification and trust-less, decentralized networks to ensure data remains private and secure while retaining the ability to verify its source. Recently, we have continued this work in the realm of advertising technology: with a specific lens towards how agentic AI will change the field. As part of the research team, I get to work on some really exciting new ideas in the worlds of cryptography, machine learning/AI, game theory, and network science!
A lot of my work has been focused on value attribution (the idea of identifying the components of a system that contributed the most towards the systemic outcome) and decision optimization (seeking the best possible return on economic decisions under specified constraints). This research has taken me down some interesting roads in game theory and network science. Some of my recent research has been focused on the following:
Shapley values: a game theoretic tool to calculate the contribution of each player in a cooperative game. First introduced in 1951, this idea has gained a lot of traction in recent years as a way to explain results of AI models, which can often be a black box.
Network Centrality and Connectivity: changes in the topology of a network can lead to some interesting and unpredictable outcomes in it's properties
Mixed Integer Optimization: the mathematics of optimizing a value function of linear and binary variables, subject to various constraints, is an interesting field with applications to a wide array of practical problems.