Linas Nasvytis

I am a first year PhD student at Stanford University, advised by Professor Judith Fan.

My research focuses on the intersection of human and machine intelligence. I am particularly interested in the process of learning by thinking – how humans and machines can improve their reasoning without any additional data or feedback from the outside world. More broadly, my goal is to formalize the computational principles behind human intelligence, and use such insights to both interpret and improve the reasoning and decision-making capacities of artificial systems.

Prior to coming to Stanford, I spent a wonderful year as a Pre-doctoral Fellow at Harvard University, collaborating with Sam Gershman and Fiery Cushman. Before that, I earned a master’s degree in statistics with a focus on statistical machine learning at the University of Oxford, where I worked with Jakob Foerster, Christian Schroeder de Witt, and Chris Summerfield.

Before discovering the intersection of machine learning and cognitive science, I was a research analyst working with Paul Romer at NYU, where we focused on tech policy, cryptography, and economic research. I graduated from the Dual Bachelor’s progam between Columbia University (BA in Economics) and Institut d’études politiques de Paris (BA in Politics, Philosophy and Economics).

If you’d like to discuss any research ideas or have any questions, feel free to reach out by email!