David (Dowon) Baek

I am a first-year PhD student at the EECS Department at Massachusetts Institute of Techology, where I am fortunate to be advised by Prof. Max Tegmark in the Tegmark AI Safety Group. Previously, I completed my Bachelor's degree in Physics and Computer Science at Seoul National University.

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Research

My research interest revolves around representation learning, AI safety, mechanistic interpretability, and AI alignment. Modern machine learning models often operate like a black-box: my goal is to understand the mechanism that enabled the success of these large-scale models, thereby making them more interpretable and aligned with human's goals.

GenEFT: Understanding Statics and Dynamics of Model Generalization via Effective Theory

We present GenEFT: an effective theory framework for shedding light on the statics and dynamics of neural network generalization, and illustrate it with graph learning examples.

Design and source code from Leonid Keselman's Jekyll fork of Jon Barron's website.