Daniel Beaglehole

Hello! I am a PhD student in the CSE department at UC San Diego in the machine learning and theory groups. I am very fortunate to be advised by Professor Mikhail Belkin. Prior to my PhD, I completed an MS in computer science at Columbia University, where I researched under the excellent mentorship of Professor Alexandr Andoni.

I was previously a Student Researcher at Google DeepMind working on feature learning and an ML Research intern at Goldman Sachs. I am supported by the ARCS Foundation Fellowship.

Broadly, I am excited about developing conceptually-driven machine learning and algorithmic methods. I am especially interested in:

1. Feature/representation learning
2. Deep learning (theory + applications)
3. Steering and monitoring LLMs
4. Tabular/scientific data

Feel free to reach out via email: dbeaglehole@ucsd.edu.

* denotes equal contribution.

Selected publications

1. Mechanism for feature learning in neural networks and backpropagation-free machine learning models
   Adityanarayanan Radhakrishnan*, Daniel Beaglehole*, Parthe Pandit, Mikhail Belkin
   Science
2. xRFM: Accurate, scalable, and interpretable feature learning models for tabular data
   Daniel Beaglehole, David Holzmüller, Adityanarayanan Radhakrishnan, Mikhail Belkin
3. Toward universal steering and monitoring of AI models
   Daniel Beaglehole*, Adityanarayanan Radhakrishnan*, Enric Boix-Adserà, Mikhail Belkin
4. Sampling Equilibria: Fast No-Regret Learning in Structured Games
   Daniel Beaglehole*, Max Hopkins*, Daniel Kane*, Sihan Liu*, Shachar Lovett*
   Symposium on Discrete Algorithms (SODA 2023)