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
   International Conference on Learning Representations (ICLR 2026)
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)