About

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, where I researched under the excellent mentorship of Professor Alexandr Andoni.

I was previously a Student Researcher at Google DeepMind working on feature learning. I was also 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.) Optimization in neural networks and linear systems
4.) Tabular/scientific data

Feel free to email me: dbeaglehole {at} ucsd {dot} edu

(* denotes equal contribution).

Pre-prints

1. Aggregate and conquer: detecting and steering LLM concepts by combining nonlinear predictors over multiple layers
   Daniel Beaglehole, Adityanarayanan Radhakrishnan, Enric Boix-Adserà, Mikhail Belkin
2. Mechanism of feature learning in convolutional neural networks
   Daniel Beaglehole*, Adityanarayanan Radhakrishnan*, Parthe Pandit, Mikhail Belkin
3. Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features
   Adityanarayanan Radhakrishnan*, Daniel Beaglehole*, Parthe Pandit, Mikhail Belkin
   (twitter link)
4. Fast, optimal, and dynamic electoral campaign budgeting by a generalized Colonel Blotto game
   Thomas Valles, Daniel Beaglehole

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. Average gradient outer product as a mechanism for deep neural collapse
   Daniel Beaglehole*, Peter Súkeník*, Marco Mondelli, Mikhail Belkin
   Conference on Neural Information Processing Systems (NeurIPS 2024)
3. Feature learning as alignment: a structural property of gradient descent in non-linear neural networks
   Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala
   Transactions on Machine Learning Research (TMLR)
   Workshop on High-dimensional Learning Dynamics (HiLD) @ ICML 2024
4. Emergence in non-neural models: grokking modular arithmetic via average gradient outer product
   Neil Mallinar, Daniel Beaglehole, Libin Zhu, Adityanarayanan Radhakrishnan, Parthe Pandit, Mikhail Belkin
   International Conference on Machine Learning (ICML 2025)
   Mathematics of Modern Machine Learning (M3L) @ NeurIPS 2024
5. On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions
   Daniel Beaglehole, Mikhail Belkin, Parthe Pandit
   SIAM Journal on Mathematics of Data Science (SIMODS) ,
   Conference on the Mathematical Theory of Deep Neural Networks (DeepMath 2022)
6. 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)
   (twitter link)
   • This began as an earlier version:
     An Efficient Approximation Algorithm for the Colonel Blotto Game
     Daniel Beaglehole
7. Learning to Hash Robustly, Guaranteed
   Alexandr Andoni*, Daniel Beaglehole*
   International Conference on Machine Learning (ICML 2022)
   (twitter link) (presented by Prof. Andoni for NeurIPS’21 ANN competition)

Presentations

1. Google Brain: (“Feature learning in neural networks and kernel machines that recursively learn features”, 3/2023)
2. Yale University: Inference, Information, and Decision Systems Group, (“Feature learning in neural networks and kernel machines that recursively learn features”, 3/2023)
3. UCSD: Theory Seminar, (“Learning to Hash Robustly, Guaranteed”, 10/2021)
4. Goldman Sachs: Data Science and Machine Learning paper club, (“Learning to Hash Robustly, Guaranteed”, 07/2021)
5. Goldman Sachs: Summer internship final presentation, (“Predictive Clustering Time Series for Finance”, 08/2021)