Scholar
Isaac Reid
Google Scholar ID: 3JPyAi0AAAAJ
PhD student, University of Cambridge
Citations & Impact
Citations
100
H-index
5
i10-index
5
Publications
14
Co-authors
0
Contact
Publications
Resume (English only)
Academic Achievements
- Multiple papers accepted to top conferences such as NeurIPS, ICML, ICLR, and AISTATS. In September 2025, d-TDA was accepted to NeurIPS and Gemini Robotics 1.5 was released; in May 2025, STRING paper was accepted to ICML; in January 2025, two papers were accepted to ICLR and one to AISTATS; in November 2024, awarded a Google PhD Fellowship in Algorithms and Theory; in January 2024, two papers were accepted to ICLR; in September 2023, Quasi-Monte Carlo Graph Random Features was accepted to NeurIPS as a spotlight paper; in April 2023, Simplex Random Features was accepted to ICML with an oral presentation.
Research Experience
- My current research interest is in efficient transformer attention for graph-structured data. I have designed novel Monte Carlo estimators to approximate the softmax kernel, random walk-based algorithms for building 'graph random features', and new position encodings using Lie groups. I also dabble in statistical physics, approximate Bayesian inference, and influence functions. Sometimes, I help train foundation models for robotics. I taught mathematics for second-year undergraduate engineers at Trinity (2023-2025) and continue to mentor master’s students. I was a guest lecturer for the 2024-2025 Cambridge MLMI course, providing an introduction to transformers.
Education
- I am pursuing my PhD at the University of Cambridge, supervised by Dr Adrian Weller and Prof. Rich Turner, and collaborating closely with Prof. Krzysztof Choromanski. I am funded by a Trinity College external studentship and a Google PhD Fellowship. I intend to graduate in February 2026. I obtained my MPhys at Hertford College, Oxford, where my dissertation was on entanglement barriers in dual-unitary quantum circuits, supervised by Dr Bruno Bertini and Prof. Fabian Essler.
Background
- I’m a final-year PhD student at the Machine Learning Group within the Cambridge University Engineering Department. My research interests lie at the interface of ML, statistical physics, and applied mathematics. I currently live in NYC, where I'm working with Google DeepMind.
Miscellany
- My interests include statistical physics, approximate Bayesian inference, and influence functions. I worked at an early-stage startup for a year, developing adaptive optics for laser fabrication of qubits inside diamonds, and advised IQ Capital as a part-time research associate.
Co-authors