WELCOME!

I am a principal researcher at Microsoft Research Cambridge, where I lead work on next-generation training algorithms and architectures for AI.

My broader research seeks to understand the computational principles of intelligence — and turn them into better ways to build AI systems.

(New!) Next-Gen Training Algorithms

Modern AI progress has been driven largely by scale. This project explores a complementary direction: better training algorithms that use compute more effectively.

An early result is SinkGD, developed in 2024 and recently adopted in DeepSeek V4.1 pretraining.

Building on this work, we develop ARO, a unifying view of LLM optimization through adaptive rotations — and a framework for designing more efficient training algorithms at scale. See Microsoft Research Forum Talk.

AI4Real-World Impacts

I also work broadly across probabilistic & causal AI for decision-making, with an emphasis on turning research into deployed systems.

One highlight is AI-driven personalized education: the technology I developed has reached more than half of UK schools. See media coverage: AI helps create personalized math lessons for students.

Bio

Before joining Microsoft, I did my Ph.D (2018- early 2023) in Machine Learning Group, CBL at the University of Cambridge, supervised by Prof. José Miguel Hernández-Lobato, and advised by Prof. Richard Turner. My PhD research focused on the field of probabilistic and causal machine learning. Check out my PhD thesis Advances in Bayesian Machine Learning: From Uncertainty to Decision Making. During my PhD, I also worked as an intern researcher at Microsoft Reserach Cambridge (MSRC), under the supervision of Dr. Cheng Zhang. Before joining the University of Cambridge, I obtained an MRes degree in Computational Statistics and Machine Learning from the Depertment of Computer Science, University College London, supervised by Prof. David Barber.