ABOUT ME

WELCOME!

I am a senior researcher at Microsoft Research Cambridge. I study the computational principles of intelligence – and turn them into smarter ways to train AI.

(New!) Towards Next Gen Training Algorithms for AI

Progress in AI isn’t just about scaling compute – discovering better learning rules matters just as much. Our recent work uncovers a new, unifying view of LLM training through the lens ofloss-landscape symmetries induced by architectures. We compile those insights into ARO (Adaptively Rotated Optimization), a new optimization framework that pushes the efficiency frontier for training LLMs at scale.

Check out our technical report: ARO: A New Lens On Matrix Optimization For Large Models. This work will be featured at the Microsoft Research Forum (Mar 3, 2026). Register now!

Past Research

I maintain a broad interest in core machine learning, with past research focused on advanced methodologies in probabilistic modeling, causal machine learning, and decision-making. My research has translated into high-impact deployments, both inside Microsoft and through external partners. Publicly disclosed examples include AI-driven personalized education, 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.