I am a Quantitative Researcher at Qube Research & Technologies, previously co-lead of Generative Music at Google DeepMind. I have 9 years of ML experience and an extensive background in computer science and competitive programming.
I like to solve problems in the real world using my ML expertise and thrive in collaborative environments where everyone works towards a shared agenda. Music is my lifelong passion - I’ve brought essential contributions to AI tools that support the creative process of making music. I was part of the core Lyria and Music AI Sandbox teams and GDM tech lead for the YouTube Dream Track quality workstream.
My PhD at King’s College, University of Cambridge was awarded with no thesis corrections. I also earned a First Class BA and an MPhil with Distinction from Cambridge. During my studies, I interned at Big Tech and startup companies, top research labs in academia and industry. Since graduate years, I’ve mentored and taught for 100s of hours. I love giving demos, talks or lectures and always get positive energy from a room full of people!
Outside work, I love rowing, travelling, playing rock/jazz guitar and chasing bands on tour. 🎼 I sometimes write poetry and lyrics for (ever-)future songs :)
PhD in Machine Learning, 2021
University of Cambridge
MPhil in Advanced Computer Science, 2017
University of Cambridge
BA in Computer Science, 2016
University of Cambridge
2024: One of 3 research leads for the generative music effort, working with the other leads to solicit research ideas, plan workstreams, ensure communication between leadership and team members, maintain momentum, and run recurring team meetings. IC work on model controls and finetuning for product use-cases. Regularly delivered demos of our music AI tech to industry stakeholders. The work of our team was presented at various events including Google I/O 2024.
2023: I was a core contributor to Lyria and Music AI Tools (Sandbox), and GDM tech lead for one of the Youtube Shorts Dream Track workstreams, where I coordinated with several YouTube teams to help our research team hit the quality launch bar and inform leadership product decisions.
Master’s research projects: Structure-aware Generation of Molecules in Protein Pockets (Pavol Drotar, 2020-21) (92⁄100) (presented at NeurIPS MLSB), Machine Unlearning (Mukul Rathi, 2020-21) (91⁄100), Goal-Conditioned Reinforcement Learning in the Presence of an Adversary (Carlos Purves, 2019-20) (87⁄100), Representation Learning for Spatio-Temporal Graphs (Felix Opolka, 2018-19) (85⁄100) (presented at ICLR RLGM), Dynamic Temporal Analysis for Graph Structured Data (Aaron Solomon, 2018-19) (presented at ICLR RLGM)
Computer Science Tripos Part II projects: Benchmarking Graph Neural Networks using Wikipedia (Péter Mernyei, 2019-20, Novel Applications spotlight talk at ICML GRL+), Multimodal Relational Reasoning for Visual Question Answering (Aaron Tjandra, 2019-20), The PlayStation Reinforcement Learning Environment (Carlos Purves, 2018-19) (80⁄100) (presented at NeurIPS Deep RL), Deep Learning for Music Recommendation (Andrew Wells, 2017-18) (76⁄100).
Undergraduate courses for Murray Edwards, King’s, and Queens’ Colleges: AI, Databases, Discrete Mathematics, Foundations of Computer Science, Logic and Proof, Machine Learning and Real-world Data.