Hi, my name is Will!
I work at the intersection of AI and science. I recently completed my Encode AI for Science Fellowship (Pillar VC and the University of Oxford), where I researched AI scientists and agentic systems for clinical drug development. Over the last year I have had three papers at ICML 2026 and co-built two products: et al., an AI-native reference manager for researchers and their agents, and A2A Net, infrastructure that lets agents from different people and organisations work together.
Before that, I completed my PhD in AI for Healthcare at Imperial College London, developing AI-driven clinical decision support for antibiotic prescribing. I have published eight first-authored papers in venues including Nature Communications, The Lancet Digital Health, and workshops at ICML, ICLR and AAAI. I have also interned at GSK.ai and hold a Master’s in Bioscience Enterprise from the University of Cambridge.
I care deeply about how new technology turns into products with real-world impact. Having finished the fellowship, I am now exploring what comes next, across startups, research labs and new ventures.
In my spare time, I like all things sports including rugby, running, climbing and F1.
Feel free to get in contact!
PhD in Artificial Intelligence, 2025
Imperial College London
MPhil Bioscience Enterprise, 2019
University of Cambridge
BSc in Biochemistry, 2018
Imperial College London

A framework for benchmarking AI scientist capabilities using adversarial, fast-moving real-world domains, where expert practitioners independently produce observable ground truth after an information cutoff. Instantiated in Formula 1 car design for the 2026 regulations and Magic: The Gathering deck building, with frontier models ideating from pre-cutoff information only and their outputs scored against real innovations and Pro Tour decklists.

Framing oncology clinical development as an offline decision-making problem, where an agent predicts the next six-month trial portfolio of a drug programme from information available at the decision date. Built on a temporal dataset of 31.7k public records turned into 881 decision episodes across 45 historical programmes, used to train offline policies and compare them against frontier LLM agents on held-out drug, sponsor, drug-class and temporal splits.

The use of decision-support systems based on artificial intelligence approaches in antimicrobial prescribing raises important moral questions. Adopting ethical frameworks alongside such systems can aid the consideration of infection-specific complexities and support moral decision-making to tackle antimicrobial resistance.