About me

I’m a first-year PhD student in Computer Science at the University of Cambridge. Previously, I worked on AI safety research at LASR in London.

I find neural networks really cool and work on making LLMs safe. Driven both by curiosity about their internal mechanisms and by concern for their safe deployment, I currently explore how white-box interpretability methods can aid alignment and monitoring. I also appreciate the awesome, research-driven engineering¹ behind highly performant deep learning systems!

Please, don’t hesitate to reach out! I’m very happy to hear about your research, tell you a bit about mine, or just chat about anything interesting!

More about me

In early 2026, I joined the LASR fellowship, which I highly recommend². Under the supervision of Stefan Heimersheim, my team and I showed that model organism training methodology significantly affects white-box interpretability. We presented it as The Model Organism Lottery at an ICML 2026 Workshop in Seoul. At the end of the fellowship in April, my team was awarded a ~£200k research grant for further research work until September. Simultaneously, I worked on LangMAP – our collaborative (Cambridge-ETH-EPFL) work towards an alternative tokenisation paradigm for multilingual LLMs, which was accepted as an oral at EMNLP 2026. Towards the end of summer, I focused on TopKLoRA, a fine-tuning method providing interpretable-by-design and composable feature latents. Following my mini-project on cross-model thought anchors, I was accepted into the exploration phase of Neel Nanda’s MATS 12 stream.

Throughout my year-long MPhil degree, I was supervised by Prof. Nic Lane and worked with the CaMLSys group, where I focused on Federated Learning. My dissertation explored how several institutions with limited computational resources can collaborate on training a joint foundational language model. During my studies, I also benchmarked the inner workings of torch.compile(), and explored the KV-caching strategies in LLM inference. On the more theoretical front, I looked into the phenomenon of attention sinks³ in transformers and studied the concept of dynamic tokenisation. What a year it was!

Previously, I have been working with researchers from UCL NLP on BritLLM – a joint effort towards producing freely available Large⁴ Language Models for UK languages⁵. In my undergraduate dissertation supervised by Prof. Pontus Stenetorp, I focused on the problem of the poor availability of LLMs for low-resource languages and worked on language model adaptation methods for African languages. Furthermore, we explored Data-Efficient Task Unlearning in LMs, a method for increasing the safety of language models and removing their undesired capabilities.

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