My research develops generalisable strategies for sequential learning, with applications across: (1) scientific computing, where controls steer dynamical systems into desirable states; (2) world models, where agent actions determine subsequent states; and (3) experimental design, where utility functions prescribe the optimal areas to explore. To unify varied real-world modalities such as text, videos, or physical observables, I rely on the remarkably powerful concept of weight-space learning, which I view as a broad paradigm to explain meta-learning, Bayesian learning, and more.
Currently, I am a Postdoctoral Researcher at the University of Manchester in Prof. Samuel Kaski’s team. Within the Centre for AI Fundamentals, my research seeks to design principled world models for Bayesian experimental design.
I completed my PhD at the University of Bristol under the supervision of Dr. Tom Deakin, Prof. David Barton, and Prof. Simon McIntosh-Smith. I received my MSc in Applied Mathematics from the University of Strasbourg (2021), advised by Prof. Stéphane Labbé at Sorbonne’s Laboratoire Jacques-Louis Lions. Prior to that, my undergraduate studies in Mathematics and Physical Sciences spanned a BSc from Aix-Marseille University (2019) and associate degrees from Oshima College of Technology (2019), the University of the People (2019), and the National Advanced School of Engineering of Yaoundé (2017).
Beyond research, I am committed to equity in STEM education, volunteering for outreach initiatives with CodeMakers, ExamStar, and St. Teresa, among others. In my downtime, I enjoy football, playing the piano, creative coding, and building indie games.
Feel free to explore my CV or learn more through my profile on LinkedIn. Also, let’s connect via email, X, LinkedIn, or WeChat if you’d like to collaborate or simply chat!
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