I lead a small but growing group at Imperial College London aiming to discover shared principles of efficient intelligence between natural and artificial systems.

In 2024, I was awarded a joint Imperial College Research Fellowship and Schmidt Sciences Fellowship, in addition to a Nature Computes Better Opportunity Seed from the UK’s Advanced Research and Invention Agency (ARIA). In 2022, I completed my PhD at the University of Cambridge at the Cognition and Brain Sciences Unit. I previously trained as a medical doctor with a research focus on neurosurgery and neuroanatomy.

The core belief running through our research is that by inferring the latent principles of efficient computation we see in nature and reverse-engineering them, we can fuel the growth of entirely new paradigms of intelligence. Modern architectures powerfully exploit one core principle: scale through immense parallelism. What new principles will fuel the future generations of efficient intelligent systems?

My work has been featured by outlets including the University of Cambridge and Nature Machine Intelligence. My primary interests include:

• Building neural networks operating in context of spacetime constraints (e.g., spatially and temporally embedded neural networks).

• Understanding how deep multi-scale heterogeneity and asynchrony, intrinsic to neural systems, widens the expressivity of possible architectures and computational primitives.

• Instantiating energy-efficiency and systems-level principles common in nature within scalable AI architectures.

• Understanding the early stages of human neural and cognitive development through constructivist simulation (e.g., brain development via generative methods).

• Using network science and dynamical systems theory to improve AI safety and interpretability.

• Leveraging technology, in particular AI, to accelerate scientific discovery.