I study how evolution builds efficient learners. My work probes the inductive biases that enable biological systems to learn rapidly, operate under energy constraints, and support lifelong adaptation — combining large-scale neural recordings in mammals with genetic dissection of computation in insects. I aim to turn these principles into new foundations for artificial intelligence.
University of Oxford
Research Programme
01 — Neural geometry for interpretability
Methods for inspecting the internal representations that govern consequential model decisions.
2026
02 — Interpretable control of large models
Designing systems that preserve human agency while amplifying judgment, creativity, and care.
2025
03 — Digital Twin of brain
Building evidence practices for reliable deployment in public, institutional, and high-stakes settings.
2024
selected Publications
Granule cells reorient cortical manifolds to separate contexts but preserve their geometry
Garcia-Garcia, M. G., Wojcik, M. J., … & Wagner, M. J.
Nature · in press
2026
Learning shapes neural geometry in the prefrontal cortex
Wójcik, M. J., Stroud, J. P., … & Stokes, M. G.
Nature Neuroscience
2026
Organizing across disciplines to tackle shared computational challenges
Treyde, W., Kwiatkowski, A., Acherberg J., … & Wójcik, M. J.
Patterns
2026
Working memory shapes neural geometry in human EEG over learning
Wójcik, M. J., … Myers, N. E., & Hunt, L. T.
eLife · 14, RP106609
2025
Effects of noise and metabolic cost on cortical task representations
Stroud, J. P., Wojcik, M. J., … & Lengyel, M.
eLife · 13, RP94961
2025