Summary
Lee Sharkey is a principal investigator and interpretability researcher with nine years' experience unraveling what neural networks really learn to ensure safe deployment. Based in London and trained at ETH Zurich and Cambridge, he combines expertise in high-dimensional data analysis, visualisation, and mechanistic interpretability across PyTorch, TensorFlow, JAX and Keras. He has co-founded a research startup, published multiple influential papers on neural network geometry and RL agent analysis while at Conjecture, and continues to lead applied interpretability work at Goodfire. His background in public health and global organizations gives him an unusual systems-level perspective on risk and robustness in AI. Colleagues know him for pragmatic methods—like layer decomposition techniques—and for bridging rigorous research with deployable tools.
9 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD (discontinued), Human and Machine Cognition Lab & Neurocognitive modelling group, Doctor of Philosophy - PhD (discontinued), Human and Machine Cognition Lab & Neurocognitive modelling group at University of Tübingen
Master of Science - MS, Neural Systems and Computation, Master of Science - MS, Neural Systems and Computation at ETH Zurich
Methodist College Belfast
Bachelor of Arts (BA), Pre-Clinical Medical Sciences and Neuroscience, Bachelor of Arts (BA), Pre-Clinical Medical Sciences and Neuroscience at University of Cambridge
MSc in Data Analytics, with Distinction, MSc in Data Analytics, with Distinction at University of Glasgow