Scholar

Oliver J. Sutton

Google Scholar ID: 23pAfUcAAAAJ

King's College London

Machine learningAdversarial attacksNumerical analysisFinite element methods

Citations & Impact

All-time

Citations

742

H-index

i10-index

Publications

Co-authors

list available

Contact

Publications

2 items

2025

Cited

2025

Cited

Resume (English only)

Research Experience

Currently a researcher in the Department of Mathematics, King's College London
Conducts research in machine learning, AI, and computational simulation
Works on AI system (in)stabilities, learning from few examples, geometric aspects of high-dimensional learning
Develops novel numerical algorithms for biological and physical simulations
Researches adaptive meshing techniques using general polygonal/polyhedral elements

Background

Researcher in Machine Learning and Artificial Intelligence
Background in Applied Mathematics, specializing in numerical analysis
Deeply interested in both theoretical and practical aspects of learning algorithms and AI systems
Focuses on understanding AI decision-making, mitigating risks, and enabling positive societal impact
Research interests include: adversarial and stealth attacks on AI, few-shot learning, the role of high-dimensional geometry in learning, numerical algorithms for simulating biological/physical systems, flexible numerical methods based on polygonal/polyhedral meshes or exotic discrete function spaces, and adaptive numerical methods

Co-authors

16 total