Scholar
Hugh Dance
Google Scholar ID: h4Q6RJEAAAAJ
Gatsby Computational Neuroscience Unit, University College London
Citations & Impact
Citations
93
H-index
4
i10-index
2
Publications
6
Co-authors
7
list available
Publications
Resume (English only)
Academic Achievements
- Two papers accepted at ICML 2025: (1) a novel algorithmic implementation for adaptive sampling methods designed to improve their computational efficiency on modern hardware, and (2) a novel, distribution-free method for causal discovery using parameterized vector fields
Research Experience
- Worked as an econometrician in PwC's Economic Consulting team for about 5 years (2016-2020, 2021-2022)
Education
- PhD: Gatsby Unit, University College London, supervised by Professor Peter Orbanz (2023-present)
- Master's: Computational Statistics and Machine Learning, University College London (2021)
- Master's: Economics, University College London (2016)
- Bachelor's: Economics and International Relations, University of Birmingham (year not provided)
Background
- Research interests include causal machine learning, machine learning under symmetry, and aspects of probabilistic machine learning such as Gaussian processes, MCMC, and scalable inference. Currently focused on understanding how measure transports that satisfy certain algebraic properties can be used to avoid the pitfalls of existing methods for counterfactual inference.
