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Mission: I help
Mission: I help
- Scientists and Businesses
- maximise the return on their hard-earned data investments
- using Statistics and Machine Learning
Applications:
Laboratory Experimentation: Chemistry, Synthetic Biology
Customer Experimentation: A/B testing, market modeling
Research & Methods: I specialise in
Causal Inference:
Partial Identification
Synthetic Control
Intervention Generalisation
Bayesian Optimisation (Active Learning)
Causal Bayesian Optimisation
Multi-fidelity Bayesian Optimisation
Transfer-learning Bayesian Optimisation
Big Maths and I
Give me anonymous feedback via Admonymous here.
Supervision
Supervision
Academic Research:
Kaichuang (Kevin) Yang, University of Oxford, MSc -> KAUST, PhD
Dylan Schubert, UCL, MSc -> Chubb Global Markets
Jakub Kmec, UCL, MSc -> InstaDeep
Christophe Muller, Oxford, PhD -> TBD
Kyra Edwards, Oxford, PhD -> TBD
Johannes Hruza, Copenhagen, PhD -> TBD
Julia Zervakos, UCL, MSc -> TBD
Ewan Burns, UCL, MSc -> Royal Bank of Canada
Nigel Cendra, UCL, MSc -> TBD
Industry Research Lead:
Gbetondji Dovonon, UCL, PhD -> Mila, Montreal
Jordan Penn, KCL, PhD -> Valence Labs (Recursion)
Daniel Augusto, UCL, PhD -> Microsoft Research, Cambridge
Mahdi Eskandari, Kent, PhD -> Sony Research, UK
Hao Wen, Surrey, PhD -> TBA
Mahirah Rahman, Oxford, BSc -> Morgan Stanley
Zoe Wang, Imperial MSc -> St Benedict’s School, London
Thesis/Transfer Committees:
Vuyiswa Kubulasa, University of Cape Town, MSc
Ruizi Yan, University of Oxford, PhD
CV (linear)
CV (linear)
2025+ Pioneer Fellow, University of Oxford, UK
Part of the Robin Evans Group, researching Causal Inference, Bayesian Optimisation and its combinations.
2024 PhD University College London, UK
Advised by Ricardo Silva on Fundamentals of Causal Inference
2019 PhD/MSc Syracuse University, US
Advised by Chilukuri Mohan and Volker Weiss, transferred to UCL
2017 BA University of Exeter, UK
Studied Philosophy, Politics and Economics with a focus on Statistics, Machine Learning and the Philosophy of Science
Selected Publications
Selected Publications
Non-parametric identifiability and sensitivity analysis of synthetic control models
J Zeitler, A Vlontzos, CM Gilligan-Lee
Stochastic causal programming for bounding treatment effects
K Padh, J Zeitler, D Watson, M Kusner, R Silva, N Kilbertus
Intervention Generalization: A View from Factor Graph Models
R Silva, G Bravo-Hermsdorff, D Watson, J Yu, J Zeitler
Patents
Patents
Systems and methods for bias bounded sensitivity analysis of synthetic control models (Google patents)
Language Learning with Lyrics (link)
r-Transfer Learning Bayesian Optimisation (link)
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