CV

For full CV, download [pdf]

Education

PhD in Statistics and Machine Learning, University Of Oxford, 2019 (expected)
Part III: Master of Advanced Study, Mathematical Statistics in University Of Cambridge, 2014
BSc Mathematics in Imperial College London, 2011

Spring 2019: Citadel Securities, Hong Kong
- Quantitative Research Intern
- Project: Study on market data characteristics for alpha development
Summer 2018: Tencent AI Lab, Shenzhen
- Research Intern
- Project: Construct new methodology for Bayesian optimisation (BO), used for automatic hyperparameter selection (AutoML)
Spring 2018: Institute of Statistical Mathematics, Japan
- Research Intern
- Project: Construct a ML model for predicting malaria incidences, given real data with more than 1 million points
Fall 2017: Amber AI, Hong Kong
- Quantitative Research Intern
- Project: Construct a 1-step, end-to-end stock portfolio machine learning Model
Summer 2016: Printastic, London, UK
- Data Science Intern
- Project: Prediction of user’s intent to purchase over time using app data
Summer 2016: Styloko, London, UK
- NLP Data Science Intern
- Project: Cluster fashion words with similar meanings, to construct a similarity measure between descriptions

Programming (in order of experience)
- Language: Python, R, MATLAB
- Libraries: TensorFlow, Torch

Variational Learning on Aggregate Outputs with Gaussian Processes
H. Law, D. Sejdinovic, E. Cameron, T. CD Lucas, S. Flaxman, K. Battle, K. Fukumizu
Advances in Neural Information Processing Systems (NeurIPS), 2018
Bayesian Approaches to Distribution Regression
H. Law, D. Sutherland, D. Sejdinovic, S. Flaxman
Artificial Intelligence and Statistics (AISTATS), 2018
Testing and Learning on Distributions with Symmetric Noise Invariance
H. Law, C. Yau, D. Sejdinovic
Advances in Neural Information Processing Systems (NeurIPS), 2017