University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Asymptotic Meta Learning for Cross Validation of models for financial data
IEEE Intelligent Systems page 1-1 (11 February 2020)
Impact of Price–Quantity Uncertainties and Risk Aversion on Energy Retailer’s Pricing and Hedging Behaviors
Energies issue 17 volume 12 page 3296-3296 (27 August 2019)
Best investment strategy selection using asymptotic meta learning
2017 IEEE/SICE International Symposium on System Integration (SII) page 72-76 (5 February 2018)
Hedging quantity risks of power plants with standard power options
2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA) (1 November 2016)
Dr Xiang works as postdoc in AI and Fintech. He Obtained a Dphil in Data Science and Information Technology from Tsinghua University. His research is focused on machine learning models that optimize and understand our current socio-economic system, with special attention on the energy and finance sectors. He develops Meta learning algorithms that solves the ordinal optimization problem in the domain of finance and operations research. He is also interested in the operational research in the blockchain systems.
Specifically, He is interested in
1.Theories of Reinforcement Learning, Meta Learning;Their applications in Energy System Optimization and Financial Engineering.
2.Theories of Generative Adversarial Nets, Deep Unsupervised/Semi-supervised Learning; Their applications in data mining.
3.Utility Theory,Game theory and their applications in Economics and Financial Engineering.
4.Complex System Simulation, Complex networks Simulation, Agent Based Simulation and their applications in sociology.