Faculty of Science – IORA – Institute of Operations Research and Analytics

Mathematics

Forecasting and Business Intelligence, Non-stationary Time Series Analysis, High Frequency Data and Limit Order Book, Functional Data Analysis and Network Analytics, Text Mining and Sentiment Analysis, Renewable Energy Data Analytics, Risk Management, Crypto Blockchain

Mathematics

My research interests lie broadly inside probability theory and its applications in statistics, machine learning and nonlinear systems. My recent research involves investigating the behavior of algorithms, e.g. EnKF, MCMC and SGD, when the problem dimension is high. One of the common theme is that we can show stable performance of these algorithms, if certain intrinsic structures can be exploited

Statistics and Data Science

Bayesian model selection, Bayesian nonparametrics, hierarchical models, Markov chain Monte Carlo, spatio-temporal modelling.

Statistics and Data Science

His research interests centre around stochastic processes and their broad applications and intersections with other fields such as data science, with a particular focus on Markov chains theory and stochastic algorithms driven by Markov chains

Statistics and Data Science

Sequential analysis, Change-point detection, Monte Carlo methods

Mathematics

I am broadly interested in mathematical optimization and its applications to the data sciences. I am specifically interested in developing optimization-based methodology for a range of data processing problems, as well as understanding the performance and limitations of widely used optimization-based methods in the data sciences

Mathematics

Second order backward stochastic differential equations (2BSDEs), Stochastic control in finance and insurance, Valuation adjustments, Principal-Agent problems, Data analytics and information acquisition, Deep learning methods

Mathematics

Mathematical optimization, particularly mixed-integer nonlinear programming and nonsmooth optimization, along with their applications in operations research, data science, and other related fields.

Mathematics

Matrix Optimization Problems: algorithms, applications, and theory. In particular, large scale semidefinite programming. Fast algorithms for large scale statistical and machine learning problems. Iterative methods for large linear systems of equations, especially those arising in optimization.

Faculty Members: Science

Chen Ying

Tong Xin

David Nott

Michael Choi

Chan Hock Peng

Soh Yong Sheng

Zhou Chao

Shaoning Han

Toh Kim Chuan

IORA

Resources