We showcase our data science and big data analytics research activities in key areas like predictive modelling, high dimensional data mining, machine learning, business intelligence and artificial intelligence solutions, amongst others.
December 2017 - NUS statisticians have developed the Regularised Text Logistic (RTL) regression model to extract informative word features from digital text for decision-making.
September 2017 - NUS statistician has proposed a new Monte Carlo method that is computationally more effective for quantifying uncertainty.
September 2017. NUS mathematicians have proposed improvements to a well-known optimisation algorithm to significantly boost its computational efficiency.
February 2017 - NUS mathematicians have developed effective methods for modelling real-life problems with monotone density ratio conditions.
January 2017 - NUS statisticians have developed adaptive functional time series models that improve the forecast accuracy of complex data.
November 2016 - NUS mathematicians have developed an unbiased and consistent estimator for counting motifs (patterns) of gene regulatory relationships.
November 2016 - NUS mathematicians have developed an efficient and stable numerical method to solve fluid structure interaction problems involving large convections of fluid and near-contact of structures.
August 2015 - NUS statisticians reported that Glomerular filtration rates estimation using a self-directed 24-hour urine creatinine clearance is less accurate.
August 2015 - Mathematicians in NUS have developed computational methods to explore how microstructures affect the condensation of vapour onto a surface.
• Multiple Testing via FDRL for Large Scale Imaging Data
• Regression Density Estimation and Stochastic Approximation
• Summary of 'On the Stability of Sequential Monte Carlo Methods in High Dimensions'
• A Sequential Monte Carlo Approach to Computing Tail Probabilities in Stochastic Models
• Central Limit Theorem for Hotelling's T2 Statistics under Large Dimensions