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OUR RESEARCH

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.

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Unlocking the power of web text data

December 2017 - NUS statisticians have developed the Regularised Text Logistic (RTL) regression model to extract informative word features from digital text for decision-making.

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Statistical hypothesis testing for high-dimensional data

October 2017 - NUS statisticians have developed an efficient method for comparing multi-group high-dimensional data.

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A more efficient method for quantifying uncertainty

September 2017 - NUS statistician has proposed a new Monte Carlo method that is computationally more effective for quantifying uncertainty.

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A practical optimisation algorithm for big data applications

September 2017. NUS mathematicians have proposed improvements to a well-known optimisation algorithm to significantly boost its computational efficiency.

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Paying for “extras” in freemium products and services

August 2017 - NUS statisticians have developed a better methodology to study user behaviour for freemium products and services.

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What is the weather tomorrow?

July 2017 - NUS mathematician has revealed ways which can lead to more accurate weather predictions.

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Statistical inferences with monotone density ratio 

February 2017 - NUS mathematicians have developed effective methods for modelling real-life problems with monotone density ratio conditions.

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Inference for dynamical systems

February 2017 - NUS statisticians have gained insights which allow for better prediction of physical phenomena.

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Fast algorithms for quantifying uncertainty

January 2017 - NUS statisticians have developed a method to estimate unknown parameters efficiently for modelling complex situations.

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Complex data analytics using functional and adaptive approach 

January 2017 - NUS statisticians have developed adaptive functional time series models that improve the forecast accuracy of complex data.

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Counting motifs in the human interactome

November 2016 - NUS mathematicians have developed an unbiased and consistent estimator for counting motifs (patterns) of gene regulatory relationships.

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Fluid structure interaction

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.

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Memory allocation for computer systems

August 2016 - NUS mathematicians have formulated an equation for designing efficient memory allocation techniques for computer systems.

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New approach in panel data analysis

July 2016 - NUS statisticians have developed a model which accounts for hidden factors and their effects for improved accuracy from economic data.

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Adaptive simulation methods 

June 2016 - NUS scientists have developed research which establishes the correctness of numerical methods used for important statistical applications.

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Querying social network data

April 2016 - Mathematicians in NUS have developed a new theory and technique for database query optimisation.

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Ways to estimate Asian kidney donors 

August 2015 - NUS statisticians reported that Glomerular filtration rates estimation using a self-directed 24-hour urine creatinine clearance is less accurate.

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Vapour condensation and microstructure

August 2015 - Mathematicians in NUS have developed computational methods to explore how microstructures affect the condensation of vapour onto a surface.

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Monitoring the performance of surgeons

May 2015 - NUS statisticians developed a highly improved risk-adjusted procedure for monitoring the performance of surgeons.

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New approach for analysing complex data  

May 2015 - NUS statistician has developed a new approach for analysing shape dynamics.

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Statistical models for classifying acute leukemia

October 2014 - NUS researchers developed a new Bayesian statistical model that can be used for classifying acute leukemia.

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High-dimensional statistical problems

August 2014 - NUS professors have demonstrated the feasibility of computational algorithms in high-dimensions.

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Taming Big Data

July 2014 - NUS statisticians have developed new statistical approaches and methodologies to deal with complex big data.

Address
Data Analytics Consulting Centre Block S16, Level 7
6 Science Drive 2
Faculty of Science
National University of Singapore Singapore 117546

dacc@nus.edu.sg

Business Hours 
Monday to Thursday:
8.30 am – 6.00 pm
Friday:
8.30 am – 5.30 pm