Dr Sourav Das ~ Lecturer, Statistics and Data Science

I am interested in application of statistical methods that help model natural events which evolve over space and time. Of particular interest are natural hazards, environmental variables and diseases. The statistical problems are wide rannging and complex. My personal interests fall in two broad categories-

a. Modelling of spatial and temporal stochastic dependence structures.

b. Efficient computation of big spatio-temporal dependence matrices. 

c. Applications involving the above.

 

Teaching

• MA1580: Foundations of Data Science (Level 1; CNS & TSV)
• MA5800: Foundations for Data Science (Level 5; CNS)

Research Disciplines

• Statistics (0104)
• Physical Geography and Environmental Geoscience (0406)
• Public Health and Health Services (1117)

Socio-Economic Objectives

• Natural Hazards (9610)
• Public Health (9204)
• Atmosphere and Weather (9602)

These are the most recent publications associated with this author. To see a detailed profile of all publications stored at JCU, visit ResearchOnline@JCU. Hover over Altmetrics badges to see social impact.

Journal Articles

• Das S and Tordesillas A (2019) Near real-time characterization of spatio-temporal precursory evolution of a rockslide from radar data: integrating statistical and machine learning with dynamics of granular failure. Remote Sensing, 11 (23).
• Das S, Subba Rao T and Boshnakov G (in press) An asymptotically unbiased weighted least squares estimation criterion for parametric variograms of second order stationary geostatistical processes. Communications in Statistics: simulation and computation,
• Wallbridge P, Parry S, Das S, Law C, Hammerschlag G, Irving L, Hew M and Steinfort D (2018) Parasternal intercostal muscle ultrasound in chronic obstructive pulmonary disease correlates with spirometric severity. Scientific Reports, 8.
• Das S, Cook A, Wah W, Win K, Chee C, Wang Y and Hsu L (2017) Spatial dynamics of TB within a highly urbanised Asian metropolis using point patterns. Scientific Reports, 7.
• Das S and Nason G (2016) Measuring the degree of non-stationarity of a time series. Stat, 5 (1). pp. 295-305
• Rao T, Das S and Boshnakov G (2014) A frequency domain approach for the estimation of parameters of spatio-temporal stationary random processes. Journal of Time Series Analysis, 35 (4). pp. 357-377
• Wah W, Das S, Earnest A, Lim L, Chee C, Cook A, Wang Y, Win K, Ong M and Hsu L (2014) Time series analysis of demographic and temporal trends of tuberculosis in Singapore. BMC Public Health, 14 (1).

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