Applied Statistics & Computing

Although this division normally works in close collaboration with the chemistry and nanotechnology faucet of Π, developing methodologies that can enable the revelation of similarities or disparities within multidimensional data sets acquired from scientific experimentation or analyses within Π or/and helping in the implementation of such methods that build theories and uncovers new phenomena, they will also perform work that solely involves their expertise.

Statistical methods typically used within the group includes those used in the calculation of reliability, biometrics, time series, forecasting, categorical data analysis, multivariate analysis, independence determinations, statistical significance, spatial statistics, the analysis of sparse data, Bayesian methods and stochastic processes. Applied computing operations include software engineering, artificial intelligence and distributive systems. An active area of R&D within Π is in the fusion and reduction of the dimensionality of datasets of amalgamated disparate data by implementing multiple sequence alignment, techniques for pattern discovery and feature detection, feature selection, Principal Components Analysis (PCA) and Discriminant Analysis, partitional and hierarchical clustering, classifier design, classification and cross-validation techniques.

Focus Areas

  • Algorithm design, implementation and testing
  • Chemometrics and Bioinformatics
  • K9~Machine Learning
  • Neural Networks
  • Data Mining and Clustering
  • 3D Image Analysis, Enhancement and Segmentation
  • Feature extractions and Pattern Classification.
  • Object Extraction and Recognition

Programming Languages: MATLAB, R, Visual C++, Java, Visual Basic, OpenGL, DirectX, PERL, SQL, LabView

Operating Systems: Windows 95/98/ NT/2K/XP/VISTA, UNIX, Linux, Sun Solaris, MAC-OS.

Application Software Packages: Adobe Photoshop, OPNET, MS Access, POSER.