Thompson Hall 224
Department of Mathematics and Computer Science
171 Moultrie Street
Charleston, SC 29409 USA
I finished my
PhD in Computer Science in 2011 at the University of Nebraska-Lincoln under the
guidance of Dr. Ashok Samlal and Dr. Leen-Kiat Soh.
Since then I have been at the Citadel teaching both graduate and
undergraduate courses. My research interests lie in the field of
spatio-temporal data mining, geospatial computing, big data anlytics
and processing, and VGI computing.
You can download my complete CV here. [CV]
Spatio-temporal data mining
Every entity in this world has a geospatial
location that allows us to answer questions such as where it is, where it occured, where it belongs to, etc. Similary
everything also has time attached to it. It may be a single point in time when
it occured, or a window of time which may denote its
lifetime. Making use of the spatio-temporal inofrmation
related to the objects or entities that exist in the world, in addition with
everything else that we know of the objects to study the relationships that may
exist in them is a very challenging problem. My contributions have been
specifically in incorporating the geospatial properties of objects within
clustering algorithms thus producing more robust and scalable geospatial
clustering algorithms. In addition, I have also developed a clustering algorithm
that treats both time and space as first-class citizens, and produces clusters
that span across both the spatial and temporal dimensions.
Currently, I am working to further develop algorithms for the analysis of spatio-temporal clusters that will allow us to identy the movements of clusters along both the spatial and temporal dimensions.
Big Data Analytics and Processing
Confluence of global positioning system-based sensor systems, satellite technology, and motivation to monitor the Earth’s natural and human resources has resulted in massive explosion of geospatial datasets (i.e., data with associated explicit or implicit geographic coordinates). In addition, widespread use of location aware devices (e.g., smart phones) has facilitated a new mode of data collection by volunteers and resulted in a new paradigm called Volunteered Geographic Information (VGI) computing (Goodchild, 2007). This form of crowd sourcing for data collection has resulted in massive data being collected that fills gaps in the information collected by institutional sources (e.g., governmental and research) at spatial and temporal resolutions that were not feasible before. The resultant emergent collective intelligence from these data will play an increasingly greater role in the understanding of the Earth’s processes and their interactions and has the potential to fundamentally transform how humans interface with their natural and managed ecosystems. These datasets provide new pathways to understand the ecosystems, biological and epidemiological processes, and human activities and their impact. This kind of data-driven scientific inquiry can accelerate the progress of discovery and lead to improved decision making.
My interest lies in maximizing the information potential of these datasets by developing new scalable computational techniques based on new mathematical and statistical foundations to examine these disparate, diverse(in collection, dissemination, and usage) datasets as a whole (not individually) and to exploit the unique properties of the geographic space.
Web Resources and Design (CSCI 217) MWF 11:00 – 11:50 am TH 215
Database Design (CSCI 320) MWF 9:00 – 9:50 am TH 216
Microcomputer Applications (CSCI 110) MW 1:00 – 2:15 pm TH 220
Senior Research Project
Since Fall 2011, I have taught the following undergraduate courses:
Survey of Computer Science
Introduction to Computer Science I
Introduction to Computer Science II
Introduction to Programming and Databases
Web Resources and Design
In addition, I have taught the following graduate courses:
Data Modeling and Database Design
Object-Oriented Design Patterns
Advanced Topics in Database Systems
Programming for STEM Educators (This course is currently being developed as an online only course which can be taken as an elective towards an M.Ed. in STEM Education)