Our academic, formal and informal education.
GeoCAS faculty and staff conduct research in the social ecology, marine biology, public policy, resource mapping, and human impacts on the natural environment. Current projects include mapping fish spawning behavior, the location of impervious surfaces on Saint Thomas, changes in seagrass bed size and extent through time, the relationship of land use and nearshore water and habitat quality, and the relation of census data to geography.
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- GISci: Principles of Geographic Information Science (MES 565). Graduate level course that explores key principles, theories, practices and applications of the new area of Geographic Information Science. The course aims to provide to graduate students a good mix of theoretical, methodological, and practical knowledge regarding geographic information. It includes advanced lab training and certification in 72 hours of training. It promotes professional competencies and skills in spatial thinking and analysis for natural resource management professionals. 5 credits. Fall semesters. Co-taught by Drs. Alexandridis and Primack.
- Human Dimensions in Natural Resource Management and Policy (MES 690).Graduate level course that provides a comprehensive and integrative or transdisciplinary overview of a range of human dimensions areas involved in environmental and natural resource management and policy. The course content is complemented with a parallel group research project development that aims to builds graduate student skills and competencies in addressing coupled or linked social-ecological systems and their need for close integration. 4 credits, Fall semesters. Taught by Dr. Alexandridis.
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- Knowledge Engineering and Expert Systems (CSC430)Undergraduate level course providing basic and key concepts, techniques, methods and applications of knowledge engineering and expert systems. Such systems are founded within the broader development and historical evolution of the area of artificial intelligence in computer and computational sciences. The course advances learning and understanding in gathering and codification of knowledge; the role of data, information and knowledge in real-world systems; logic programming, formula manipulation and predicate logic; skills and attributes of knowledge engineering; knowledge-based systems (KBS); decision support systems (DSS); deductive and inductive logic and information retrieval; natural language processing; ontologies and semantic inference; expert system interfaces and applications; knowledge-based technologies, big-data engineering and knowledge extraction. 2 credits. Spring semesters. Taught by Dr. Alexandridis.
- Scientific Computing Applications (CSC239).Undergraduate level course open to all UVI students, from all colleges and schools. The course aims to provide the content and context of using computers and computational principles in advancing scientific exploration and investigation. It further provides a general overview of the types of scientific computing applications, and the important of computational sciences and approaches to advancing knowledge and technology in our modern societies. 3 credits, Fall semesters. Taught by Dr. Alexandridis.
- Junior/Senior Computer Science Seminars (CSC397,398,497,498).Undergraduate capstone computer science seminars for Junior and Senior students of the Computer and Computational Science department. The course aims to introduce and develop student capabilities and skills in understanding, applying and implementing scientific research. Students develop their own research projects and actively engage in the scientific method. They develop assumptions, hypotheses and research questions; engage in critical literature review; design experiments and actively collect and analyze data. 0.5 credits. Fall and Spring Semesters. Taught by Dr. Alexandridis.
- Mathematical Modeling (MAT 352).Undergraduate level course open to upper-level UVI students of the College of Science and Mathematics. It provides a comprehensive review of mathematical modeling applications, from working with random variables and data structures, probability calculus, distributions and probabilistic modeling, game-theoretical modeling, graph-theoretic and network (including social networks and Bayesian networks) modeling, and introduction to agent-based modeling techniques. The students obtain skills in multiple modeling software and modeling tools. 3 credits, Fall Semester of odd years. Dr. Alexandridis.