Courses & Classes

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Description: This course aims at showing the cutting-edge research on semantic web and encouraging research capability for advanced students. Students attending this course should expect reading, presenting and evaluating important research papers on semantic web, identifying and surveying interesting semantic web research areas.
Professors: Jim Hendler
Description: Cognitive Computing is a term being used for a new generation of artificially intelligent computers that interact with humans in new and important ways. Rather than human-machine interaction, cognitive computing is said to be leading to a new generation of human-machine collaboration, where computers help humans gain new insights into problems via a suite of technologies ranging from natural language to machine learning.
Description: This course will explore the breadth of what is meant by "cyberinfrastructure" and examine the state of the art and open challenges. In addition to discussing high-performance computing; data, analysis, and visualization; and virtual organizations, the course will touch on the nature of infrastructure, the revolutionary potential of cyberinfrastructure to enable research, education, and societal application, the concept of socio-technical solutions, and designs to provide end-to-end support of the scientific lifecycle.
  • To enable an understanding of the meaning of the term "cyberinfrastructure" and the current state of the art and open challenges
  • To provide an understanding of the potential for cyberinfrastructure as a research and competitiveness tool and of the design and implementation factors that influence how well cyberinfrastructure capabilities enable realization of that potential
  • To enable future cyberinfrastructure developers, users, and stakeholders to construct/evaluate cyberinfrastructure R&D, deployment, and maintenance arguments.
  • To provide an overview of cyberinfrastructure development and deployment best-practices
Description: Data and Information analytics extends analysis (descriptive and predictive models to obtain knowledge from data) by using insight from analyses to recommend action or to guide and communicate decision-making.
Description: This course combines aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. It aims to provide formal education and training in the key cognitive and skill areas to enable graduates to become key participants in escience collaborations.
Goals: To instruct future scientist how to sustainably generate/ collect and use data for their research as well as for others. Participants will learn and be evaluated on the full life-cycle of data and relevant methods, technologies and best practices.
Description: This is a seminar course — not a lecture course — that will discuss emerging trends in semantic technologies. We will have many presentations and discussions throughout the course that help you to understand, conduct, and evaluate academic research while we discuss the emerging trends in semantic technologies.
Goals: Prepare student for research in Semantic Technologies by giving them practice and guidance on:
  • Critically reading research papers
  • Making technical presentations, both individually and in groups
  • Preparing research papers
  • Preparing grant proposals
  • Providing the opportunity for practice talks, suggestions, literature review, etc.
Professors: Peter Fox
Description: Introduction to relational analysis and interpretation of spatial data and their presentation on maps (using MapInfo software). Geographic spatial data concepts covered are map projections, reference frames, multivariate analysis, correlation analysis, regression, interpolation, extrapolation, and kriging.
  1. To provide students an opportunity to learn geospatial applications and tools.
  2. To introduce relational analysis and interpretation of spatial data and presentation on maps.
  3. To introduce spatial database concepts and technical aspects of query languages and geographic integration of graphic and tabular data.
  4. To introduce intermediate aspects of geospatial analysis: map projections, reference frames, multivariate analysis, correlation analysis, regression, interpolation, exptrapolation, and kriging.
  5. To gain experience in an end-to-end GIS application via a term project.
Description: Learn about: preparation of informative, manageable datasets; accessing "big data" quickly and reliably during and subsequent to analysis; data pre-processing, analytics methods selection and testing, work flow design, and bulk data processing; exploratory data analysis including interpretation, generation of hypotheses and intuition about the data; prediction, utilizing statistical tools such as regression, classification, and clustering; communication of results through visualization, stories, and interpretable summaries.
Goals: Who should enroll: Students with prior experience/coursework in data analytics or statistics, students with experience in domains where big data techniques are employed, and students interested in graphical and written communication are all encouraged to join the class. You will work together in teams to develop significant analytics projects as a major part of this course.
Description: This course provides an introduction to ontologies, their uses, and an overview of their application in semantically enabled systems.
Goals: To learn how to build computer understandable definitions of terms for usage in automated systems.
Description: As semantic technologies have been gaining momentum in various e-Science areas, it is important to offer semantic-based methodologies, tools, middleware to facilitate scientific knowledge modeling, logical-based hypothesis checking, semantic data integration and application composition, integrated knowledge discovery and data analyzing for different e-Science applications.
Goals: To fill the gaps that are currently present in the integrative nature of informatics for the translation of science into requirements for the underlying and largely syntactic e-infrastructure.  
Description: Prepares students for research in knowledge graphs.
Goals: Prepare students for research in knowledge graphs. Teach students how to: Read and find relevant research papers, Present research ideas, Synthesize material, Identify gaps in existing research, Critically review (as one might do for a publication venue) Develop a literature corpus for use in research.
Professors: Jim Hendler
Description: Since its inception, the World Wide Web has changed the ways people work, play, communicate, collaborate, and educate. There is, however, a growing realization among researchers across a number of disciplines that without fundamental understanding of the current, evolving and potential Web, we may be missing or delaying opportunities for new and revolutionary capabilities.
Goals: This course attempts to provide the foundations of that understanding, exploring the fundamentals of the World Wide Web's function including the HTTP protocol, key algorithms that make the Web function, future trends, and social issues with respect to Web use and effect.
Description: Xinformatics is intended to provide both the common informatics knowledge as well as how it is implemented in specific disciplines. The theoretical basis arises from information science, cognitive science, social science, library science as well as computer science, aggregating these studies and adds both the practice of information processing, and the engineering of information systems.
Goals: This course will introduce informatics, each of its components and ground the material that students will learn in discipline areas by coursework and project assignments.