Department of Computer Science

The Department of Computer Science in the School of Information and Computer Science, is internationally recognized as a unique group of faculty, visiting researchers, students and educational programs. Computer science faculty conduct research in numerous apsects of computer science including:

This research is performed in an open and interdisciplinary culture: faculty and students frequently are part of multiple research groups, continuously fostering new collaborations, and are at the forefront of addressing core issues in computer science.

Current & Planned CBMI related courses include:

• CS222 - Principles of Data Management
  
  Covers fundamental principles underlying data management systems. Content includes key techniques including storage management, buffer management, record-oriented file system, access methods, query optimization, and query processing.
  
  
• CS244 - Introduction to Embedded and Ubiquitous Systems
  
  Embedded and ubiquitous system technologies including processors, DSP, memory, and software. System interfacing basics; communication strategies; sensors and actuators, mobile and wireless technology. Using pre-designed hardware and software components. Design case studies in wireless, multimedia, and/or networking domains.
  
  
• CS261 - Data Structures
  
  An in-depth treatment of data structures and their associated management algorithms including resource complexity analysis.
  
  
• CS284A - Representations and Algorithms for Molecular Biology
  
  Introduction to computational methods in molecular biology, aimed at those interested in learning about this interdisciplinary area. Covers computational approaches to understanding and predicting the structure, function, interactions, and evolution of DNA, RNA, proteins, and related molecules and processes.
  
  
• CS284B - Probabilistic Modeling of Biological Data
  
  A unified Bayesian probabilistic framework for modeling and mining biological data. Applications range from sequence (DNA, RNA, proteins) to gene expression data. Graphical models, Markov models, stochastic grammars, structure prediction, gene finding, evolution, DNA arrays, single- and multiple-gene analysis.
  
  
• CS284C - Computational Systems Biology
  
  Computational inference and modeling of gene regulation networks, signal transduction pathways, and the effects of regulatory networks in cellular processes, development, and disease. Introduction of required mathematical, computational, and data handling tools.
  
  
• CS288A - Biological Networks
  
  The course introduces the basics of primarily graph theoretic analysis and modeling of biological networks. It presents the necessary biological background for understanding different types of biological networks as well as mathematical, algorithmic, and computational complexity issues associated with them.
  
  
• CS295 - Chemoinformatics
  
  Chemoinformatics.
  
  
• CS295 - XML Data Management
  
  This special topics class will cover XML and related technologies from a data management perspective. Part 1 of the course will cover the basics, including XML itself, DTDs, XML Schema, XPath, XSLT, and Web services. In Part 2 we will turn our attention to XQuery, the XML query language from the World Wide Web consortium (W3C) that lies at the heart of most XML data management approaches and technologies. In addition to XQuery 1.0, we will examine various XQuery extensions that enhance XQuery with support for aggregation, updates, scripting, and full-text searching. We will also cover SQL/XML, a set of SQL extensions for publishing and querying XML data. Part 3 of the course will cover different kinds of industrial systems related to XML data management, including XML support in relational database systems, XML-based data integration middleware, and the role of XML and XQuery in ESBs and business process management systems. Finally, Part 4 will look under the hood of these systems, covering topics including XML storage and indexing (both relational and native) and XML query processing (both for native and federated systems).