EECS Associate Professor James Miller and EECS student David Wolfe develop interactive educational tools. Prof. Miller helped lead interdisciplinary research to create a virtual environment that taught social skills to students with Autism Spectrum Disorders.
EECS researchers are developing new tools and methods in data collection, integration, cleansing, and representation. In addition to innovative data gathering and management, EECS researchers advance all aspects of modeling, including model construction, selection, averaging, evaluation, and interpretation. The multidisciplinary research identifies and deploys data science and engineering components for efficient solutions to real-world problems in medicine, science, industry, and other numerous other fields.
Associated Disciplines
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Associated Programs
Associated Faculty
Primary Research Interests
- Intelligent Systems
- Robotics
- Medical Applications of Artificial Intelligence
- Software Engineering
Primary Research Interests
- Knowledge Discovery
- Data Mining
- Machine Learning
- Expert Systems
- Reasoning Under Uncertainty
Primary Research Interests
- Algorithm Design and Analysis
- Combinatorial Optimizations
- Graph Algorithms
Primary Research Interests
- Information security and privacy, database security
- Information retrieval, Web and online social networks
- Security and privacy issues in smart grid systems
- XML and conventional database systems, data management
Primary Research Interests
- Computer Graphics
- Visualization
- Geometric Modeling
- Technology in Education
Primary Research Interests
- High Performance Scientific Computing Algorithms
- Parallel Unstructured Mesh and Optimization Algorithms
- Model Order Reduction
- Computational Medicine
- Image Processing
Associated Facilities
- Multiagent development tools: ACCS, C++, CORBA, Java
- Information retrieval and Web tools: KUIR Information Retrieval Library, Php, XMLSpy, MySQL, Perl
- Data Mining Tools: SNOB, Cobweb, ID3, C4.5, statistical analysis packages
- Artificial intelligence development tools and languages: Lisp, CLOS, CLIPS, Prolog, GBB, OPS, MEM-1
- Image processing and computer vision tools: KUIM Image Processing Library, high-speed video, and data cable/fiber link
- Human-intelligent system interaction tools: Mobile robots, VR user interface, head-mounted display, force feedback joysticks
- PoepleBot, two Nomad Scouts, three Kheperas, and one Pioneer robot
- Software packages for virtual prototyping and kinematics and dynamics modeling, such as the visualNastran 4D and Working Model.
Program Objectives
- Understand fundamental principles and algorithms of data science and engineering.
- Understand the data collection, data integration, data cleansing, data representation, model construction, model selection, model averaging, model evaluation, and model interpretation components in data science and engineering.
- Understand how to identify data science and engineering components and provide efficient solutions in solving real-world problems.
- Have the ability to effectively communicate to impact technological decisions.
Core Coursework (MS)
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Note: Students must take either EECS 740 or 741, credit will not be given for both.
Elective Coursework (MS)
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