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Yuchou Chang

faculty

Yuchou Chang, PhD

Associate Professor

Computer & Information Science


Contact

508-999-8475

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Dion 317B

Education

2012University of Wisconsin-MilwaukeePhD
2006Shanghai Jiao Tong UniversityME
2003Northwestern Polytechnical UniversityBE

Teaching

Programs

Teaching

Courses

Coverage of advanced topics of data mining and its applications. The course will review related mathematics and then focus on data mining core algorithms and advanced modeling including regression, dimensionality reduction, support vector machines, clustering, graph theory, and frequent pattern mining. The course will also explore several real-world problems and discuss strategies for large-scale data. Requires pre-knowledge from an undergraduate course on algorithms and data structures.

Expert system architectures: forward-production, logic programming, deductive retrieval, and semantic network systems. The course also treats natural language systems, illustrative working systems, and AI programming.

Prerequisites: Completion of three core courses.   Development of a detailed, significant project in computer science under the close supervision of a faculty member, perhaps as one member of a student team. This project may be a software implementation, a design effort, or a theoretical or practical written analysis. Project report with optional oral presentation must be evaluated by three faculty members including the project supervisor.  

Prerequisites: Completion of three core courses.   Development of a detailed, significant project in computer science under the close supervision of a faculty member, perhaps as one member of a student team. This project may be a software implementation, a design effort, or a theoretical or practical written analysis. Project report with optional oral presentation must be evaluated by three faculty members including the project supervisor.  

Prerequisites: Completion of three core courses.   Development of a detailed, significant project in computer science under the close supervision of a faculty member, perhaps as one member of a student team. This project may be a software implementation, a design effort, or a theoretical or practical written analysis. Project report with optional oral presentation must be evaluated by three faculty members including the project supervisor.  

A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources. Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system. Oral and written reports and ethical aspects are highlighted.

A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources. Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system. Oral and written reports and ethical aspects are highlighted.

Teaching

Online and Continuing Education Courses

Coverage of advanced topics of data mining and its applications. The course will review related mathematics and then focus on data mining core algorithms and advanced modeling including regression, dimensionality reduction, support vector machines, clustering, graph theory, and frequent pattern mining. The course will also explore several real-world problems and discuss strategies for large-scale data. Requires pre-knowledge from an undergraduate course on algorithms and data structures.
Register for this course.

Research

Research awards

  • $ 1,400,112 awarded by Massachusetts Technology Collaborative for A Digital Twin for the Massachusetts Shellfish Aquaculture Industry
  • $ 20,000 awarded by Office of Naval Research for Tiny ML-UUVs: Tiny Machine Learning for Low-Power Unmanned Undersea Vehicles

Research

Research interests

  • Artificial Intelligence / Machine Learning / Pattern Recognition
  • Biomedical Imaging
  • Intelligent Robotics
  • Brain-Computer Interface
  • Statistical Signal Processing