Hua Fang

faculty

Hua Fang, PhD

Professor

Computer & Information Science

Computational Statistics and Data Science Lab Website

508-910-6411

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Violette Research 220B

Education

2006Ohio UniversityPhD in Statistics
2006Ohio UniversityMA/ MFE in Financial Economics
1998Sichuan International Studies University, Chongqing, ChinaBA in Business, English

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.

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.  

Investigations of a fundamental and/or applied nature representing an original contribution to the scholarly research literature of the field. PhD dissertations are often published in refereed journals or presented at major conferences. A written dissertation must be completed in accordance with the rules of the Graduate School and the College of Engineering. Admission to the course is based on successful completion of the PhD comprehensive examination and submission of a formal proposal endorsed by the student's graduate committee and submitted to the EAS Graduate Program Director.

Research

Research interests

  • Machine learning/ statistical learning/ pattern recognition
  • Computational statistics
  • Behavioral trajectory pattern recognition in longitudinal studies
  • Wireless health

External links