faculty
Hua Fang, PhD
Professor
Computer & Information Science
Contact
508-910-6411
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Violette Research 220B
Education
2006 | Ohio University | PhD in Statistics |
2006 | Ohio University | MA/ MFE in Financial Economics |
1998 | Sichuan International Studies University, Chongqing, China | BA in Business, English |
Teaching
Programs
Programs
- Computer Science BS, BS/MS
- Computer Science Cybersecurity
- Computer Science Graduate Certificate
- Computer Science MS
- Data Science BS, BS/MS
- Data Science Graduate Certificate
- Data Science MS
- Engineering and Applied Science PhD
- Mobile Applications Development
- Software Engineering
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.
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.
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 ECE Graduate Program Director. 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 ECE Graduate Program Director.
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 ECE Graduate Program Director. 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 ECE Graduate Program Director.
Research
Research interests
- Machine learning/ statistical learning/ pattern recognition
- Computational statistics
- Behavioral trajectory pattern recognition in longitudinal studies
- Wireless health