People
Hong Huang
Associate Professor
CONTACT
Office: CIS 2040
Email
BIO
Dr. Hong Huang is an Associate Professor in the School of Information at the University
of South Florida. He received a B.S. degree in Biochemistry, both M.S. degrees in
Genetics and Computer Science, and a Ph.D. in Information from Florida State University.
His research and teaching areas encapsulate three related disciplines - information
and library science, bioinformatics, and information & learning technology. With his
extensive LIS, bioinformatics/genetics backgrounds and work experiences, he bridges
these disciplines in various ways. His research interests include data management,
AI/ML data practice and sharing, IT in library and education, as well as bio/health information and learning. He published 140 peer review publicaitons and
conference presentations. He is the PIs or Co-PIs with collaborative and federal grant awards (e.g., USDA) in
data management & practice, and IT in learning science. He served as the Associated
Editor for Journal of Information and Learning Sciences (Emerald), the Editorial Board Member Library & Information Science Research (Elsevier).
EDUCATION
- Ph.D., Florida State University
- M.S., Florida State University
- M.S., Florida A&M University
- B.S., Zhongshan (Sun Yetsen) University
Recent Publications & Research
- Huang H., Yu H., Li W. (2024). Assessing the importance of content versus design for successful crowdfunding of health education games: online survey study. JMIR Serious Game (DOI:10.2196/39587).
- Rathke, B. Han Y. Huang H. (2023). What remains now that the fear has passed: Developmental Trajectory Analysis of COVID-19 Pandemic for co-occurances of Twitter, Google Trends, and Public Health Data, Disaster Medicine and Public Health Prepardness, (DOI:10.1017/dmp.2023.101).
- Huang, H., Qin J. (2023). Metadata functional requirements for genomic data practice and curation. Information Research, (In press).
- Oduro M., Yu H., Huang H. (2022) Entrepreneurship success: predicting crowdfunding campaigns using model-based machine learning methods. International Journal of Crowd Science, 6(1), 7-16. IEEE.org. (DOI:10.26599/IJCS.2022.9100003).
- Huang H., Li Y. (2021). Exploring the motivation of livestreamed users in learning computer programming and coding. The Electronic Journal of e-Learning, 19(5), 363-375. (DOI:10.34190/ejel.19.5.2470).