Dr. Tinghao Feng
Assistant Professor
Research Concentration
My research focuses on time-oriented data analysis, including the development of visualization and analysis systems for various types of time series data. I aim to provide comprehensive tools for exploring and understanding temporal patterns and trends in diverse datasets. This involves creating innovative methods for visualizing complex data, developing algorithms for data mining and pattern recognition, and integrating these tools into user-friendly software applications. My research has applications in numerous fields, including environmental science, healthcare, and finance, where understanding time-based data is crucial for making informed decisions.
Courses Taught
- CS3430 Database
- CS4755 Applied Machine Learning
- CS3533 Visual Analytics
Publications
- Feng, T., Arkle, S., Campell, C., & Tashakkori, R. (2024, March). Bee Vis: A Comprehensive Honey Bee Data Visualization, Exploration, and Analysis System. In SoutheastCon 2024 (pp. 1274-1281). IEEE. DOI: 10.1109/SoutheastCon52093.2024.10500272
- Feng, T., Hu, Y., Yang, J., Polk, T., Zhao, Y., Liu, S., & Yang, Z. (2023, October). TimePool: Visually Answer “Which and When” Questions On Univariate Time Series. In 2023 IEEE Visualization and Visual Analytics (VIS) (pp. 201-205). IEEE. DOI: 10.1109/VIS54172.2023.00049
- Liu, P., Feng, T., & Liu, R. (2022). Quantifying syntax similarity with a polynomial representation of dependency trees. arXiv preprint arXiv:2211.07005. DOI: 10.53482/2022_53_402
- Eppes, M. C., Feng, T., & Yang, J. (2021, December). Landslide by a thousand cuts: Exploring the role of subcritical cracking, combined stresses and climate in driving critical rock failures. In AGU Fall Meeting Abstracts (Vol. 2021, pp. NH43A-05).
- Feng, T., Yang, J., Eppes, M.-C., Yang, Z., & Moser, F. (2021). EVis: Visually Analyzing Environmentally Driven Events. IEEE Transactions on Visualization and Computer Graphics, 1–1. DOI: 10.1109/TVCG.2021.3114867
- De, A., Huang, M., Feng, T., Yue, X., & Yao, L. (2021). Analyzing Patient Secure Messages Using a Fast Health Care Interoperability Resources (FIHR)–Based Data Model: Development and Topic Modeling Study. Journal of Medical Internet Research, 23(7): e26770. DOI: 10.2196/26770
- Chen, S., Paul, R., Janies, D., Murphy, K., Feng, T., et al. (2021). Exploring Feasibility of Multivariate Deep Learning Models in Predicting COVID-19 Epidemic. Frontiers in Public Health, 9. DOI: 10.3389/fpubh.2021.661615
- Eppes, M. C., Magi, B., Scheff, J., Warren, K., Ching, S., & Feng, T. (2020). Warmer, Wetter Climates Accelerate Mechanical Weathering in Field Data, Independent of Stress‐Loading. Geophysical Research Letters, 47(24), 2020GL089062. DOI: 10.1029/2020GL089062
- Huang, F., Yu, Y., & Feng, T. (2019). Automatic building change image quality assessment in high resolution remote sensing based on deep learning. Journal of Visual Communication and Image Representation, 63, 102585. DOI: 10.1016/j.jvcir.2019.102585
- Huang, F., Yu, Y., & Feng, T. (2019). Automatic extraction of urban impervious surfaces based on deep learning and multi-source remote sensing data. Journal of Visual Communication and Image Representation, 60, 16-27. DOI: 10.1016/j.jvcir.2018.12.051
- Huang, F., Yu, Y., & Feng, T. (2019). Automatic extraction of impervious surfaces from high resolution remote sensing images based on deep learning. Journal of Visual Communication and Image Representation, 58, 453-461. DOI: 10.1016/j.jvcir.2018.11.041
- Huang, F., Yu, Y., & Feng, T. (2019). Hyperspectral remote sensing image change detection based on tensor and deep learning. Journal of Visual Communication and Image Representation, 58, 233-244. DOI: 10.1016/j.jvcir.2018.11.004