Gang Shao

University Title:
Assistant Professor
Research Data
Stewart Center

Professional Information

Faculty Rank:
Assistant Professor
Liaison Areas:
Computer Science
Courses Taught:
ILS595 Geospatial Programming and Data Science (Spring 2021)
ILS295 Intro to Data Lifecycle Management (Fall 2020)
Research Areas:
Data Science; Data Science Education; Machine Learning; Artificial Intelligent; Image/video processing; Text analysis; Digital environmental analysis (Precision Ag and Digital Forest)
Ph.D., Forestry and Natural Resources, Purdue University, 2016
M.S., Forestry and Natural Resources, Purdue University, 2012
B.S., Biomedical Engineering, Northeastern University (CN), 2009
Professional Experience:
Assistant Professor, PULSIS, Purdue University, 2019 - present
Honors and Awards:
Joanne J. Troutner Innovative Educators Award, 2021
IDEA institute on AI, IMLS-funded Fellow 2021


Selected publications, more on Google Scholar (Journal Impact Factors updated in 2021)

Shao, G., Quintana, J. P., Zakharov, W., Purzer, S., & Kim, E. (2021). Exploring potential roles of academic libraries in undergraduate data science education curriculum development. The Journal of Academic Librarianship, 47(2), 102320. (Journal Impact Factor: 1.533)

Li, S., Liu, Y., Her, Y., Chen, J., Guo, T., & Shao, G. (2021). Improvement of simulating sub-daily hydrological impacts of rainwater harvesting for landscape irrigation with rain barrels/cisterns in the SWAT model. Science of The Total Environment, 149336. (Journal Impact Factor: 7.963)

Hu, T., Toman, E. M., Chen, G., Shao, G., Zhou, Y., Li, Y., ... & Feng, Y. (2021). Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 176, 250-261. (Journal Impact Factor: 8.979)

Guo, T., Johnson, L. T., LaBarge, G. A., Penn, C. J., Stumpf, R. P., Baker, D. B., & Shao, G. (2020). Less agricultural phosphorus applied in 2019 led to less dissolved phosphorus transported to Lake Erie. Environmental Science & Technology, 55(1), 283-291. (Journal Impact Factor: 9.028)

Shao, G., Stark, S. C., de Almeida, D. R., & Smith, M. N. (2019). Towards high throughput assessment of canopy dynamics: The estimation of leaf area structure in Amazonian forests with multitemporal multi-sensor airborne lidar. Remote Sensing of Environment, 221, 1-13. (Journal Impact Factor: 10.164)

Almeida, D. R. A. D., Stark, S. C., Shao, G., Schietti, J., Nelson, B. W., Silva, C. A., ... & Brancalion, P. H. S. (2019). Optimizing the remote detection of tropical rainforest structure with airborne lidar: Leaf area profile sensitivity to pulse density and spatial sampling. Remote Sensing, 11(1), 92. (Journal Impact Factor: 4.848)

Shao, G., Shao, G., Gallion, J., Saunders, M. R., Frankenberger, J. R., & Fei, S. (2018). Improving Lidar-based aboveground biomass estimation of temperate hardwood forests with varying site productivity. Remote Sensing of Environment, 204, 872-882. (Journal Impact Factor: 10.164)

Other Information

D-VELoP lab workshops on Machine Learning and Data Visualization: Data Science Libguide: