Customized Computer Vision with Transfer-Learning for TAMU Gameday Pedestrian and Vehicle Counting
Date and Time: Thursday, May 29, 2025: 10:30 AM - 12:00 PM
Presenter: Yalong Pi | Associate Research Scientist | Texas A&M Institute of Data Science
Speaker Biography
Dr. Yalong Pi is an Associate Research Scientist at the Texas A&M Institute of Data Science (TAMIDS). With a diverse professional background, he previously worked as an Engineer at Gree Real Estate Co. Ltd in Zhuhai City, China, from 2013 to 2016 and as a junior architect at Shanghai Water&Rock Group in 2017. He completed his post-doc training at the Texas A&M Institute of Data Science. Prior to that, he obtained a Ph.D. in Architecture/Construction from Texas A&M University (2020), an M.S. of Civil Engineering (2013) from Wuhan Institute of Technology, and a B.S. in Mechanical Engineering (2011) from Wuhan Textile University. He is a certified Architectural Engineer in China. Pi’s research focuses on computer vision applications in disaster reconnaissance, transportation information retrieval, precision livestock farming, and medical confocal imagery. Pi is skilled in traditional and deep learning based computer vision techniques, geographic information system (GIS), Python programming, and high performance computing (HPC).
Co-presenter(s):
Nick Duffield Texas A&M Institute of Data Science
Speaker Biography
Dr. Nick Duffield is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, holder of the Royce E. Wisenbaker Professorship I, and Director of the Texas A&M Institute of Data Science. He is co-director of the NSF TRIPODS Research Institute on the Foundations of Interdisciplinary Data Science at Texas A&M, serves on the Executive Committee of the Texas A&M Center for Geospatial Sciences, Applications and Technology (GEOSAT), and holds a courtesy appointment in the Texas A&M Department of Computer Science and Engineering. His research combines the foundations and applications of data science and computer networking, currently graph stream sampling, network measurement and resilience, and applications of data science to transportation, agriculture, hydrology, infrastructure, and operations. In his TAMIDS role, he directs the MS in Data Science in collaboration with the Colleges of Science and Engineering,
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