https://cis.temple.edu/~yu/wanglab/
About The W ireless and A dvanced N etworking G roup (WANG Lab) in the Department of Computer and Information Sciences at Temple University focuses on research that advances the way that people, devices and applications interact in emerging wireless networking, smart sensing, distributed computing, and artificial intelligence. Wang Lab was established in the University of North Carolina at ...
https://cis.temple.edu/~pwang/5603-AI/5603-index.htm
CIS 5603. Artificial Intelligence Section 001, Fall 2025 Syllabus Instructor: Dr. Pei Wang Schedule
https://community.mis.temple.edu/mis5203sec001spring2021/files/2021/02/5203_04_Requirements.pdf
Explain the advantages and pitfalls of observing workers and analyzing business documents to determine system requirements. Explain how computing can provide support for requirements determination.
https://liberalarts.temple.edu/directory/lee-hachadoorian
Expertise Open Source GIS, Open Data, Urban Economic Geography, Spatial Analysis, Residential Location, Segregation, Local Public Finance, Suburbanization and Sprawl, Metropolitan Governance, Spatial Justice Biography I am an assistant professor of instruction in Temple University’s Department of Geography & Urban Studies, and the assistant director of the professional science master’s in ...
https://sites.temple.edu/janederoseevans/numismatic-work-at-sardis-turkey/
“A New Revival of an Old Coin Type: Sardis in the Augustan Era”, in Concordia Disciplinarum: Essays on Ancient Coinage, History, and Archaeology in Honor of William E. Metcalf. Edited by Nathan T. Elkins and Jane DeRose Evans. American Numismatic Society 2019.
https://cis.temple.edu/~jiewu/research/publications/Publication_files/INFOCOM2024_PSFL%20Parallel-Sequential%20Federated%20Learning%20with%20Convergence%20Guarantees.pdf
Abstract—Federated Learning (FL) is a novel distributed learning paradigm which can coordinate multiple clients to jointly train a machine learning model by using their local data samples. Existing FL works can be roughly divided into two categories according to the modes of model training: Parallel FL (PFL) and Sequential FL (SFL). PFL can speed up each round of model training time through ...