Smart Cities and Connected Communities
Integrating people, data, devices, infrastructure, and institutions to realize the potential of communities.
Research Projects
Personalized Account and Community Management Tool
Principal Investigator(s): Jennifer Golbeck
Funder: Intelligent Automation, Inc Other
Research Areas: Future of Work > Smart Cities and Connected Communities
Personalized Account and Community Management Tool
Principal Investigator(s): Jennifer Golbeck
Funder: Intelligent Automation, Inc Other
Research Areas: Future of Work > Smart Cities and Connected Communities
Personalized Account and Community Management Tool
Convergence Accelerator Phase I (RAISE): Credible Open Knowledge Network
Principal Investigator(s): Naeemul Hassan
Funder: The University of Texas, Arlington Other
Research Areas: Computational Linguistics, Machine Learning, and Information Retrieval > Smart Cities and Connected Communities
Principal Investigator(s): Naeemul Hassan
Funder: The University of Texas, Arlington Other
Research Areas: Computational Linguistics, Machine Learning, and Information Retrieval > Smart Cities and Connected Communities
Long Term Multi-Instruments Land Surface Reflectance Record and Applications
Principal Investigator(s): Sergii Skakun
Funder: NASA - Goddard Space Flight Center Other
Research Areas: Computational Linguistics, Machine Learning, and Information Retrieval > Future of Work > Smart Cities and Connected Communities
Principal Investigator(s): Sergii Skakun
Funder: NASA - Goddard Space Flight Center Other
Research Areas: Computational Linguistics, Machine Learning, and Information Retrieval > Future of Work > Smart Cities and Connected Communities
Faculty
Recent News
A farmer tends crops in Uganda, an area where bias in AI algorithms monitoring global crop health might misinterpret data due to a lack of knowledge of local growing methods. Photo by Nuno Almeida, Dreamstime.com
UMD Co-leads $750K NSF, Amazon Project to Tackle AI Bias in Mapping
INFO College's Dr. Sergii Skakun will play a key role in building the system that could enable fairer, safer decisions on resource distribution
Dr. Zubin Jelveh: Machine Learning Can Predict Shooting Victimization Well Enough to Help Prevent It
Using arrest and victimization records from the Chicago PD, a machine learning model can predict the risk of being shot in the next 18 months.
Photo by Julia M. Cameron