Project Grant W911NF1910094

This National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) Project Grant award of $200,592 to Northwestern University will fund the "Collaborative Research: HNDS-R: Advancing Diverse Viable Effective Networked Teams (ADVENT)" project. The research aims to create an open-source computational network model to optimize team formation by integrating functional individual differences and member affinity. The researchers will train and test this model...

This Project Grant award from the National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program provides $106,000 to the Research Foundation of the City University of New York (RFCUNY) to conduct research on designing smart environments to enhance collective learning and creativity. The key products and services to be delivered under this 3-year award include: Developing large-scale online experiments and computational models to investigate how technological...

This three-year Project Grant from the National Science Foundation's Division of Computing and Communication Foundations under the Computer and Information Science and Engineering program will support the development of new optimization approaches for machine learning problems. The $600,000 award to the University of Wisconsin-Madison beginning October 1, 2022 will advance optimization algorithms and analysis techniques for convex-concave minimax problems incorporating sparsity or regularity....

This three-year, $330,000 National Science Foundation Social, Behavioral, and Economic Sciences project grant will fund research at Harvard University evaluating the impacts of machine learning algorithms on human decisions. Principal Investigator James Greiner will lead a team that includes Zhichao Jiang of the University of Massachusetts, who will help direct the project. The team will develop statistical frameworks to estimate the causal effects of algorithm-generated pretrial risk assessment...

This $323,388 award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports a collaborative research project to examine how smart technological environments can be optimized to augment collective learning and creativity. The research aims to investigate the parameters that drive group-directed learning in three key areas: cooperative behavior, collective intelligence, and collective creativity. The project involves large-scale...

This $299,862 National Science Foundation project grant under the Computer and Information Science and Engineering program will support the development of a decision support system to strengthen public health response to infectious disease outbreaks in the US and Australia. Researchers from the University of Texas at Austin and Commonwealth Scientific and Industrial Research Organisation (CSIRO) in Australia will collaborate to develop graph representation learning methods for fair teaming...

This National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant awarded to the Regents of the University of Michigan in the amount of $574,681 will support research on informed, fair, efficient, and incentive-aware group decision making. The project aims to develop new models, mechanisms, and methods that can incentivize individuals to ensure group decisions achieve desired outcomes, particularly in fast-paced and increasingly online...

This three-year, $299,938 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop a decision support system and fair teaming recommendations for infectious disease response. Funded through a collaboration between the University of California, Los Angeles and the Australian Commonwealth Scientific and Industrial Research Organisation, the project will construct biomedical knowledge graphs, identify core response teams, and...

This $349,427 CAREER grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research at The Leland Stanford Junior University (Stanford University) to develop machine learning (ML) techniques to improve the performance of discrete optimization algorithms. The project aims to address challenges in efficiently solving complex combinatorial optimization problems, such as those encountered in supply chain logistics and...

This National Science Foundation (NSF) Project Grant award under the Social, Behavioral, and Economic Sciences (CFDA 47.075) program provides $225,974 to Cornell University to investigate how technological augmentations can enhance the learning and effectiveness of work teams, community groups, classrooms, and other collectives. The research examines the parameters that drive group-directed learning in areas such as cooperative behavior, collective intelligence, and collective creativity. The...