Research Projects
Filtered by: Future of Work
CAREER: Data-driven Models of Human Mobility and Resilience for Decision Making
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Future of Work
Using cell-phone data to better understand the reactions and movement of people in violent or disaster events, specifically looking at droughts in Haiti, armed conflicts in Colombia, and floods in Bangladesh, with the aim of providing decision makers with data-driven models they can use to create preparedness and response plans.
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Future of Work
Using cell-phone data to better understand the reactions and movement of people in violent or disaster events, specifically looking at droughts in Haiti, armed conflicts in Colombia, and floods in Bangladesh, with the aim of providing decision makers with data-driven models they can use to create preparedness and response plans.
CRII: CHS: Investigating Multilingual Teams Communication and Collaborative Writing
Principal Investigator(s): Ge Gao
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Future of Work > Information Justice, Human Rights, and Technology Ethics > Youth Experience, Learning, and Digital Practices
This project investigates new ways to create grounding in multilingual teams engaged in collaborative writing. It will improve understanding and develop new tools.
Principal Investigator(s): Ge Gao
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Future of Work > Information Justice, Human Rights, and Technology Ethics > Youth Experience, Learning, and Digital Practices
This project investigates new ways to create grounding in multilingual teams engaged in collaborative writing. It will improve understanding and develop new tools.
Earth Observation for National Agricultural Monitoring
Principal Investigator(s): Sergii Skakun
Funder: NASA Other
Research Areas: Future of Work > Machine Learning, AI, Computational Linguistics, and Information Retrieval
This project aims to advance national agriculture monitoring with Earth Observations (EO) data in East and Southern Africa using machine learning tools and open source data to develop baseline datasets.
Principal Investigator(s): Sergii Skakun
Funder: NASA Other
Research Areas: Future of Work > Machine Learning, AI, Computational Linguistics, and Information Retrieval
This project aims to advance national agriculture monitoring with Earth Observations (EO) data in East and Southern Africa using machine learning tools and open source data to develop baseline datasets.
Human-Agent Teaming on Intelligence Tasks
Principal Investigator(s): Susannah Paletz
Funder: US Army Research Office
Research Areas: Future of Work
Our goal is to create a platform for running experiments that would simulate an AI intervention into intelligence analysis tasks, specifically involving a human shift handover. Participants would work through materials, including notes and feedback from the “previous” analyst, to solve a fictional intelligence task. This study examines how potential AIs can influence team cognition and decision making.
Principal Investigator(s): Susannah Paletz
Funder: US Army Research Office
Research Areas: Future of Work
Our goal is to create a platform for running experiments that would simulate an AI intervention into intelligence analysis tasks, specifically involving a human shift handover. Participants would work through materials, including notes and feedback from the “previous” analyst, to solve a fictional intelligence task. This study examines how potential AIs can influence team cognition and decision making.
Long Term Multi-Instruments Land Surface Reflectance Record and Applications
Principal Investigator(s): Sergii Skakun
Funder: NASA - Goddard Space Flight Center Other
Research Areas: Future of Work > Machine Learning, AI, Computational Linguistics, and Information Retrieval > Smart Cities and Connected Communities
The long-term data record (LTDR) from the Advanced Very High-Resolution Radiometer (AVHRR) provides daily surface reflectance with global coverage from the 1980s to present day, making it a unique source of information for the study of land surface properties and their long-term dynamics. Surface reflectance is a critical input for the generation of products such as vegetation indices, albedo, and land cover. Therefore, it is of utmost importance to quantify its uncertainties to better understand how they might propagate into downstream products.
Principal Investigator(s): Sergii Skakun
Funder: NASA - Goddard Space Flight Center Other
Research Areas: Future of Work > Machine Learning, AI, Computational Linguistics, and Information Retrieval > Smart Cities and Connected Communities
The long-term data record (LTDR) from the Advanced Very High-Resolution Radiometer (AVHRR) provides daily surface reflectance with global coverage from the 1980s to present day, making it a unique source of information for the study of land surface properties and their long-term dynamics. Surface reflectance is a critical input for the generation of products such as vegetation indices, albedo, and land cover. Therefore, it is of utmost importance to quantify its uncertainties to better understand how they might propagate into downstream products.
My Creative AI Co-Worker
Principal Investigator(s):
Research Areas: Accessibility and Inclusive Design > Future of Work > Human-Computer Interaction > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Principal Investigator(s):
Research Areas: Accessibility and Inclusive Design > Future of Work > Human-Computer Interaction > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Theory of Change Through Data
Principal Investigator(s): Wayne G. Lutters
Funder: New York University Other
Research Areas: Future of Work > Human-Computer Interaction > Library and Information Science
The Democratizing Data project supports federal agencies in understanding how their data assets are being used. Novel machine learning algorithms interrogate over 90 million publications to identify data usage, which is presented via a search and discovery platform with three interaction styles: interactive dashboards, Jupyter Notebooks, or direct API.
Principal Investigator(s): Wayne G. Lutters
Funder: New York University Other
Research Areas: Future of Work > Human-Computer Interaction > Library and Information Science
The Democratizing Data project supports federal agencies in understanding how their data assets are being used. Novel machine learning algorithms interrogate over 90 million publications to identify data usage, which is presented via a search and discovery platform with three interaction styles: interactive dashboards, Jupyter Notebooks, or direct API.
UMD Break Through Tech
Principal Investigator(s):
Funder: Cornell University Other
Research Areas: Future of Work
Break Through Tech works at the intersection of academia and industry to propel more women and underrepresented communities into technology education and careers. Break Through Techs' goal is to achieve gender equality in tech.
Principal Investigator(s):
Funder: Cornell University Other
Research Areas: Future of Work
Break Through Tech works at the intersection of academia and industry to propel more women and underrepresented communities into technology education and careers. Break Through Techs' goal is to achieve gender equality in tech.