Research Projects
Filtered by: Machine Learning, AI, Computational Linguistics, and Information Retrieval
Accelerating Cross-Disciplinary Innovation with Computational Analogy
Principal Investigator(s): Joel Chan
Funder: US Office of Naval Research
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval
Investigating how to develop interactive search engines that enable scientists and inventors to discover and adapt ideas across disciplinary boundaries.
Principal Investigator(s): Joel Chan
Funder: US Office of Naval Research
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval
Investigating how to develop interactive search engines that enable scientists and inventors to discover and adapt ideas across disciplinary boundaries.
Capturing Computational Thinking Literacy Development in Public Libraries
Principal Investigator(s): Mega Subramaniam
Funder: Institute of Museum and Library Services
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval > Library and Information Science
Identifying the learning outcomes that can be achieved through CT programs for youth offered through libraries and to develop a bank of assessment tools that can be used by public library staff to document and measure CT literacy development in youth as a result of participating in library CT programs.
Principal Investigator(s): Mega Subramaniam
Funder: Institute of Museum and Library Services
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval > Library and Information Science
Identifying the learning outcomes that can be achieved through CT programs for youth offered through libraries and to develop a bank of assessment tools that can be used by public library staff to document and measure CT literacy development in youth as a result of participating in library CT programs.
CHS: Small: Teachable Object Recognizers for the Blind
Principal Investigator(s): Hernisa Kacorri
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Human-Computer Interaction > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The research aims to develop a teachable object recognizer (TOR) app for blind users, enabling them to train machine learning models with personalized data through their smartphone cameras. This "teachability" approach addresses data scarcity in assistive technology. The study will explore effective user training, measure system efficacy, and evaluate accessible interactions through various research methods, aiming to improve the robustness of assistive tech.
Principal Investigator(s): Hernisa Kacorri
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Human-Computer Interaction > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The research aims to develop a teachable object recognizer (TOR) app for blind users, enabling them to train machine learning models with personalized data through their smartphone cameras. This "teachability" approach addresses data scarcity in assistive technology. The study will explore effective user training, measure system efficacy, and evaluate accessible interactions through various research methods, aiming to improve the robustness of assistive tech.
Computational Thinking to Unlock the Japanese American WWII Camp Experience
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Exploring the legacy of WWII Japanese American Incarceration through computational archival science approaches.
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Exploring the legacy of WWII Japanese American Incarceration through computational archival science approaches.
Designing a Computer Science Pre-service Teacher Methods Course for Maryland
Principal Investigator(s): David Weintrop
Funder: University System of Maryland Other
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval > Youth Experience, Learning, and Digital Practices
Principal Investigator(s): David Weintrop
Funder: University System of Maryland Other
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval > Youth Experience, Learning, and Digital Practices
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.
FAI: Advancing Deep Learning Towards Spatial Fairness
Principal Investigator(s): Sergii Skakun
Funder: University of Pittsburgh National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The project aims to address spatial biases in AI, ensuring spatial fairness in real-world applications like agriculture and disaster management. Traditional machine learning struggles with spatial fairness due to data variations. The project proposes new statistical formulations, network architectures, fairness-driven adversarial learning, and a knowledge-enhanced approach for improved spatial dataset analysis. The results will integrate into geospatial software.fference between habits and behaviors ef
Principal Investigator(s): Sergii Skakun
Funder: University of Pittsburgh National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The project aims to address spatial biases in AI, ensuring spatial fairness in real-world applications like agriculture and disaster management. Traditional machine learning struggles with spatial fairness due to data variations. The project proposes new statistical formulations, network architectures, fairness-driven adversarial learning, and a knowledge-enhanced approach for improved spatial dataset analysis. The results will integrate into geospatial software.fference between habits and behaviors ef
IARPA BETTER: Multilingual Fine-grained Decompositional Analysis
Principal Investigator(s):
Funder: USODNI Intelligence Advanced Research Projects Activity
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval
Developing enhanced methods for personalized, multilingual semantic extraction and retrieval from text, in support of IARPA's goal of providing users with a system that quickly and accurately extracts complex semantic information, targeted for a specific user, from text.
Principal Investigator(s):
Funder: USODNI Intelligence Advanced Research Projects Activity
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval
Developing enhanced methods for personalized, multilingual semantic extraction and retrieval from text, in support of IARPA's goal of providing users with a system that quickly and accurately extracts complex semantic information, targeted for a specific user, from text.
III: Small: Bringing Transparency and Interpretability to Bias Mitigation Approaches in Place-based Mobility-centric Prediction Models for Decision
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Health Informatics > Information Justice, Human Rights, and Technology Ethics > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The project focuses on improving the fairness of place-based mobility-centric (PBMC) prediction models, particularly in high-stakes scenarios like public health and safety. By addressing biases in COVID-19 mobility and case data, it aims to make predictions more accurate and equitable. The research introduces novel bias-mitigation and interpretability methods across three technical thrusts, promoting transparency in PBMC models.
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Health Informatics > Information Justice, Human Rights, and Technology Ethics > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The project focuses on improving the fairness of place-based mobility-centric (PBMC) prediction models, particularly in high-stakes scenarios like public health and safety. By addressing biases in COVID-19 mobility and case data, it aims to make predictions more accurate and equitable. The research introduces novel bias-mitigation and interpretability methods across three technical thrusts, promoting transparency in PBMC models.
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.
Machine Learning Strategies for FDR Presidential Library Collections (ML-FDR)
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Demonstrate computational treatments of digital cultural assets using Artificial Intelligence (AI) and Machine Learning (ML) techniques that can help unlock hard-to-reach archival content related to WWII-era records housed at the FDR Presidential Library. This content is under-utilized by scholars examining American responses to the Holocaust.
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Demonstrate computational treatments of digital cultural assets using Artificial Intelligence (AI) and Machine Learning (ML) techniques that can help unlock hard-to-reach archival content related to WWII-era records housed at the FDR Presidential Library. This content is under-utilized by scholars examining American responses to the Holocaust.
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