Active Grants/Awards

Past Grants/Awards

Teacher Retirement System of Texas Website Usability Testing

The Teacher Retirement System of Texas (TRS) is the largest public retirement system in Texas, serving nearly 1.9 million people. TRS disseminates its information to the public through a variety of outlets, including the TRS website (www.trs.texas.gov), which provides access to the member portal, TRS publications, forms, and other informational content. TRS is undertaking a robust, multi-year enterprise project to rewrite, streamline, restructure, and redesign TRS’ website to meet the highest standards of usability and web accessibility. 

As part of the re-design process to build a user-centric website, TRS plans to conduct a series of formal usability test sessions. TRS requires support to organize and facilitate the website usability testing sessions. The contractor shall support TRS with tasks such as assisting with recruiting and scheduling participants, planning, and conducting the usability test sessions and generating reports to be shared with stakeholders. The participants for the test sessions will be representative of potential users of the TRS website, will be drawn from the web-using public, and will be recruited according to other reasonable demographic screening criteria specified by TRS. 

Andrew Dillon
Agency
TRS/ Texas Teacher Retirement System
Grant Dates
Sep 20, 2024 - Aug 31, 2025
Funding

The award is $49,500 over the project period.

Award Number
CTR002809

Informing Memory Institutions and Humanities Researchers of the Broader Impact of Open Data Sharing via Wikidata

This project proposes to investigate how GLAM institutions and humanities researchers can help to address knowledge gaps by contributing their local collections, metadata, and records to Wikidata. Many GLAM institutions and projects from humanities scholars (e.g. the Womenʼs Print History Project, a bibliographical database) hold valuable information andhistorical artifacts that have the potential to fill critical knowledge gaps on Wikidata; however, these contributors often lack visibility into what knowledge gaps their local databases are well positioned to address. This limits their ability to effectively organize their efforts and fulfill their mission of sharing human knowledge and mitigating epistemic injustice.

The goal is to develop an assessment tool that can highlight what past contributions are particularly valuable for addressing knowledge gaps and providing unique coverage relative to other knowledge technologies (search engines and LLMs). This tool will help GLAM and humanities contributors to better understand the unique value of their various contributions and make informed decisions about their future focus. The successful completion of the project will help GLAM institutions and humanities scholars optimize their efforts to mitigate the most critical gaps in the knowledge ecosystem.

Hanlin Li
Agency
Wikimedia Foundation, Inc.
Grant Dates
Jul 15, 2025 - Jan 15, 2027
Funding

The award is $49,450 over the project period.

Closing the loop with an automatic referral population and summarization system

In the United States, more than a third of patients are referred to a specialist each year, and specialist visits constitute more than half of outpatient visits. Even though all physicians highly value communication between primary care providers (PCPs) and specialists, both PCPs and specialists cite the lack of effective information transfer as one of the most significant problems in the referral process. Therefore, it is critical to investigate a new method to improve communication during care transitions. With their ubiquitous use, it is recognized that electronic health records (EHRs) should ensure a seamless flow of information across healthcare systems to improve the referral process. But, a lack of accessible and relevant information in the referral process remains a pressing problem. Recently, emerging deep learning (DL) and natural language processing (NLP) methods have been successfully applied in extracting pertinent information from EHRs and generating text summarization to improve care quality and patient outcomes. However, existing technologies cannot be applied to process heterogeneous data from EHRs and create high-quality clinical summaries for communicating a reason for referral. 

Responding to PA-20-185, this project will develop and validate a novel informatics framework to collect and synthesize longitudinal, multimodal EHR data for automatic referral form generation and summarization. While the referring provider and specialist can be any type of provider for any condition, the focus in this application has been on headache for primary care, because it is an extremely common symptom and affects people of all ages, races, and socioeconomic statuses. More importantly, relevant information needed for headache referrals has been defined in local and national evidence-based practice guidelines. Therefore, a health information technology solution to make these data accessible will empower communication between PCPs and specialists, which can improve the care of millions of patients suffering from disabling headache disorders. 

Based on our preliminary data and our experience with an interdisciplinary team of data scientists and physicians, we plan to execute specific aims: 1) Convert text-based guidelines into a standards-based algorithm for electronic implementation; 2) develop models to automatically populate data from EHR and clinical notes to fill the referral form; 3) create a framework to summarize the longitudinal clinical notes to fill out the referral form; and 4) develop and validate the headache referral system with a user-centered design approach. The research proposed in this project is novel and innovative because it will produce and rigorously test new solutions to improve the communication between health professionals to ensure that safe, high-quality care is provided and care continuity is maintained. The success of this project will (1) fill important gaps in our knowledge of understanding the types of information exchange that will optimize patient care during transitions and (2) provide evidence-based solutions to enable the exchange.

Ying Ding
Agency
National Institutes of Health (NIH)
Grant Dates
Aug 1, 2023 - Apr 30, 2028
Funding

The collaborative award is $712,024 over the project period. The School of Information portion of the award is $333,944.

Award Number

1R01LM014306-01

NSF-CSIRO: RESILIENCE: Graph Representation Learning for Fair Teaming in Crisis Response

The recent COVID-19 pandemic has revealed the fragility of humankind. In our highly connected world, infectious disease can swiftly transform into worldwide epidemics. A plague can rewrite history and science can limit the damage. The significance of teamwork in science has been extensively studied in the science of science literature using transdisciplinary studies to analyze the mechanisms underlying broad scientific activities. How can scientific communities rapidly form teams to best respond to pandemic crises? Artificial intelligence (AI) models have been proposed to recommend scientific collaboration, especially for those with complementary knowledge or skills. But issues related to fairness in teaming, especially how to balance group fairness and individual fairness remain challenging. Thus, developing fair AI models for recommending teams is critical for an equal and inclusive working environment. Such a need could be pivotal in the next pandemic crisis. 

This project will develop a decision support system to strengthen the US-Australia public health response to infectious disease outbreak. The system will help to rapidly form global scientific teams with fair teaming solutions for infectious disease control, diagnosis, and treatment. The project will include participation of underrepresented groups (Indigenous Australians and Hispanic Americans) and will provide fair teaming solutions in broad working and recruiting scenarios. 

This project aims to understand how scientific communities have responded to historical pandemic crises and how to best respond in the future to provide fair teaming solutions for new infectious disease crises. The project will develop a set of graph representation learning methods for fair teaming recommendation in crisis response through: 1) biomedical knowledge graph construction and learning, with novel models for emerging bio-entity extraction, relationship discovery, and fair graph representation learning for sensitive demographical attributes; 2) the recognition of fairness and the determinant of team success, with a subgraph contrastive learning-based prediction model for identifying core team units and considering trade-offs between fairness and team performance; and 3) learning to recommend fairly, with a measurement of graph-based maximum mean discrepancy, a meta learning method for fair graph representation learning, and a reinforcement learning-based search method for fair teaming recommendation. The project will support cross-disciplinary curriculum development by effectively bridging gaps in responsible AI and team science, fair project management, and risk management in science.

Ying Ding
Agency
National Science Foundation (NSF)
Grant Dates
Apr 1, 2023 - Mar 31, 2026
Funding

The award is $299,862 over the project period.

Award Number

2303038

Use AI ML to Address the Crisis of Black Youth Suicide

A research team led by Professor Ying Ding was awarded a $1 million dollar research grant from the National Institutes of Health (NIH) Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Program to develop novel interventions targeting risk and protective factors among Black youth with the goal of reducing the suicide rate. The team's objective is twofold: to develop and validate new AI approaches to identify individual-level social risks of Black youth as well as develop approaches that enhance trust within underserved communities regarding the use of artificial intelligence/machine learning (AI/ML).

Professor Ding is joined by an interdisciplinary team of experts, including Professor Craig Watkins from the Moody College of Communication and Professor Yan Leng from McCombs School of Business at The University of Texas at Austin, and Professors Yifan Peng, Yunyu Xiao, and Jyoti Pathak from Cornell Medicine. Additionally, the research team will collaborate with two Historically Black Colleges and Universities (HBCUs), Prairie View A&M and Tuskegee University. This partnership will allow researchers to work with health professionals from historically underrepresented groups to investigate the culturally specific barriers that impact trust and hinder deploying machine learning techniques to address the behavioral health crises among Black youth.

Ying Ding
Agency
AIM-AHEAD and National Institutes of Health (NIH)
Grant Dates
Sep 17, 2023 - Sep 16, 2025
Funding

The collaborative award is $998,739 over the project period. The School of Information portion of the award is $698,739.

Award Number

1OT2OD032581-02-259

Classifying Text with Intuitive and Faithful Model Explanations

The objective of this Research Project is to develop an advanced neural NLP modeling framework for interpretable and accurate text classification. Intuitively, when human users better understand model predictions (via model interpretability), the users can better use model predictions to augment their own human reasoning and decision-making. More generally, effective model explanations offer a variety of other potential benefits, such as promoting trust, adoption, auditing, and documentation of model decisions. Our modeling framework, ProtoType-based Explanations for Natural Language (ProtoTexNL), seeks to provide faithful explanations for model predictions in relation to training examples and features of the input text. 
Matthew Lease
Agency
Cisco Systems Inc.
Grant Dates
Jun 1, 2022 - Aug 31, 2025
Funding

The award is $199,458 over the project period. 

SCC-IRG Track 1: Community-Driven Design of Fair, Urban Air Mobility Transportation Management Systems

Urban Air Mobility (UAM) envisions integrating the skyscape into the transportation network and encompasses services such as delivery drones, on-demand shared mobility by Vertical-Take Off and Landing (VTOL) aircraft for intra-city passenger trips, and, in the longer run, electric and autonomous VTOLs. This possible modal alternative provides a safe, reliable, and environmentally sound option to reduce surface-level congestion. Nevertheless, the history of transportation infrastructure development shows that it is imperative to design transportation infrastructures with the community to find the best balance between these sociotechnical requirements. Much research shows that the design of transportation systems has a long-lasting, often discriminatory effect that reinforces existing socio-economic inequality. As UAM is being developed as a new transportation mode, we are at an opportune moment to design its infrastructure to provide effective and equitable air mobility for all, avoiding our past mistakes. 

This project will focus on understanding the preferences, attitudes, and concerns of all stakeholders of UAM, including the potential users of UAM, the general public in different communities who may be positively and/or adversely affected by UAM, policymakers, and city planners. The knowledge elicited from the stakeholders will guide the design of an open-source Computer Aided Planning tool that policy-makers and urban planners can use to design UAM infrastructure that accommodates communities? priorities and enables transportation equity. While the timeline for UAM may be in the future, its deployment may entail significant future investment in infrastructure which makes inclusion of equity considerations and early community engagement critical. We propose a ''Community-in-the-Loop Integrative Framework for Fair and Equitable Urban Air Mobility (UAM) Infrastructure Design''. Our integrative framework will develop methods to engage with key stakeholders to address significant socio-technical challenges, including (a) understanding the community preferences and desiderata in terms of necessary considerations for equitable mobility, (b) developing novel machine learning techniques to generate design options that optimize for community desiderata efficiently and (c) devising community-driven evaluative measures and trade-off decision mechanisms. We address these challenges by drawing from urban and transportation engineering, aerospace, and computer and information sciences. 

The final product of our framework is an open-source Computer Aided Planning tool called VertiCAP. VertiCAP will be equipped with novel machine learning-based algorithms to navigate complex design space options, including long-term decisions (i.e., allocation of UAM airports, also known as vertiports), medium-term decisions (i.e., design of air space), and short-term decisions (i.e., air-traffic control). We will establish a ''community council'' representing different stakeholders. Through continuous interactions with the community council, we will evaluate and demonstrate the effectiveness of the developed VertiCAP tool in the City of Austin, TX and Southern California.

Min Kyung Lee
Agency
National Science Foundation (NSF)
Grant Dates
Jun 1, 2023 - May 31, 2027
Funding

The collaborative award is $2,000,000 over the project period. The School of Information portion of the award is $1,054,998. 

Award Number

NSF Award # 2313104

ALGOSH: Algorithmic management at work - challenges, opportunities, and strategies for occupational safety and health and wellbeing

Algorithms are at the forefront of a transformative shift in the World of Work, profoundly influencing work dynamics, organizational structures, and the work environment. Despite their profound impact, a substantial knowledge gap exists concerning algorithmic management (AM) and its repercussions on occupational safety, health, and wellbeing. This gap is particularly pronounced in non-platform work settings, where AM's prevalence is growing. As the use of AM continues to expand across various economic sectors, it is imperative to investigate its effects on the wellbeing of workers. The overarching objective of the ALGOSH research program is to enhance our understanding of AM in non-platform sectors and its impact on the health, safety, and wellbeing of workers. Moreover, it aims to develop tools and strategies to mitigate associated risks. 

The three research aims of ALGOSH are: Facilitating the development of a standard for measurement of algorithmic management at work and related risks for health, safety and well-being. Increasing knowledge about the effects of algorithmic management on workers’ health, safety, and well-being. Investigating the balance of interests related to the control of algorithms in different legal contexts regarding occupational health and safety (OSH). To accomplish this mission, an international and interdisciplinary consortium of researchers has been assembled. For our research to have maximum societal impact, the program also has a strong stakeholder involvement and support from trade unions, business organizations, international bodies, and government agencies. Their collective efforts will examine, discuss, and assess the opportunities and challenges posed by algorithmic management, fostering a safer and healthier work environment for all. The program applies multiple methods including quantitative, qualitative, literature reviews and participatory research. 

Min Kyung Lee
Agency
Karolinska Institutet
Grant Dates
Jul 1, 2023 - Dec 31, 2025
Funding

The School of Information allocation from this collaborative award is $28,381.

Collaborative Research: DASS: Designing accountable software systems for worker-centered algorithmic management

Software systems have become an integral part of public and private sector management, assisting and automating critical human decisions such as selecting people and allocating resources. Emerging evidence suggests that software systems for algorithmic management can significantly undermine workforce well-being and may be poorly suited to fostering accountability to existing labor law. For example, warehouse workers are under serious physical and psychological stress due to task assignment and tracking without appropriate break times. On-demand ride drivers feel that automated evaluation is unfair and distrust the system's opaque payment calculations which has led to worker lawsuits for wage underpayment. Shift workers suffer from unpredictable schedules that destabilize work-life balance and disrupt their ability to plan ahead. Meanwhile, there is not yet an established mechanism to regulate such software systems. For example, there is no expert consensus on how to apply concepts of fairness in software systems. Existing work laws have not kept pace with emerging forms of work, such as algorithmic management and digital labor platforms that introduce new risk to workers, including work-schedule volatility and employer surveillance of workers both on and off the job. 

To tackle these challenges, we aim to develop technical approaches that can (1) make software accountable to existing law, and (2) address the gaps in existing law by measuring the negative impacts of certain software use and behavior, so as to help stakeholders better mitigate those effects. In other words, we aim to make software accountable to law and policy, and leverage it to make software users (individuals and firms) accountable to the affected population and the public. This project is developing novel methods to enable standards and disclosure-based regulation in and through software systems drawing from formal methods, human-computer interaction, sociology, public policy, and law throughout the software development cycle. The work will focus on algorithmic work scheduling, which impacts shift workers who make up 25% of workers in the United States. It will take a participatory approach involving stakeholders, public policy and legal experts, governments, commercial software companies, as well as software users in firms and those affected by the software's use, in the software design and evaluation. 

The research will take place in three thrusts in the context of algorithmic scheduling: (1) participatory formalization of regulatory software requirements, (2) scalable and interactive formal methods and automated reasoning for software guarantees and decision support, and (3) regulatory outcome evaluation and monitoring. By developing accountable scheduling software, the project has the potential for significant broader impacts by giving businesses the tools they need for compliance with and accountability to existing work scheduling regulations, as well as the capacity to provide more schedule stability and predictability in their business operations.

Min Kyung Lee
Agency
National Science Foundation (NSF)
Grant Dates
Sep 1, 2022 - Aug 31, 2025
Funding

The award is $249,999 over the project period.

Award Number

NSF Award # 2217721

Bridge2AI: Cell Maps for AI (CM4AI) Data Generation Project

As part of the NIH Common Fund’s Bridge2AI program, the CM4AI data generation project seeks to map the spatiotemporal architecture of human cells and use these maps toward the grand challenge of interpretable genotype-phenotype learning. In genomics and precision medicine, machine learning models are often "black boxes," predicting phenotypes from genotypes without understanding the mechanisms by which such translation occurs. 

To address this deficiency, project will launch a coordinated effort involving three complementary mapping approaches – proteomic mass spectrometry, cellular imaging, and genetic perturbation via CRISPR/Cas9 – creating a library of large-scale maps of cellular structure/function across demographic and disease contexts. These data will broadly stimulate research and development in "visible" machine learning systems informed by multi-scale cell and tissue architecture. In addition to data and tools, this project will implement a standards data management approach based on FAIR access and software principles, with deep provenance and replication packages for representation of cell maps and their underlying datasets; initiate a research program in ethical AI, especially as it relates to how maps will be used in genomic medicine and model interpretation; and stimulate a diverse portfolio of training opportunities in the emerging field of biomachine learning.

Ying Ding
Agency
National Institutes of Health (NIH)
Grant Dates
Sep 1, 2022 - Aug 31, 2026
Funding

The collaborative award is $4,894,457 over the project period. The School of Information portion of the award is $333,944.

Award Number

1OT2OD032742-01