Research Awards/Grants (Current)

Ying Ding

National Science Foundation (NSF)

04/01/2023 to 03/31/2026

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

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

Yan Leng, and Samuel Craig Watkins, University of Texas at Austin;
Yifan Peng Weill Cornell Medicine

AIM-AHEAD and National Institutes of Health (NIH)

09/17/2023 to 09/16/2025

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

Closing the loop with an automatic referral population and summarization system

Suicide is a public health concern and is ranked as the second leading cause of death in 10-24 years old.1,2 In particular, the increasing rates of suicide mortality and suicidal ideations and behaviors among Black youth in the United States (US) have become a pressing concern in recent years.3 Between 2001 and 2015, Black children under 13 years old were twice as likely to die by suicide, compared to their White counterparts.4 Furthermore, suicide mortality rates among Black youth have risen more rapidly than in any other racial or ethnic group.2,5 However, there remains a significant knowledge gap in understanding culturally tailored suicide prevention strategies for this population, particularly regarding unique social risk factors specific to Black youth. Specifically, a detailed understanding of social risk factors unique to Black youth and their differentiation from risk factors for other racial and ethnic groups is limited.2 This knowledge gap is critical, as research has indicated that Black youth face greater exposure to adverse childhood experiences (ACEs), which are linked to higher risks of suicidal ideation and attempts.

The National Violent Death Reporting System (NVDRS) is a state-based violent death reporting system in the U.S. that helps provide information and context on when, where, and how violent deaths occur and who is affected.11 However, much of the information in NVDRS is unstructured, limiting its use in examining a complete picture of the social risk factors contributing to Black youth suicide. Therefore, it is imperative to develop machine learning (e.g., natural language processing [NLP]) algorithms to automatically extract social risk factors from free text to help analyze Black youth suicide. Our long-term goal is to reduce the suicide rate by developing novel interventions targeting risk and protective factors among Black youth. The overall objective of this application is to develop and validate new AI approaches to identify individual-level social risks of Black youth suicide and enhance trust within the underserved communities regarding the approaches of AI/ML.

Angela D.R. Smith

National Science Foundation

06/15/2023 to 05/31/2028

The award is $1,368,414 over the project period.

Collaborative Research: Racial Equity: Engaging MarginalizedGroups to Improve Technological Equity


This collaborative project investigates the lack of diverse, representative datasets and insights in the development and use of technology. It explore the effects of disparities on the ability of technologists (e.g., practitioners, designers, software developers) to develop technology that addresses and mitigates systemic societal racism and historically marginalized individuals' ability to feel seen and heard in the technology with which they engage. The implications of this project are threefold: 1) it supports building relationships between technologists and technology users by understanding the values that most impact historically marginalized communities' engagement and data contributions; 2) given access to more diverse data and insights, the project provides technologists with interventions that empower them to make use of these data and insights in practice; 3) lastly, the work provides support and affirmation for the technologists who are already making these explicit considerations in their work without the adequate support. More broadly, insights from this project can be applied in practice to promote racial equity and ensure systemic racism is an explicit consideration in STEM education and workforce development by incorporating more equitable practices in technologists' workflow.

This study seeks to answer three main research questions: 1) What are the barriers to engaging and amplifying marginalized voices in technological spaces and data sets for both technologists and users? 2) How can marginalized groups be engage when designing and developing data-centric systems without sacrificing their safety, security, and trust? 3) What does it look like to provide interventions for engaging the margins to technologists without compromising the safe spaces for marginalized groups? Using a multi-modal approach, the project will examine how researchers and technologists can best learn to engage in data-centric research with marginalized communities in an ethically and socially responsible manner that centers the rights and values of the communities of interest. Culturally relevant approaches and grounding philosophies will drive the research methods and analyses. Through surveys, semi-structured interviews, design workshops utilizing a combination of participatory design and community-based approaches, as well as case study analysis to collect qualitative and quantitative data, the research team will develop an intervention that supports technologists in responsible engagement. Aside from real-world implementation, this project will share its findings through academic and community-facing venues, such as journal publications, conference presentations, op-eds, blogs, workshops, and social media.

This collaborative project is funded through the Racial Equity in STEM Education program (EDU Racial Equity). The program supports research and practice projects that investigate how considerations of racial equity factor into the improvement of science, technology, engineering, and mathematics (STEM) education and workforce. Awarded projects seek to center the voices, knowledge, and experiences of the individuals, communities, and institutions most impacted by systemic inequities within the STEM enterprise. This program aligns with NSF's core value of supporting outstanding researchers and innovative thinkers from across the Nation's diversity of demographic groups, regions, and types of organizations. Programs across EDU contribute funds to the Racial Equity program in recognition of the alignment of its projects with the collective research and development thrusts of the four divisions of the directorate.

Min Kyung Lee

Chandra Bhat (University of Texas at Austin) and Yasser Shoukry (University of California-Irvine)

National Science Foundation (NSF)

06/01/2023 to 05/31/2027

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

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.

Ying Ding

led by Yifan Peng Weill Cornell Medicine

National Institutes of Health (NIH)

08/01/2023 to 04/30/2028

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

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.