Research Awards/Grants (Current)

Ying Ding

National Science Foundation (NSF)

09/01/2023 to 08/31/2024

The award is $50,000 over the project period.

NSF I-Corps Project Title: CARE: Contextualization of Explainable AI for Better Health

The broader impact/commercial potential of this I-Corps project is the development of the explainable Artificial Intelligence (XAI) methods for healthcare data. Currently, the number of electronic medical records is increasing while machine learning and deep learning models, especially large language models, have been employed to address healthcare needs. However, the healthcare domain is highly regulated and explainability for the black-box AI model becomes increasingly critical for any AI application. Users need to comprehend and trust the results and output created by machine learning algorithms. The proposed XAI technology may be used to describe an AI model, its expected impact, and potential biases. Further, the proposed technology may be used to transfer AI predictions into explainable medical interventions to enable the last mile delivery of AI in healthcare The commercial potential of these technologies may impact three major groups: health insurance companies who may provide better care management interventions and achieve personalized care delivery based on XAI; health analytic companies who rely on explanation to further enhance their products and meet the government regulations; and medical device startups who demand explainable analytical outputs based on the collected data from medical devices to enrich their user experience.
This I-Corps project is based on the development of explainable Artificial Intelligence (XAI) methods applied to the healthcare industry. Providing explainability is critical for AI health applications. Healthcare is a unique domain with multimodality data: tableau data about patient demographic information, textual data about medical notes, time series data about vital sign measures, images about medical scan, and wavelet data about EEG and ECG. To provide a holistic view of these data, deep learning is used to create universal embeddings on different modalities of data and build the prediction models for health risks. But deep learning methods lack transparency and demand explainability. The proposed technology combines integrated gradients with ablation studies to identify the contributing factors of different data components in the explanation. In addition, the proposed platform adds knowledge graphs into the prediction and explanation workflow to detect the relationships between contributing features to generate an explanation with a holistic view, and translates weights or feature importance into risk scores to enable the last mile delivery of AI in healthcare. The proposed XAI method may be used to explain the importance of input data components, identify the contributing features at the individual patient level and the patient cohort level; scale and save computational resources; and self-improve by using reinforcement learning to enhance positive feedback.

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.

Kayla Booth

The Andrew W. Mellon Foundation

11/01/2021 to 10/31/2024

The award is $700,772 over the project period. 

Summer Institutes for Advanced Study in the Information Sciences

The iSchool Inclusion Institute (i3) is an undergraduate research and leadership development program that prepares students from underrepresented populations for graduate study and careers in the information sciences. Only 25 students from across the country are selected each year to become i3 Scholars. Those students undertake a yearlong experience that includes two summer institutes hosted by the University of Texas at Austin’s iSchool and a research project spanning the year. i3 prepares students for the rigors of graduate study and serves as a pipeline for i3 Scholars into internationally recognized information schools—the iSchools. Most importantly, i3 empowers students to create change and make an impact on the people around them.

Min Kyung Lee

Haiyi Zhu (Carnegie-Mellon University)

National Science Foundation (NSF)

10/01/2020 to 09/30/2024

The collaborative award is $2,013,764 over the project period. The School of Information portion of the award is $266,000. 

SCC-IRG Track 1: Empowering and Enhancing Workers Through Building A Community-Centered Gig Economy

The gig economy is characterized by short-term contract work performed by independent workers who are paid in return for the "gigs" they perform. Example gig platforms include Uber, Lyft, Postmates, Instacart, UpWork, and TaskRabbit. Gig economy platforms bring about more job opportunities, lower barriers to entry, and improve worker flexibility. However, growing evidence suggests that worker wellbeing and systematic biases on the gig economy platforms have become significant societal problems. For example most gig workers lack financial stability, have low earning efficiency and lack autonomy, and many have health issues due to long work hours and limited flexibility. Additionally, gig economy platforms perpetuate biases against already vulnerable populations in society. To address these problems, this project aims to build a community-centered, meta-platform to provide decision support and data sharing for gig workers and policymakers, in order to develop a more vibrant, healthy, and equitable gig economy.

The project involves three major research activities. (1) Working with gig workers and local policymakers to understand their concerns, challenges, and considerations related to gig worker wellbeing, as well as the current practices, problems, and biases of existing gig economy platforms. (2) Developing a data-driven and human-centered decision-assistance environment to help gig workers make "smart" decisions in navigating and selecting gigs,and provide a macrolevel perspective for policymakers working to balance their diverse set of objectives and constraints. (3) Deploying and evaluating whether and how the above environment addresses the fundamental problems of worker wellbeing and systematic biases in the gig economy.

James Howison

Jennifer Schopf, Angela Newell, and Michael Shensky


Alfred P. Sloan Foundation

08/01/2023 to 07/31/2025

The award is $650,000 over the project period.

University of Texas Open Source Program Office

The University of Texas Open Source Program Office (UT-OSPO) is the center for open source activity, connection, training, and support to enable open source practices as a key part of the university mission. With financial support from the Alfred P. Sloan Foundation, this project is led by personnel from UT Austin’s central IT services, Libraries, iSchool, and TACC in order to form an umbrella organization that is more than the sum of its pieces. 

The UT-OSPO coordinates a shared open infrastructure for software development, establishing a central hub for open source support that enables the university to leverage and formalize the pre-existing infrastructure on campus, unify and expand the work already being done in this space, create additional opportunities for engagement among faculty and students, and foster interdisciplinary connections across departments and units. 

This infrastructure promotes more reproducible and open research through the development of an ecosystem of researchers engaging and growing open source skills and practice through a pathway of participation. We provide support through:

  • joint training
  • personalized consultations   
  • lecture series
  • a help desk network
  • publishing of best practices, and
  • events that help students, faculty, and staff engage with open source software.