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.
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.
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.
Matthew Lease
Jessy Li
Cisco Systems Inc.
06/01/2022 to 08/31/2025
The award is $199,458 over the project period.
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.
Ying Ding
led by Trey Ideker, University of California, San Diego
National Institutes of Health (NIH)
09/01/2022 to 08/31/2026
The collaborative award is $4,894,457 over the project period. The School of Information portion of the award is $333,944.
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.