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.
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.
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.
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.