Funded Working Groups

The Data, AI, and Computing Initiative has funded 19 faculty-led, interdisciplinary working groups that will identify promising directions for future research, recruitment, team science, and translational impact in data, AI, and computing. These groups span all major DAC research pillars—responsible, inclusive, safe, and empowering (RISE) AI; Foundational AI and Next-Generation Computing; AI-Enabled Science and Engineering; Data-Driven Computational Social Sciences and Digital Humanities; and Health Data & AI—and include representation from nearly every college and school across the University. Through structured collaboration, convenings, and strategic assessment, each working group will evaluate Notre Dame’s existing strengths, identify gaps and opportunities, and articulate recommendations to strengthen institutional leadership in key domains. Together, this portfolio underscores DAC’s commitment to building a coherent, faculty-driven foundation for purposeful and interdisciplinary advancement in data, AI, and computing.

Pragmatic Design and Engineering of Human-Centered RISE AI Technologies

Faculty Leads: Toby Li, assistant professor, computer science and engineering, College of Engineering; and Tim Weninger, associate professor, computer science and engineering
Members: Diego Gomez-Zara, from the Department of Computer Science and Engineering; Ann-Marie Conrado, from the Department of Art, Art History, and Design; Yang Yang from the Department of Information Technology, Analytics, and Operations; Mohammad Rifat from the Keough School of Global Affairs; Sugana Chawla from the Lucy Family Institute for Data & Society; and Edgar Boliva-Nieto from the Department of Aerospace and Mechanical Engineering

A persistent gap exists between the conceptual design of what responsible AI should look like and the technical engineering required to build systems that embody these principles and guidelines in practice. This working group explores paths to position Notre Dame as a global leader in bridging this divide, which currently hinders the translation of RISE (Responsible, Inclusive, Safe, and Empowering) AI technologies into tangible societal benefits. The group aims to foster a co-evolution between human-centered, community-engaged design inquiry and engineering methods to transform abstract frameworks into deployable AI technologies. Planned activities include developing a comprehensive landscape report on institutional efforts, programs, and infrastructure, conducting focus groups and surveys to identify specific opportunities and barriers, and hosting a workshop with a globally recognized leader in this area. Through these efforts, the group seeks to identify programmatic supports that reduce friction for interdisciplinary collaborations and make recommendations for establishing a shared roadmap for the University’s strategies in this domain.

AI/ML for Peace and Conflict Research

Faculty Leads: Josefina Echavarría Álvarez, professor of the practice in the Keough School of Global Affairs, and director of the Peace Accords Matrix; and Matthew Hauenstein, an assistant research professor at the Lucy Family Institute of Data & Society
Members: Drew Marcantonio, from the Kroc Institute for International Peace Studies; Emma Murphy, from the Keough-Naughton Institute for Irish Studies; Henry Potter from the Keough-Naughton Institute for Irish Studies; Cristian Flórez from the Kroc Institute; and Matthew Sisk from the Lucy Family Institute for Data & Society

The AI/ML for Peace and Conflict Research working group is an interdisciplinary collaboration to explore how computational methods and AI tools can revolutionize violent conflict and sustainable peace research. This group brings together researchers whose work applies AI to the study of conflict, peace processes, and post-settlement politics. Researchers will hold regular meetings to develop an actionable plan to establish a sustainable AI/ML for Peace Studies research hub at Notre Dame. The group will also host a visiting speaker to collaborate with our team and present their work to the broader campus community.

A Global Approach to RISE AI

Faculty Leads: Karla Badillo-Urquiola, Clare Booth Luce Assistant Professor of computer science and engineering in the College of Engineering; and Mohammad Rashidujjaman Rifat, Assistant Professor of Tech Ethics and Global Affairs in the Keough School of Global Affairs
Members: Yong Suk Lee, from the Keough School of Global Affairs; Diego Gomez-Zara, from the Department of Computer Science and Engineering; Maria Mercedes Salmon from Notre Dame Mexico; Jenny Padilla, from the Department of Psychology; Nydia Morales-Soto from the Eck Institute for Global Health; Ron Metoyer, vice president and associate provost for teaching and learning; and Alexi Orchard, from the College of Arts & Letters

This project advances RISE AI by expanding Notre Dame’s AI ethics leadership beyond the Global North and grounding it in real-world conditions across the Global South. The group will conduct an institutional landscape audit to map Notre Dame’s current responsible computing efforts and identify opportunities for stronger global integration. Researchers will convene an expert summit through Notre Dame’s Global Gateways and run a multi-site hybrid student hackathon to develop early ethical AI prototypes and policy concepts shaped by regional needs. Building on these activities, the group will write a Global RISE AI position paper outlining long-term visions of global partnership in advancing ethical issues of AI. Finally, researchers will sustain momentum through working group meetings and research seminars that form interdisciplinary teams and accelerate actionable collaborations.

A Sociotechnical Perspective on Auditing AI

Faculty Lead: Cam Kormylo, assistant professor of information technology, analytics, and operations at the Mendoza College of Business; and John Lalor, assistant professor of information technology, analytics, and operations
Members: Heng Xu, Ahmed Abbasi, and Yang Yang, from the Department of Information Technology, Analytics, and Operations; Walter Scheirer, and Nitesh Chawla, from the Department of Computer Science and Engineering; Daniel Slate, from the Law School; Ting Hua, from Lucy Family Institute for Data & Society; and Alison Cheng, from the Department of Psychology

Effective AI auditing requires both rigorous technical benchmarking and a sociotechnical perspective that examines how systems are developed, embedded in organizations, interpreted by people, and shaped by institutional practices. This working group will bring together faculty members from across the University to establish best practices for sociotechnical AI audits and explore opportunities for research and partnerships in this field. Monthly meetings will facilitate discussions to develop a comprehensive framework that incorporates both technical evaluations and human-centered analysis. The group aims to identify gaps in current auditing practices and generate actionable items to enhance interdisciplinary collaboration on campus. Additionally, an external expert will be invited to share insights on industry standards and shortcomings in AI audits, further informing the group's work to strengthen Notre Dame's leadership in AI governance.

Culture and Interculturality in AI for Understanding Our Past and Setting Course for Our Future

Faculty Leads: Elena Mangione, teaching professor of Spanish in the College of Arts & Letters; Liang Cai, Ruth and Paul Idzik Associate Professor in Digital Scholarship; Chengxu Yin, teaching professor of Chinese; and David Chiang, associate professor of computer science and engineering
Members: Hana Kang, from the Department of East Asian Languages; Meng Jiang, Diego Gomez-Zara, Mariana Fernandez Espinosa, Xiangliang Zhang, and Toby Li, from the Department of Computer Science and Engineering; Steve Varela from Notre Dame Learning; John Behrens, from the Department of Theology; Tom Stapleford, from the Department of History; Ebrahim Moosa, from the Keough School of Global Affairs; and Alexander Jech, from the Department of Philosophy

The working group on AI, the humanities, and critical interculturality will meet once a month from February through April. The group will explore themes of interculturality and AI approaches to culture while it provides space and opportunities for collaboration between AI professors and scholars in the humanities. The working group will host two speakers from computer science and philosophy of language, and it will facilitate a discussion of Fred Dervin's book AI for Critical Interculturality.

Supporting the Ecclesial Response to Evolving Impacts of AI on Ministry

Faculty Leads: John Behrens, professor of the practice of technology and digital studies and concurrent professor of the practice in the College of Engineering; and Brett Robinson, associate director for outreach and associate professor of the practice at the McGrath Institute for Church Life
Members: Rev. Nathan Willis, C.S.C., from the Alliance for Catholic Education; Jerry Powers, from the Kroc Institute for International Peace Studies; and Stacy Noem, from the Department of Theology

The SERVIAM Working Group is investigating how Notre Dame can partner with Catholic dioceses to support both their administrative needs and pastoral mission in the new world of artificial intelligence. Dioceses face distinctive AI stewardship challenges due to their decentralized structures, hybrid workforce of clergy, staff, and volunteers, and the need to understand and integrate secular technical competencies along with theological commitments to advance human flourishing. The project will conduct discovery research with U.S. dioceses, beginning with the Archdiocese of Chicago, to understand current AI awareness, risks, governance gaps, and educational needs. In parallel, the group will survey Notre Dame’s internal capacity to support a coordinated, interdisciplinary response. The primary outcome will be a synthesized report and collaboration roadmap evaluating the feasibility of future programs that equip diocesan leaders to engage AI in service of the Church’s mission.

Scientific AI Agents at Notre Dame

Faculty Leads: Daniele E. Schiavazzi, associate professor in the Department of Applied Computational Math & Statistics in the College of Science; and Yamil J. Colon-Rodriguez, associate professor of chemical and biomolecular engineering in the College of Engineering
Members: Brett Savoie, from the Department of Chemical and Biomolecular Engineering; Meng Jiang, Lynn Zhang, and Paul Brenner, from the Department of Computer Science and Engineering; Zecheng Zhang and Zhiliang Xu, from the Department of Applied and Computational Mathematics and Statistics

AI agents are poised to revolutionize the way we work, conduct research, accomplish tasks, and learn. This initiative specifically focuses on agents that can understand physics and extract knowledge from physics-based systems. By fostering discussions with internal and external stakeholders, hosting presentations from research leaders, anticipating changes brought by next-generation teaching and learning, and proactively pursuing federal funding, this initiative will better position Notre Dame to lead this revolution.

Becoming a National Leader in Data Chemistry

Faculty leads: Olaf Wiest, Grace-Rupley Professor of Chemistry & Biochemistry in the College of Science; and Xiangliang Zhang, Leonard C. Bettex Collegiate Professor of Computer Science in the College of Engineering
Members: Brett Savoie, from the Department of Chemical and Biomolecular Engineering; Meng Jiang, from the Department of Computer Science and Engineering; and Dan Gezelter and Brittany Morgan, from the Department of Chemistry & Biochemistry

The use of AI in science will fundamentally reshape how research is done in the next few decades, while at the same time inspiring new research in AI. While data chemistry has become a highly competitive field, the head start Notre Dame had and the resources allocated to its strategic initiatives opens a short window of opportunity to position it among the top three U.S. universities in data chemistry. The goal of this working group is to formulate a strategic plan to firmly position Notre Dame among the top three universities in the United States for data chemistry. This plan will prioritize the most promising focus areas in data chemistry, identify infrastructure gaps, establish an interdisciplinary community of scholars across colleges, and aim to maximize return on investment in these areas by leveraging them with federal, foundation, and industrial funding. This will be accomplished through series of faculty lunches and two half-day workshops to solicit outside expertise and input from the campus community.

Self-Driving Labs for Autonomous Materials Experimentation and Discovery

Faculty Leads: Tengfei Luo, Dorini Family Professor for Energy Studies; and Rev. Bryan Paulsen, S.J., assistant professor of chemical and biomolecular engineering in the College of Engineering
Members: Yanliang Zhang, from the Department of Aerospace and Mechanical Engineering; Jennifer Schaefer and Alexander Dowling from the Department of Chemical and Biomolecular Engineering; Lynn Zhang and Meng Jiang, from the Department of Computer Science and Engineering; Brandon Ashfeld, from the Department of Chemistry & Biochemistry; and Mengxue Hou, from the Department of Electrical Engineering  

This working group aims to advance the development and coordination of AI-enabled, self-driving laboratories to accelerate materials science research across the University. By bringing together faculty and staff with expertise in materials, robotics, automation, and data-driven methods, the group will assess existing experimental capabilities, identify gaps, and explore opportunities to better leverage and integrate current resources. Planned activities include biweekly meetings, campus-wide surveys, and invited seminars with internal and external experts to map expertise, infrastructure, and training needs. The group will develop strategic recommendations for future investments in personnel, instrumentation, and data standards to strengthen Notre Dame’s leadership in autonomous materials experimentation. Ultimately, this effort seeks to translate advances in AI into impactful, real-world experimental research addressing challenges in energy, environment, security, and human health.

Data, Sensing, and AI-Enabled Urban Sustainability

Faculty Leads: Chaoli Wang, professor of computer science and engineering in the College of Engineering; and Matthew Sisk, co-director of the Civic-Geospatial Analysis and Learning Lab and associate professor of the Practice in the Lucy Family Institute for Data & Society
Members: Ming Hu, from the School of Architecture; Tracy Kijewski-Correa and Wade McGillis, from the Department of Civil and Environmental Engineering and Earth Sciences; Aaron Striegel, from the Department of Computer Science and Engineering; Toros Arikan, from the Department of Electrical Engineering; and Danielle Wood, from the Environmental Change Initiative  

This working group leverages machine learning and sensor data to tackle urban environmental and resilience challenges. Using Google Street View images and environmental sensors, the group aims to build a scalable AI framework to predict energy burdens and guide sustainable infrastructure design across the United States. This interdisciplinary effort unites architecture, engineering, and data science experts to explore sensor design, privacy, and disaster-resilient planning. Activities include monthly brainstorming sessions, external speaker seminars, and broader internal meetings to foster a sustainability-focused learning ecosystem. The group emphasizes open-source dissemination of software and research through NSF-recognized repositories to impact both academia and policy. Ultimately, the initiative seeks to advance Notre Dame's leadership in sustainability while promoting environmental justice and stewardship.

Physical AI

Faculty Leads: Meng Jiang, Frank M. Freimann Collegiate Professor of Computer Science and Engineering; and James Schmiedeler, professor of aerospace and mechanical engineering in the College of Engineering
Members: Jane Cleland-Huang and Fanxin Kong, from the Department of Computer Science and Engineering; Mengxue Hou and Hai Lin, from the Department of Electrical Engineering; Margaret McGuinness and Patrick Wensing, from the Department of Aerospace and Mechanical Engineering

The Physical AI Working Group brings together faculty and researchers across Notre Dame to explore how artificial intelligence can be integrated with robotics, cyber-physical systems, and embodied machines to responsibly transform the physical world. Building on Notre Dame’s strengths in foundation models, simulation, robotics, and ethics, the group aims to define a distinctive, human-centered vision for Physical AI that advances AI-enabled problem solving in the physical world while addressing safety, trust, and societal impact. The group will convene monthly meetings and host two seminars featuring leaders from industry and emerging startups. These activities will map existing campus expertise, surface opportunities for collaboration, and engage faculty and students from across disciplines. The effort will culminate in a white paper and strategic recommendations on research priorities, infrastructure needs, and faculty hiring to guide future institutional investment in Physical AI.

Foundational AI Working Group

Faculty Leads: Fang Liu, Notre Dame Collegiate Professor and Associate Chair in the Department of Applied and Computational Mathematics and Statistics
Members: Marinho Bertanha, from the Department of Economics; Erin Chambers and Meng Jiang, from the Department of Computer Science and Engineering; Yuefeng Han, Soham Jana, Xiufan Yu, and Changbo Zhu, from the Department of Applied and Computational Mathematics and Statistics, and Zifeng Zhao, from the Department of Information Technology, Analytics, and Operations  

Modern AI systems are advancing at a remarkable pace, but largely driven by empirical evidence rather than principled understanding. Theoretical and mathematical foundations lag behind, leaving questions central to the Data, AI, and Computing Initiative’s mission unanswered. Notre Dame currently lacks sufficient strength in foundational machine learning (ML), mathematical and probabilistic research in AI, creating a critical gap and missed opportunity in a field that will have fundamental and long-term implications in AI research and applications. This working group brings together faculty from core DAC disciplines—including AI, mathematical and theoretical statistics, high-dimensional inference, statistical learning, probabilistic and trustworthy ML, causal inference, theoretical computer science, and optimization—to provide the breadth and depth needed for high-impact university-wide recommendations on how to fill the gap. The group will engage in sustained and in-depth discussion with both internal and external experts through regular group meetings, colloquium talks and on-campus mini-symposium, and produce actionable recommendations to strengthen Notre Dame’s research in rigorous and theory-driven AI, positioning the university to be an active contributor to and help shape the directions of AI foundation research and attract top talent in this rapidly evolving area.

ACMS Strategic Bridges: Data, Modeling, and Computation

Faculty Lead: Jonathan Hauenstein, Robert and Sara Lumpkins Collegiate Professor in Applied and Computational Math and Statistics in the College of Science
Members: Bei Hu, Fang Liu, Robert Rosenbaum, Adam Volk, Roger Woodard, and Victoria Woodard, from the Department of Applied and Computational Mathematics and Statistics

Data, modeling, and computation are essential pillars of the Data, AI, and Computing Initiative and other University strategic priorities, as well as forming the foundation of the Department of Applied and Computational Mathematics and Statistics (ACMS). This working group has a vision of transcending disciplinary boundaries by establishing many functioning bridges throughout campus for collaboration on important research problems centered around data, modeling, and computation. In particular, this working group aims to fortify and build interdisciplinary bridges to amplify collective impact and identify areas where Notre Dame can differentiate itself in data, modeling, and computation. Activities for this working group include listening sessions with University stakeholders and discussions with several external leaders visiting campus to determine future actions for advancing a strategic vision around data, modeling, and computation on campus.

Socio-Technical Infrastructure for Sustaining Next-Generation Digital Projects in the Humanities

Faculty Leads: Dan Johnson, subject librarian and specialist at Hesburgh Libraries and interim co-department head of the Navari Family Center for Digital Scholarship; and Julie Vecchio, interim co-director of the Navari Family Center for Digital Scholarship
Members: Summer Mengarellia from the Hesburgh Libraries; Don Brower, from the Center for Research Computing; Matthew Kilbane, from the Department of English; Matthew Payne, from the Department of Film, Television, and Theatre; and Rebecca Bowen, from the Department of Romance Languages and Literatures

Questions about funding, training, platforms, and collaborators present steep challenges to researchers engaged in creating and sustaining digital projects. Multiple kinds of expertise are needed for next-generation digital research, and challenges to conducting it must be addressed both locally and collectively across institutional boundaries. This working group will investigate emerging trends in these challenges to the broad field(s) of digital humanities, soliciting local perspectives as well as wider experiences across academia through a combination of monthly discussions, invited presentations, and regional campus interviews. Insights will be synthesized in a final report that addresses socio-technical needs for facilitating digital project development, sustaining digital outputs, and strengthening partnerships with current and future colleagues.

The Notre Dame Econ-CS Working Group

Faculty Leads: Maciej Kotowski, Associate Professor of Economics in the College of Arts & Letters; and Taeho Jung, associate professor of computer science and engineering in the College of Engineering
Members: Andrew Ferdowsian and Yijun Liu, from the Department of Economics; Jarek Nabrzyski, from the Center for Research Computing; and Meng Jiang, from the Department of Computer Science and Engineering

The Notre Dame Economics and Computer Science (Econ-CS) Working Group connects the Economics and Computer Science departments to promote collaboration in areas such as algorithmic game theory, market design, computational incentives, AI alignment, blockchain protocols, and privacy-preserving mechanism design. The group supports both departments by helping faculty stay current with theoretical advances, expanding access to interdisciplinary grant opportunities, exploring joint course offerings, and strengthening research on incentives, platforms, cryptography, and security. The working group will also organize an interdisciplinary conference in the spring, open to the Notre Dame community, featuring leading research at the intersection of economics and computer science.

Digital Experimentation for Inference and Causal Modeling: Methodological Foundations and Research Applications

Faculty Lead: Ken Kelley, Edward F. Sorin Society Professor of Information Technology, Analytics, and Operations in the Mendoza College of Business
Members: Ahmed Abbasi, Francis Darku, Jeff Cai, and Zifeng Zhao, from the Department of Information Technology, Analytics and Operations; Keyan Li, from the Department of Marketing; Xiufan Yu, from the Department of Applied and Computational Mathematics and Statistics; and Oscar Gonzalez, from the Department of Psychology

As organizations increasingly deploy AI and algorithmic systems for decision-making, a critical methodological gap has emerged: the foundational principles for valid inference from digital experiments and observational data have not kept pace with technological advancement. Further, technological advancement has not always centered on valid inference and causal modeling. Digital platforms can collect unprecedented volumes of behavioral data through randomized experiments, A/B tests, and quasi-experiments. However, industry needs of prediction-focused approaches – such as multi-armed bandits optimized for immediate outcomes – has inadvertently undermined some aspects of scientific rigor necessary for building cumulative, generalizable knowledge. This working group addresses fundamental threats to internal and external validity from digital experimentation platforms that compromise inference quality across science and policy. Unlike purely algorithmic optimization methods for organizational outcomes (e.g., sales, engagement, click-through rates), our focus is on methodological frameworks that prioritize the measurement of constructs, causal estimation, accurate parameter estimation, and theory advancement through a model-comparison perspective.

Quantitative Social Sciences

Faculty Leads: Ken Scheve, the I.A. O'Shaugnessy Dean of the College of Arts & Letters and Professor of Political Science; Jeff Harden, the Andrew J. McKenna Family College Professor in the Department of Political Science, Concurrent Professor in the Department of Applied and Computational Mathematics and Statistics; and Bill Evans, Keough-Hesburgh Professor in the Department of Economics
Members: Social Science faculty across several departments and schools

The Quantitative Social Sciences Working Group seeks to identify the research infrastructure that the University of Notre Dame needs to support data-intensive social science research in the coming decade. We seek to reduce the costs of conducting data-intensive social science research in every phase of the research cycle. Social science research is rapidly evolving, with growing reliance on large-scale data, increased use of specialized quantitative methods, and substantial growth in the scale of productive research groups. The working group will convene faculty across disciplines and colleges/schools to identify unmet needs and opportunities in supporting quantitative social science research. The group will also engage with leaders of peer social science institutes to learn more fully the scope of services offered and best practices for establishing and managing such institutes. The working group will propose a core facility that will enhance faculty productivity, attract and retain top scholars, and expand methodological training and research opportunities for undergraduate and graduate students, thereby supporting the University’s goal of building the leading global Catholic research university.

Advancing Human Neuroimaging Through Innovations in AI and Ethical Computing

Faculty Lead: Aron Barbey, Andrew J. McKenna Family Professor in the Department of Psychology and director of the Notre Dame Human Neuroimaging Center
Members: Nathan Muncy, and Johnny Zhang, from the Department of Psychology; Robert Rosenbaum, from the Department of Applied and Computational Mathematics and Statistics; and Sara Berger, from the Notre Dame-IBM Tech Ethics Lab

This working group’s efforts will focus on three primary aims: (1) Team and Community Building: To establish research collaboration among an interdisciplinary team of researchers in computational neuroscience, statistics and machine learning, and ethics and public policy, with the goal of establishing Notre Dame as a leader in the development and application of AI-enabled next-generation brain mapping technology. (2) Research and Development: To develop special AI tools and brain atlases urgently needed for AI-enabled brain mapping and to demonstrate the feasibility and potential of AI-enabled brain mapping technology. (3) Ethical and Policy Implications: To elucidate the primary ethical concerns that result from modern neuroimaging methods, to investigate their role in currently available open access datasets, and to propose guidelines and practices for policy and standards development.

Data Collection, Storage, and Modeling for Research on Health and AI

Faculty Lead: Corey Angst, Jack and Joan McGraw Family Professor of Information Technology, Analytics, and Operations and chair of the Department of Information Technology, Analytics, and Operations
Members: John Lalor, from the Department of Information Technology, Analytics, and Operations; Margaret McGuinness, from the Department of Aerospace and Mechanical Engineering; Cheng Liu, from the Department of Psychology, and Angelica Martinez, from the Lucy Family Institute for Data & Society

Notre Dame conducts research related to healthcare across various centers, labs, and institutes on campus, as well as work done by individual scholars or teams. With new university-wide partnerships, such as the one with Beacon Health System, there is a need for the University to understand its current data processing capabilities regarding sensitive healthcare data and prepare for the associated costs and considerations of large-scale research computing on healthcare data. This working group, which is closely aligned with the Lucy Family Institute for Data & Society's Health Data Exploration and Analytics Lab (HEAL) and will include Assistant Research Professor Angelica García-Martínez of HEAL as a participant, will analyze the state of health-related research on campus. The group seeks to better understand how healthcare-related data is stored and used for research, with an aim towards recommending steps that DAC can take to facilitate efficient and effective ongoing health-related research and research computing across campus.