PIT@UMass Faculty Fellowship 2026-2027
The Public Interest Technology Initiative at UMass is pleased to announce the recipients of its 2026-2027 Faculty Fellowship. Fellows will receive seed funding to support research, scholarly writing, or curriculum development on the theme of AI and the Future of Work.
Throughout 2026-2027 the PIT Fellows will meet monthly to present and discuss their work in progress. They will work across campus and with the broader PIT network to advance PIT and their projects. They will also work together to develop a visiting speaker series, inviting outside speakers to campus for public events on AI and the Future of Work.
Fellows explore PIT-related questions and integrative solutions by:
- Addressing a complex problem with public interest impacts (privacy, safety, security, equity, sustainability, ethical behavior, etc.)
- Engaging the responsible use of artificial intelligence
- Reducing cultural, economic, and other societal disparities
The following faculty members and their respective projects and teams were selected for this year’s fellowship:
College of Education
Project Lead: Javier Suárez-Álvarez, Associate Professor of Research, Educational Measurement, and Psychometrics and Associate Director of the Center for Educational Assessment
College of Engineering
Project Lead: Douglas Eddy, Research Assistant Professor, Mechanical and Industrial Engineering
College of Engineering
Project Lead: Taqi Raza, Assistant Professor, Electrical and Computer Engineering
Raza’s work investigates the hidden infrastructure behind digital payments, revealing security vulnerabilities that allow fraudulent transactions even when consumers believe their cards are protected. This project will use AI to transform today’s reactive fraud detection into proactive system hardening, analyzing everyday payment flows to design intelligent financial safeguards and evidence-based policy guidance.
College of Humanities and Fine Arts
Project Lead: Christopher White, Professor, Music Theory and Graudate Program Director
This project includes three research activities investigating AI’s impact on musical creativity and labor: archival research into David Cope’s pioneering work at UC Santa Cruz; observations and interviews at AI laboratories in the Boston area; and an interview with engineers and executives at Suno. Will AI replace working musicians, or will it merely expand who gets to make music?
College of Information and Computer Sciences
Project Lead: Anna Green, Assistant Professor, College of Information and Computer Sciences
This project seeks to explore the tension between the reported detrimental effects of AI on computer science education, and the apparent benefits of AI for expert-level industry coders. How can we train students to become the expert-level coding professionals of the future?
College of Natural Sciences
Project Lead: Katelyn Hudson, Lecturer, Environmental Conservation
College of Social and Behavioral Sciences
Project Lead: Weiai (Wayne) Xu
This project will develop AgentAcademy to address a critical AI and Future of Work question: as AI tools increasingly assist researchers, how do we ensure they improve rather than compromise research integrity? The project develops the CommDAAF Framework, an open-source guardrail that requires multiple AI systems to independently analyze data and cross-check their findings. It also pivots AgentAcademy as a decentralized network where AI research assistants and human scholars learn from each other to strengthen the guardrail and continuously sharpen their own research skills through real social science work.
Isenberg School of Management
Project Lead: Marta Calás, Professor of Organizational Studies and International Management
This project will develop a new Human Resources course in the Management Department that addresses human-technology relationships in the context of AI. It explores possibilities for different forms of relationality between humans and technology, including hybrid forms in which becoming the more-than-human is addressed and questions of ethics and values are considered. While generative AI reduces the need for human workers in some roles, it becomes essential that leaders understand the uniquely human in the sustainable success of both humans and organizations. Here identifying and focusing on uniquely human skills that AI cannot truly replicate would help humans to find their place in a future of work with more automation than ever.










