CRA Trustworthy AI Research Fellowship for Early Career Scholars

CRA Trustworthy AI Research Fellowship for Early Career Scholars is sponsored by Computing Research Association (CRA), funded by Microsoft. This fellowship supports early-career computing researchers who are advancing trustworthy artificial intelligence (AI) through interdisciplinary collaboration with humanistic social sciences.

CRA Trustworthy AI Research Fellowship for Early Career Scholars - CRA CRA Trustworthy AI Research Fellowship for Early Career Scholars Preparing the Next Generation of Trustworthy AI Research Leaders The CRA Trustworthy AI Research Fellowship for Early Career Scholars , funded by Microsoft, supports early-career computing researchers who are advancing trustworthy artificial intelligence (AI) through interdisciplinary collaboration with humanistic social sciences.

Inspired by the 2022 National Academies report Fostering Responsible Computing Research , the fellowship is designed to help scholars integrate ethical, societal, and human-centered perspectives directly into AI research—strengthening both technical innovation and its broader social impact. Following a successful inaugural year, CRA has launched the second cohort of CRA Trustworthy AI Research Fellows for the 2026-2027 program cycle.

The fellowship brings together a national cohort of early-career scholars for structured training, sustained peer engagement, mentoring, and collaborative work focused on responsible and socially grounded AI research.

What the Fellowship Offers Over 12 months, Fellows participate in: A four-day, in-person Trustworthy AI Field School Quarterly virtual convenings to support ongoing collaboration Mentoring and professional development across disciplines Opportunities to engage in Trustworthy AI initiatives with CRA partner institutions, including CRA member and CAHSI institutions Opportunities for mentorship from established scholars and practitioners advancing trustworthy computing Selected Fellows receive a $17,000 stipend, along with support for travel, lodging, and meals associated with in-person fellowship activities.

Through the fellowship, participants: Advance their own research at the intersection of computing and the social sciences Contribute to shared frameworks and language for trustworthy AI Build durable professional networks with peers and mentors across academia, industry, and policy Help shape scalable models for interdisciplinary training in responsible computing Who the Fellowship Is For The CRA Trustworthy AI Research Fellowship is designed for early-career computing researchers who: Are working within 1-3 years post-PhD (conferral date between May 1, 2023 and July 1, 2025) Have interdisciplinary training or research experience in a humanistic social science Are interested in community-based, public-interest, or socially responsible approaches to AI research Applications for the Current Cohort Are Closed Applications for the 2026-2027 cohort have now closed.

Notify Me When Applications Open For questions about the fellowship, contact Janine Myszka at jmyszka@cra. org. Jamell Dacon is an Assistant Professor of Computer Science at Morgan State University and Director of the Machine Intelligence and Data Science (MINDS) Lab.

His research sits at the intersection of computer science and socio-ethical impact, with a focus on natural language processing and the integration of artificial intelligence (AI) in education and healthcare. He specializes in empirically modeling social factors to detect and mitigate algorithmic harms, bridging complex technological systems and their real-world societal consequences.

Dacon earned his PhD in Computer Science from Michigan State University. His work is dedicated to building safer, more equitable, and culturally responsible AI systems.

He will use the CRA Trustworthy AI Research Fellowship to amplify the vital role that Historically Black Colleges and Universities (HBCUs) play in shaping the future of the responsible technology ecosystem, and to address the matriculation gap between undergraduate and graduate programs through trustworthy AI research. Samantha Dalal is a Postdoctoral Research Fellow at the Princeton University Center for Information Technology Policy.

Her research focuses on how the introduction of automated decision-making systems and AI technologies impacts the organization and quality of work. She uses community-based participatory research methods to co-design tools that support worker participation in the monitoring, design, and governance of these systems.

Dalal earned her PhD in Information Science from the University of Colorado Boulder, where her doctoral research was supported by the Mozilla Technology Fund and the CU Engage Community-Based Participatory Research Fellowship. She holds a BA in Economics and Statistics from the University of California, Santa Barbara.

She is a founding member of the Workers Algorithm Observatory, a decentralized research and design collaborative that builds and maintains tools helping workers investigate algorithmic management systems. She will use the CRA Trustworthy AI Research Fellowship to support her ongoing research mapping the social, legal, and technical infrastructures that enable precarious workers to hold algorithmic systems accountable for their impacts.

Jazette Johnson is a CREATE Postdoctoral Fellow in the Paul G. Allen School of Computer Science and Engineering at the University of Washington.

She designs and studies technologies that promote trust, safety, and well-being in digital environments by examining how communities — including people with disabilities, older adults with dementia, and Black and Brown communities — navigate and understand AI-powered systems in health and social contexts.

Her work draws on community-based approaches to center accessibility, equity, and the societal impacts of AI and digital health technologies. Johnson earned her PhD in Informatics from the University of California, Irvine, where she explored the dynamics of health information sharing and peer support across online communities.

She holds an MS in Interdisciplinary Studies of Human-Computer Interaction from Vanderbilt University and a BS in Computer Science from Spelman College.

She will use the CRA Trustworthy AI Research Fellowship to further develop community-engaged frameworks for trustworthy AI while building collaborations with researchers and practitioners across academia, industry, and policy Anantaa Kotal is an Assistant Professor of Computer Science at the University of Texas at El Paso, where she leads the PRISM Lab.

Her research focuses on trustworthy and responsible AI, with an emphasis on generative modeling and translating policy intent into enforceable computational frameworks. A defining thread of her work is bridging the gap in how principles of trustworthy AI are understood across diverse communities and stakeholders.

Kotal earned her PhD in Computer Science from the University of Maryland, Baltimore County, and her BE in Computer Science and Engineering from Jadavpur University. Her interdisciplinary team at the PRISM Lab brings together Earth and Environmental Science, Public Health Science, and Engineering around shared questions in responsible AI.

Her community-engaged research in the Paso del Norte border region has shaped a broader agenda around how trust is perceived differently across groups — and how those priorities can be translated into actionable AI system design. She will use the CRA Trustworthy AI Research Fellowship to develop replicable, community-centered design principles for trustworthy AI that can travel across disciplines and the communities they serve.

Xi Lu is an Assistant Professor in the Department of Information Science, with a joint appointment in Computer Science and Engineering, at the University at Buffalo. Her research sits at the intersection of human-computer interaction, personal health informatics, and computer-supported cooperative work, with a focus on how sociocultural contexts shape people’s experiences with health technologies.

Her scholarship has appeared in leading HCI venues — including CHI, CSCW, IMWUT, and DIS — and has been recognized with a CHI 2025 Best Paper Award. Lu earned her PhD in Informatics from the University of California, Irvine, where her doctoral research examined how sociocultural contexts shape people’s experiences with health tracking technologies, with a particular focus on collaborative approaches to pregnancy and reproductive health.

She holds an MS in Human-Computer Interaction and Design from Indiana University Bloomington and a BE in Industrial Design from Xi’an Jiaotong University.

She will use the CRA Trustworthy AI Research Fellowship to deepen and expand her community-engaged research on women’s health and culturally responsive pregnancy technologies, advancing AI systems that are equitable, accountable, and grounded in the lived experiences of underserved communities. Veronica Rivera is an Assistant Professor at the Georgia Institute of Technology School of Cybersecurity and Privacy.

Her research spans human-computer interaction, security, and privacy, with a particular focus on enabling safer digital experiences for all users — especially in contexts where technology mediates interpersonal relationships. She uses empirical and design methods to characterize and measure digital safety risks and to reimagine safer alternatives alongside affected communities.

Rivera earned her PhD from the University of California, Santa Cruz, and previously held postdoctoral positions at Stanford University and the Max Planck Institute for Security and Privacy. Through the CRA Trustworthy AI Research Fellowship, she aims to characterize how AI is changing the nature of tech-facilitated abuse and how to build data collection pipelines that enable longitudinal evaluation of these risks.

Cesa Salaam is a Tenure-Track Assistant Professor of Computer Science at Virginia State University, where he leads the Kafou AI Research Lab.

His research sits at the intersection of natural language processing, fairness and bias mitigation, culturally informed AI, AI governance, and responsible system design, with a particular focus on quantifying cultural emergence in AI systems and developing inclusive technologies for real-world impact.

Salaam also brings a keen interest in indigenous African knowledge systems and how those traditions can inform the development of innovative technologies addressing critical challenges facing Africa and its diaspora. Salaam earned his PhD in Computer Science from Howard University, where his dissertation explored cultural emergence and bias mitigation in AI systems.

His broader scholarly work examines how AI can be designed, evaluated, and governed in ways that are socially accountable and culturally grounded. He will use the CRA Trustworthy AI Research Fellowship to deepen his research through collaborative projects with fellow researchers, scholars, and community organizers, contributing to the development of culturally contextualized AI systems.

Harini Suresh is an Assistant Professor of Computer Science at Brown University, core faculty at the Brown Center for Technological Responsibility, Reimagination and Redesign (CNTR), and an incoming fellow at the Berkman Klein Center. Her research asks how communities can meaningfully shape, govern, and own the AI systems increasingly affecting their lives and work.

Her work combines qualitative studies, systems-building, and participatory partnerships with communities including journalists, data activists, and mental health practitioners. Suresh earned her PhD, MEng, and BS in Computer Science from MIT, where her doctoral research explored participatory, context-grounded approaches to AI, drawing on feminist theory, science and technology studies, and human-computer interaction.

She completed a postdoctoral fellowship at Cornell Tech, examining the limitations and opportunities of participatory approaches in the era of general-purpose foundation models. She will use the CRA Trustworthy AI Research Fellowship to deepen work on conceptual and technical foundations for community-controlled AI, envisioning ways for people to govern, reimagine, or refuse AI on their own terms.

CRA Trustworthy AI Research Fellowship Field School The CRA Trustworthy AI Research Fellowship Field School is a core component of the one-year CRA Trustworthy AI Research Fellowship. This four-day, in-person program equips Fellows — early-career computing scholars advancing trustworthy and responsible AI — to deepen engagement with the humanistic social sciences and strengthen the societal impact of their research.

Fellows participate in intensive tutorials, research workshops, and mentorship sessions with leaders across computing, humanistic social sciences, ethics, and policy. Past mentors include Bobby Kleinberg, James Mickens, Desmond Patton, and Moshe Vardi. The Field School emphasizes interdisciplinary exchange, methodological fluency, and collaborative agenda-setting.

A Unique Interdisciplinary Environment In 2026, the Field School will be co-located in Cambridge, Massachusetts with the Sloan Foundation’s Metascience & AI Summer School and Northeastern University’s AI + Data Ethics (AIDE) Summer Training Program.

This creates a multi-cohort environment for shared keynotes, lightning talks, informal discussion, and cross-program collaboration — while preserving a focused cohort experience centered on trustworthy AI research.

By the end of the Field School, Fellows will have: Expanded methodological fluency across computing and humanistic social sciences Deeper interdisciplinary professional networks spanning academia, industry, and policy Clearer research trajectories for advancing trustworthy and responsible AI Momentum to contribute to scalable models for interdisciplinary AI training and research The Field School serves as a catalyst for sustained collaboration throughout the fellowship year and beyond, reinforcing CRA’s commitment to developing the next generation of leaders in trustworthy AI research.

Seeking Early-Career Computing Scholars Passionate About Integrating Social Science Insights with Trustworthy AI Research The CRA Trustworthy AI Research Fellowship is designed for early-career computing researchers who combine strong technical expertise with interdisciplinary experience in the social sciences. Ideal candidates are committed to advancing trustworthy AI and eager to lead collaborative, cross-disciplinary work.

To be eligible for the next cohort, applicants must meet all of the following criteria: Are 1-3 years post-PhD (conferred between May 1, 2023 and July 1, 2025) Disciplinary Background: Hold a primary doctoral degree in a computing-related discipline, such as: Or other closely related computing fields Interdisciplinary Experience: Have secondary training or substantial research experience in at least one social science discipline, including but not limited to: Communication and media studies Studies of vulnerable populations Science and technology studies (STS) Institutional Affiliation: Hold a faculty, postdoctoral, or visiting researcher position at an institution of higher education in the United States.

To be notified when the 2027-2028 application opens, please fill out this interest form. Notify Me When Applications Open Answers to Commonly Asked Questions Who is eligible to apply for this fellowship?

Early-career scholars (1-3 years post-PhD, conferral date between May 1, 2023 and July 1, 2025) with a primary doctoral degree in a computing-related field and interdisciplinary training or research experience in a social science field are eligible. Applicants must hold a tenure-track faculty or visiting researcher position (e.g., postdoc or fellowship) at a U.S. institution of higher education.

For more details, please visit the Eligibility tab. What costs are covered by the fellowship? Fellows receive a $17,000 stipend plus support for travel, lodging, and meals associated with the four-day, in-person Trustworthy AI Field School.

What if my discipline isn’t explicitly listed? Applicants whose interdisciplinary training or research experience closely aligns with the fellowship’s goals are encouraged to apply and should clearly outline how their background aligns with the fellowship’s focus in their application. When is the next opportunity to apply?

Applications for the 2026-2027 CRA Trustworthy AI Research Fellowship have now closed. To be notified when applications open for the next cohort, please complete the notification form . Notify Me When Applications Open For further questions, please contact Janine Myszka at jmyszka@cra.

org. Supporting a Vibrant, Connected, and Socially Responsible Computing Research Community The Computing Research Association (CRA) catalyzes computing research by uniting industry, academia, and government.

CRA counts among its members nearly 300 North American organizations active in computing research and works with these organizations to represent the computing research community and to effect change that benefits both computing research and society at large.

By leading the computing research community, informing policymakers and the public, and promoting the development of an innovative and responsible computing research workforce, CRA is able to carry out its mission of catalyzing computing research.

Connect with the CRA Trustworthy AI Research Fellowship Team If you have questions or need additional information about the CRA Trustworthy AI Research Fellowship, please contact our team via Janine Myszka at jmyszka@cra. org . Taslima Akter is an Assistant Professor of Computer Science at the University of Texas San Antonio.

Her research focuses on accessibility and privacy for blind and low-vision individuals engaging with AI technologies. With secondary training in accessibility studies and a strong record of interdisciplinary collaboration, she centers community-informed design.

Akter earned her PhD and MS in Computer Science from Indiana University Bloomington, where her dissertation examined how to reduce privacy risks for blind and low-vision users of camera-based assistive technologies. She holds a BS in Computer Science and Engineering from the Bangladesh University of Engineering and Technology (BUET).

Her work draws on participatory methods to surface how AI systems may reinforce stigma, bias, and inequity, particularly for disabled communities. She will use the CRA Trustworthy AI Research Fellowship to co-develop inclusive AI frameworks that prioritize equity and lived experience, and to help build a shared foundation for trustworthy AI that reflects the needs and values of marginalized users.

Diana Freed is an Assistant Professor of Computer and Data Science at Brown University, a Visiting Scholar at Harvard Law School’s Petrie-Flom Center, and a Faculty Associate at the Berkman Klein Center for Internet and Society at Harvard. Her work focuses on human-centered security, privacy, and AI governance in healthcare and social services, with an emphasis on accountability and equity.

She holds an MA in Counseling and Clinical Psychology and completed post-graduate clinical training, which informs her interdisciplinary research.

She will use the CRA Trustworthy AI Research Fellowship to deepen her work on algorithmic fairness and social impact, particularly in high-stakes domains such as healthcare, legal services, and digital safety, where AI intersects with vulnerable populations and complex systems of trust and accountability. Vinitha Gadiraju is an Assistant Professor of Computer Science at Wellesley College.

Her research investigates the relationships between disabled people and generative AI technologies, focusing on perception, trust, and harm mitigation in interactions with tools such as chatbots. Her work draws on perspectives from human-computer interaction, education, and sociology and follows a community-based research approach.

Gadiraju earned her PhD and MS in Computer Science from the University of Colorado Boulder, where her research explored how technology can support visually impaired children and their learning through participatory and naturalistic design methods. She completed her BS in Computer Science with a minor in Psychology at the University of Oregon.

Her experience includes work at Google Research’s People + AI Research (PAIR) initiative, where she led focus groups examining how large language models perpetuate harms toward the disability community.

Through the CRA Trustworthy AI Research Fellowship, Gadiraju aims to explore trust, harm identification, user safety, and relationship formation in sensitive AI use contexts, such as health navigation and companionship, and advocate for disability-inclusive AI development.

Dhruv “DJ” Jain is an Assistant Professor of Computer Science and Engineering at the University of Michigan, with a courtesy appointment in the School of Information and an affiliate appointment in the Medical School. His research spans human-computer interaction, accessible computing, and Deaf/disability studies.

He co-designs AI systems with Deaf and disabled communities, using participatory design, autoethnography, and mixed-methods fieldwork to surface how AI technologies intersect with lived experience. Jain earned his PhD and MS in Computer Science & Engineering from the University of Washington, where he also completed a minor in Disability Studies.

He holds an MS from the MIT Media Lab and a BS in Computer Science & Engineering from the Indian Institute of Technology Delhi, where he also minored in Sociology. His work focuses on audio-based AI, human-centered guardrails, and equitable design practices that enhance accessibility and mitigate bias.

Through the CRA Trustworthy AI Research Fellowship, Jain aims to help integrate accessibility and Deaf/disability studies into broader conversations around AI development, contributing to more inclusive, socially responsible, and human-centered AI systems. Yasmine Kotturi is an Assistant Professor of Human-Centered Computing in the Information Systems Department at the University of Maryland, Baltimore County.

She designs and builds sociotechnical systems that foster worker resilience by centering relational, community-driven practices —particularly by scaffolding peer support among people navigating precarious forms of employment and entrepreneurship.

Her research combines human-computer interaction with insights from labor studies and feminist theory to develop approaches such as community-based software engineering that shift power in how AI systems are built and used. Kotturi collaborates closely with community partners to develop AI-powered tools and infrastructures grounded in community expertise, including projects like BizChat ( https://bizchat-io. vercel.

app/ ). She earned her PhD and MS in Human-Computer Interaction from Carnegie Mellon University, where she specialized in computer science, and holds a BS in Cognitive Science from the University of California, San Diego. Through the CRA Trustworthy AI Research Fellowship, Kotturi aims to transform computing pedagogy and equip future technologists to navigate the ethical and societal stakes of AI development.

Calvin Liang is a Mancosh Postdoctoral Fellow in Communication Studies at Northwestern University. His research investigates how AI mediates intimacy and advances health equity. He brings interdisciplinary expertise from human-centered design, communication studies, and human factors engineering.

Liang holds a PhD in Human Centered Design & Engineering from the University of Washington, an MS in Human Factors Engineering, and a BS in Engineering Psychology from Tufts University. Through the CRA Trustworthy AI Research Fellowship, he aims to gain guidance in responsibly developing AI systems that support digital intimacy and health equity.

Lindsay Sanneman is an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University. Her research on Transparent Value Alignment explores how AI systems can align with human goals through explainability and mutual understanding. She is especially interested in bridging technical innovation with human-centered evaluation to ensure trustworthy outcomes in real-world settings.

She earned her PhD in Autonomous Systems from the Massachusetts Institute of Technology, where she also collaborated across disciplines to incorporate perspectives from cognitive psychology and human factors into her work.

Through the CRA Trustworthy AI Research Fellowship, she aims to integrate social science perspectives into AI alignment and transparency research, and help build interdisciplinary frameworks for more responsible AI systems. Jayshree Sarathy is a Senior Research Fellow and incoming Assistant Professor of Computer Science at Northeastern University.

Her work integrates Science and Technology Studies (STS) and computing to examine what responsible and trustworthy AI looks like in the context of public-sector infrastructures. She focuses on bridging epistemic gaps around data, evaluating the social implications of AI, and building tools and materials that align technical development with ethical commitments.

She earned her PhD and SM in Computer Science from Harvard University and her BS in Computer Science from Yale University. Her interdisciplinary approach draws on training in both computer science and the social sciences, and is informed by collaborations with organizations such as the U.S. Census Bureau and the Wikimedia Foundation.

Through the CRA Trustworthy AI Research Fellowship, she aims to build scalable frameworks that reflect both computational and social understandings of technology. Lucretia Williams is a Research Scientist at Howard University’s Institute of Human-Centered AI and director of the ATHENA Lab (Advancing Technologies in Health, Education, and New Ventures in AI).

Her research spans AI ethics, health, and education, and is rooted in community-based design approaches. She focuses on ensuring that AI technologies are transparent, safe, and culturally responsive—designed with, not just for, communities historically excluded from technological innovation.

She earned her PhD in Informatics from the University of California, Irvine, where her dissertation explored the design and evaluation of culturally responsive digital mental health technology for racial-ethnic minorities. She also holds a BS in Psychology, with a minor in Business Administration, from Howard University.

Across her work, she integrates qualitative and participatory methods to reimagine what it means for AI to be trustworthy in practice. Through the CRA Trustworthy AI Research Fellowship, she aims to expand her multidisciplinary training and collaborate with scholars, practitioners, and policymakers to contribute to the national discourse on the societal implications of AI and the development of ethical, inclusive systems.

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