Research at OpenMined

Seungeun Lee is a PhD student in Computer Science at New York University, advised by Prof. Julia Stoyanovich at the Center for Responsible AI. She tries to make machine learning systems more explainable and trustworthy. Broadly, she is interested in designing AI that remains reliable as data, environments, and people change, both in practice and theory.

Victor Ojewale is a Ph.D. candidate in Computer Science at Brown University, affiliated with the Center for Technological Responsibility. His research builds evaluation and audit infrastructure for large language models and the agents built on them, with a focus on moving evaluation out of the sandbox and toward the communities that actually use these systems. He has developed community-centered evaluation infrastructure for LLMs, multilingual functional evaluations, and measures of when agents should act versus defer, and has co-authored regulatory filings to NIST, NTIA, and the European Commission. He was previously a researcher with the Mozilla Open Source Audit Tooling Project.

Ben Bucknall is a DPhil (PhD) student at the University of Oxford, and an affiliate at the Oxford Martin AI Governance Initiative, where his work focuses on technical AI governance. He was previously a visiting student researcher at Stanford University, a chapter lead for the 2026 International AI Safety Report, and organiser of the ICML workshop on technical AI governance research. Before his DPhil, Ben worked as a research scholar at GovAI and technical advisor in the UK AI Security Institute. His current research centres around the governance of AI agent protocols and assurance of model versioning.

Peihan Liu is a PhD student in Computer Science at Columbia University, advised by Rachel Cummings and Roxana Geambasu. His research focuses on trustworthy machine learning, with an emphasis on differential privacy and the foundations of modern machine learning.

Hannah Ismael is a Senior Program Associate at Mozilla Foundation. She is interested in how law and policy can enable publicly accountable governance of AI, and in where existing frameworks fall short of creating a technological ecosystem that meaningfully balances privacy, transparency, security, and innovation. These questions have led her to explore intellectual property, privacy, and antitrust law, as well as the broader political and geopolitical forces shaping AI policy. Her work has been featured in ACM and JIGS and has contributed to partnerships with governments, nonprofits, and think tanks in pursuit of more equitable benefit-sharing and participatory governance.

Ayana Hussain is interested in privacy-preserving machine learning and AI safety, particularly in how individuals and communities can exercise agency and influence over algorithms, models, and digital platforms. Her research explores how individuals can control whether and how their data is shared, as well as how groups can use algorithmic collective action to influence computational technologies. She is also interested in how communities can organize to resist technological harms, particularly those involving AI-enabled misinformation and surveillance, and in how influence over increasingly powerful AI technologies can be distributed more widely.

Niloofar Mireshghallah is an Assistant Professor at Carnegie Mellon University, with appointments in the Engineering & Public Policy Department and Language Technologies Institute, as well as a courtesy appointment in the Software and Societal Systems Department. She is also a core member of CyLab, where she leads the Looni Lab. Her research sits at the intersection of privacy, machine learning, and the societal implications of AI, with a particular focus on contextual integrity, information-flow norms, natural language processing, LLM reasoning, and AI for science, including chemistry and drug discovery. Her work explores the relationship between data, its influence on machine learning models, and the expectations of the people and communities who generate, regulate, and use that data. She is particularly interested in developing technical approaches to privacy and security that account for the social and normative contexts in which AI systems operate.

Prior to joining Carnegie Mellon, she was a Founding Member of Technical Staff at humans&, a Research Scientist with Meta AI’s FAIR Alignment group, and a postdoctoral scholar at the University of Washington. She received her PhD from UC San Diego and has also conducted research with Microsoft Research on differential privacy, model compression, and data synthesis. Her work has been recognized with the NCWIT Collegiate Award and the Rising Star in Adversarial ML Award.

Nick Vincent is an Assistant Professor in Computing Science at Simon Fraser University and director of the People- and Data-centric Computing Research Group (PadComp). He studies the content ecosystems and data supply chains that fuel data-dependent technologies like search engines, recommender systems, and generative AI. This involves exploring avenues for people to control how data flows and participate in the governance of AI systems. The overarching goal of this research is to work towards highly capable and widely beneficial AI technologies that mitigate—rather than exacerbate—inequalities in wealth and power. His work is published in responsible AI venues like ACM FAccT and human-computer interaction venues like ACM CHI. His dissertation on data leverage received the 2024 ACM SIGCHI Outstanding Dissertation Award.

Prof. Vincent was previously a postdoc working with the Computational Communication Research Lab at UC Davis and the Social Futures Lab at the University of Washington. He received his PhD from Northwestern University’s Technology and Social Behavior program (a joint degree in computer science and communication), where he worked in the People, Space, and Algorithms Research Group.

Kyoko Eng is a Nashville-based interaction designer. Her practice explores the problem spaces of Privacy and AI, Constructive Communication, and the Public and Private Self. Drawing from participatory design, critical design theory, and ethnographic research practices, she investigates how we might cultivate greater literacy, agency, and contextual awareness within our digital interactions.