معرفی
Dr. Harini Suresh is an Assistant Professor of Computer Science at Brown University, where she is also affiliated with the Department of Science, Technology & Society (STS) and the Center for Technological Responsibility, Reimagination and Redesign (CNTR) at the Data Science Institute. She joined Brown in 2024 after completing her PhD at MIT and a postdoctoral fellowship at Cornell University.
Dr. Suresh explores issues of equity, accountability, agency and participation in technology, with a focus on machine learning and artificial intelligence systems. Her interdisciplinary work draws from science and technology studies (STS), feminist and queer theory, sociology, pedagogy, human-computer interaction (HCI), and critical computing. She leads the Data in Society Collective (DISCO Lab), which focuses on creating grassroots, participatory, accountable, and equitable datasets and sociotechnical systems.
Her research spans participatory AI, feminist data science, and critical approaches to machine learning. Current projects examine capacity building in participatory AI, ownership of AI tools for journalists, and decentralized infrastructures for community agency on social media platforms. Her work often involves co-designing context-specific datasets and models to support civil society activists monitoring gender-related violence.
Dr. Suresh's educational background includes a B.Sc., M.Eng., and PhD in Computer Science from MIT, followed by a postdoc at Cornell University.
Her scientific contributions focus on critical perspectives in AI and machine learning:
- Participatory approaches to AI development and deployment
- Feminist and intersectional frameworks for data science
- Accountability mechanisms throughout the ML life cycle
- Context-specific evaluation of ML models
- Ethical considerations in clinical AI systems
- Community-driven data collection for social justice
Dr. Suresh's advisory work centers around the DISCO Lab, where she mentors students and researchers in critical data science. Her approach emphasizes interdisciplinary collaboration, bringing together computer science, social sciences, and humanities perspectives to address complex sociotechnical challenges. She teaches CSCI 2952W - Critical Data and Machine Learning Studies at Brown University.
