Julia Stoyanovich
Associate Professor
United StatesResearch Interests
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About
Julia Stoyanovich is an Associate Professor at New York University, United States. Her research areas include fairness in machine learning, automated decision systems, and data equity, as evidenced by her recent articles on topics such as the impact of the Russian invasion on student displacement in Ukraine and responsible AI literacy. She engages with pressing issues in algorithmic transparency and stakeholder engagement in technology development.
Positions (1)
Gautam Kamath
New York University
10 months ago
Professor Gautam Kamath is recruiting PhD students to join the Computer Science Department at the Courant Institute of Mathematical Sciences, New York University, starting Fall 2026. The research focus is on learning theory, algorithmic statistics, and trustworthy machine learning, with specific interests in differential privacy, robustness, machine unlearning, data attribution, and AI safety. NYU Computer Science is recognized as a leader in these areas, offering a vibrant research environment and collaboration opportunities with a distinguished group of faculty, including Ainesh Bakshi, Emily Black, Joan Bruna, Eunsol Choi, Siddharth Garg, Yanjun Han, He He, Chinmay Hegde, Pavel Izmailov, Arthur Jacot, Julia Kempe, Qi Lei, Allen Liu, Christopher Musco, Juan Carlos Perdomo, Julia Stoyanovich, Matus Telgarsky, and Andrew Gordon Wilson. All full-time PhD students receive comprehensive financial support, which includes a nine-month stipend, full tuition and fee coverage, and health insurance. Additional compensation is available for those who secure external fellowships or serve as instructional assistants. The department encourages applications from students interested in research abroad at NYU's global campuses in Abu Dhabi or Shanghai. Applicants should have outstanding academic records and a strong command of written English. Required application materials include a statement of purpose, three letters of recommendation, CV, academic transcripts, and proof of English proficiency (TOEFL/IELTS) if applicable. GRE scores are optional. The application deadline is December 12, 2025. Prospective students should mention relevant faculty in their application and can find further details and the application portal at the NYU Computer Science PhD admissions website. For questions, contact Professor Kamath at [email protected].
10 months ago
Articles (10)
Year:
2025
Year:
2024
Year:
2024
Year:
2023
The need for citizens to better understand the ethical and social challenges of algorithmic systems has led to a rapid proliferation of AI literacy initiatives. After reviewing the literature on AI literacy projects, we found that most educational practices in this area are based on teaching programming fundamentals, primarily to K-12 students. This leaves out citizens and those who are primarily interested in understanding the implications of automated decision- making systems, rather than in learning to code. To address these gaps, this article explores the methodological contributions of responsible AI education practices that focus first on stakeholders when designing learning experiences for different audiences and contexts. The article examines the weaknesses identified in current AI literacy projects, explains the stakeholder-first approach, and analyzes several responsible AI education case studies, to illustrate how such an approach can help overcome the aforementioned limitations. The results suggest that the stakeholder-first approach allows to address audiences beyond the usual ones in the field of AI literacy, and to incorporate new content and methodologies depending on the needs of the respective audiences, thus opening new avenues for teaching and research in the field.
Year:
2023
Year:
2023
Year:
2023
Year:
2023
Year:
2023
Data-driven systems can induce, operationalize, and amplify systemic discrimination in a variety of ways. As data scientists, we tend to prefer to isolate and formalize equity problems to make them amenable to narrow technical solutions. However, this reductionist approach is inadequate in practice. In this article, we attempt to address data equity broadly, identify different ways in which it is manifest in data-driven systems, and propose a research agenda.
Year:
2022
Collaborators (5)
Mona Sloane
Assistant Professor of Data Science and Media Studies
University of Virginia
Daniel Dominguez Figaredo
Professor of Connected and Lifelong Learning
Universidad Nacional de Educación a Distancia
Bill Howe
University of Washington
H. V. Jagadish
University of Michigan
Sebastian Schelter
University of Amsterdam
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