Julia Stoyanovich
Associate Dean of AI Initiatives; Professor; Director of the Center for Responsible AI
Works on responsible AI and data management, including fairness in ranking, data equity and AI governance and education.
Julia Stoyanovich's lab at NYU works on trustworthy & safe ML, social computing & responsible AI and data management & big data. 39 papers since 2023, 18 at top venues, mostly AAAI and VLDB. 4 PhD students. 9 alumni. Applications close .
At a glance
- papers, 2023–2026
- 10/ year
- at T1 venues
- 46%
- PhD students
- 4
- MS students
- 0
- alumni listed
- 9
Papers by yearfrom DBLP, 2023–2026
- 2 papers2026 · T1 0 · collecting
- 12 papers2025 · T1 7
- 9 papers2024 · T1 6
- 16 papers2023 · T1 5
T1 venuesOtherVenues: AAAI, VLDB, FAccT, SIGMOD, EMNLP
Research focuspaper titles that use each topic's words
- Trustworthy & safe ML8 papers
- Data management & big data7 papers
- Social computing & responsible AI6 papers
- ranking3 papers
- governance0 papers
Paper list
From DBLP.
20262 papers
202512 papers
- HILDA@SIGMODCREDAL: Close Reading of Data Models (opens in a new tab)
- NeurIPS (T1)Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data (opens in a new tab)
- ECAIFrom Benchmarking to Understanding FairML (opens in a new tab)
- EAAMOHow Much Effort Is Enough? Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity (opens in a new tab)
- AAAI (T1)Making Transparency Advocates: An Educational Approach Towards Better Algorithmic Transparency in Practice (opens in a new tab)
- HILDA@SIGMODONION: A Multi-Layered Framework for Participatory ER Design (opens in a new tab)
- EMNLP (T1)SAFENUDGE: Safeguarding Large Language Models in Real-time with Tunable Safety-Performance Trade-offs (opens in a new tab)
- FAccTSHAP-based Explanations are Sensitive to Feature Representation (opens in a new tab)
- VLDB (T1)ShaRP: Explaining Rankings and Preferences with Shapley Values (opens in a new tab)
- VLDB (T1)Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation [Experiment, Analysis and Benchmark] (opens in a new tab)
- AAAI (T1)Using Case Studies to Teach Responsible AI to Industry Practitioners (opens in a new tab)
- AAAI (T1)We Are AI: Taking Control of Technology (opens in a new tab)
20249 papers
- IJCAI (T1)A New Paradigm for Counterfactual Reasoning in Fairness and Recourse (opens in a new tab)
- AAAI (T1)A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy (opens in a new tab)
- TKDE (T1)Automated Data Cleaning can Hurt Fairness in Machine Learning-Based Decision Making (opens in a new tab)
- CHI Extended AbstractsMaking Transparency Influencers: A Case Study of an Educational Approach to Improve Responsible AI Practices in News and Media (opens in a new tab)
- SIGMOD (T1)Query Refinement for Diverse Top-k Selection (opens in a new tab)
- SIGMOD Conference CompanionResponsible Model Selection with Virny and VirnyView (opens in a new tab)
- VLDB (T1)Rodeo: Making Refinements for Diverse Top-k Queries (opens in a new tab)
- VLDB Journal (T1)Temporal graph patterns by timed automata (opens in a new tab)
- SIGMOD Conference CompanionThe Game Of Recourse: Simulating Algorithmic Recourse over Time to Improve Its Reliability and Fairness (opens in a new tab)
202316 papers
- AAAI (T1)Counterfactuals for the Future (opens in a new tab)
- VLDB (T1)ERICA: Query Refinement for Diversity Constraint Satisfaction (opens in a new tab)
- ACM Comput. Surv.Fairness in Ranking, Part I: Score-Based Ranking (opens in a new tab)
- ACM Comput. Surv.Fairness in Ranking, Part II: Learning-to-Rank and Recommender Systems (opens in a new tab)
- SIGMOD Conference CompanionFairness in Ranking: From Values to Technical Choices and Back (opens in a new tab)
- Nat. Mac. Intell.Introducing contextual transparency for automated decision systems (opens in a new tab)
- SIGMOD (T1)Most Expected Winner: An Interpretation of Winners over Uncertain Voter Preferences (opens in a new tab)
- SIGMOD Conference CompanionPersonal Data for Personal Use: Vision or Reality? (opens in a new tab)
- VLDB (T1)Query Refinement for Diversity Constraint Satisfaction (opens in a new tab)
- Big Data Soc.Responsible AI literacy: A stakeholder-first approach (opens in a new tab)
- SEBDResponsible Data Management (opens in a new tab)
- EAAMOSetting the Right Expectations: Algorithmic Recourse Over Time (opens in a new tab)
Where alumni went9 listed
Apply to NYUFall 2027
Computer Science PhD (Courant / Tandon)
- Round
- Single round (December)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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