Reza Shokri
Dean's Chair Associate Professor
Works on data privacy and trustworthy machine learning, including membership inference and fairness analysis of models.
Reza Shokri's lab at NUS works on trustworthy & safe ML, interpretability, evaluation & safety of LMs and efficient training & inference. 17 papers since 2023, 1 at top venues, mostly USENIX Security. 4 PhD students. 9 alumni. Applications close .
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 6%
- PhD students
- 4
- MS students
- 0
- alumni listed
- 9
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 8 papers2025 · T1 0
- 4 papers2024 · T1 1
- 5 papers2023 · T1 0
T1 venuesOtherVenues: USENIX Security
Research focuspaper titles that use each topic's words
- Efficient training & inference4 papers
- membership2 papers
- membership inference2 papers
- Trustworthy & safe ML1 paper
- Interpretability, evaluation & safety of LMs0 papers
Paper list
From the lab homepage.
20258 papers
- —Firewalls to Secure Dynamic LLM Agentic Networks (opens in a new tab)
- Oral International Conference on Learning Representations (ICLR)How Much of My Dataset Did You Use? Quantitative Data Usage Inference in Machine Learning (opens in a new tab)
- —Minerva: A Programmable Memory Test Benchmark for Language Models (opens in a new tab)
- IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)Range Membership Inference Attacks (opens in a new tab)
- —Rethinking Hallucinations: Correctness, Consistency, and Prompt Multiplicity (opens in a new tab)
- —The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text (opens in a new tab)
- ACM Conference on Fairness, Accountability, and Transparency (FAccT)The Data Minimization Principle in Machine Learning (opens in a new tab)
- —Watermark Smoothing Attacks against Language Models (opens in a new tab)
20244 papers
- Spotlight International Conference on Learning Representations (ICLR)Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory (opens in a new tab)
- USENIX Security (T1)Efficient Privacy Auditing in Federated Learning (opens in a new tab)
- International Conference on Learning Representations (ICLR)Leave-one-out Distinguishability in Machine Learning (opens in a new tab)
- Oral International Conference on Machine Learning (ICML)Low-Cost High-Power Membership Inference Attacks (opens in a new tab)
20235 papers
- International Conference on Learning Representations (ICLR)Bias Propagation in Federated Learning (opens in a new tab)
- Conference on Neural Information Processing Systems (NeurIPS)Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks (opens in a new tab)
- ACM Conference on Fairness, Accountability, and Transparency (FAccT)On The Impact of Machine Learning Randomness on Group Fairness (opens in a new tab)
- International Conference on Learning Representations (ICLR)Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning (opens in a new tab)
- Conference on Neural Information Processing Systems (NeurIPS)Unified Enhancement of Privacy Bounds for Mixture Mechanisms via f-Differential Privacy (opens in a new tab)
Where alumni went9 listed
Apply to NUSFall 2027
School of Computing PhD in Computer Science
- Round
- Main
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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