Sean Du
Assistant Professor
Studies reliable machine learning and AI safety for foundation models, including out-of-distribution detection.
Sean Du's lab at NTU works on trustworthy & safe ML, generative & foundation models and recognition, detection & segmentation. 23 papers since 2023, 16 at top venues, mostly ICML and NeurIPS. 4 PhD students and 5 master's students. 2 alumni.
At a glance
- papers, 2023–2026
- 6/ year
- at T1 venues
- 70%
- PhD students
- 4
- MS students
- 5
- alumni listed
- 2
Papers by yearfrom the lab homepage, 2023–2026
- 9 papers2026 · T1 5 · collecting
- 4 papers2025 · T1 4
- 5 papers2024 · T1 4
- 5 papers2023 · T1 3
T1 venuesOtherVenues: ICML, NeurIPS, ACL, ICLR, TMLR
Research focuspaper titles that use each topic's words
- Recognition, detection & segmentation8 papers
- Generative & foundation models2 papers
- Trustworthy & safe ML0 papers
- reliable machine0 papers
- reliable0 papers
Paper list
From the lab homepage.
20269 papers
- ECCV (T1)Agent-OBJ: Prompt-Driven 3D Adversaries for Multi-Modal Perception
- EMNLP OralDeeper is Not Always Better: Mitigating the Alignment Tax via Confident Layer Decoding
- KDD Research Track OralFLaG: Fine-Grained Latent Grouping for Hallucination Detection
- ICML (T1)Harnessing Reasoning Trajectories for Hallucination Detection via Answer-agreement Representation Shaping
- TMLR (T1)Journal to Conference Certificate (Top 10%)
- AI MagazineTeach AI What It Doesn't Know
- KDD Research TrackTraining a Generalist Hallucination Detector across Multiple Domains via Adaptive Layer Aggregation
- ACL (T1)Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities
- ACL (T1)VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation
20254 papers
- ICML (T1)Challenges and Future Directions of Data-Centric AI Alignment
- ICML (T1)How to Steer LLM Latents for Hallucination Detection?
- NeurIPS (T1)Learning Better Reward Model with Latent Space Synthesis
- ICML (T1)Understanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach
20245 papers
- NeurIPS (T1)HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detection
- ICLR (T1)How Does Unlabeled Data Provably Help Out-of-distribution Detection?
- TMLR (T1)Out-of-Distribution Learning with Human Feedback
- NeurIPS Safe Generative AI WorkshopSafety-Aware Fine-Tuning of Large Language Models
- ICML (T1)When and How Does In-Distribution Label Help Out-of-Distribution Detection?
20235 papers
- ICLR (T1)[PDF][Code]
- NeurIPS Workshop5: Enhanced benchmark for out-of-distribution detection
- NeurIPS (T1)Dream the Impossible: Outlier Imagination with Diffusion Models
- ICML (T1)Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection
- Transactions on Machine Learning Research (TMLR)Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions
Where alumni went2 listed
Apply to NTUFall 2027
Not announced yet
Computing and Data Science PhD
- Round
- Main
- Opens
- —
- GRE
- not required
- English
- TOEFL/IELTS
- Fee
- See program page
Not yet confirmed for this intake.
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Data
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- Collected
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- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
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