Qiaoyu Tan
Assistant Professor
Studies graph machine learning, graph foundation models and multimodal learning, with applications in bioinformatics and healthcare.
Qiaoyu Tan's lab at NYU works on graph mining & networks, generative & foundation models and graph learning. 40 papers since 2023, 30 at top venues, mostly EMNLP and NeurIPS. 9 PhD students. 4 alumni. Applications close .
At a glance
- papers, 2023–2026
- 10/ year
- at T1 venues
- 75%
- PhD students
- 9
- MS students
- 0
- alumni listed
- 4
Papers by yearfrom the lab homepage, 2023–2026
- 11 papers2026 · T1 10 · collecting
- 9 papers2025 · T1 7
- 6 papers2024 · T1 4
- 14 papers2023 · T1 9
T1 venuesOtherVenues: EMNLP, NeurIPS, WSDM, CIKM, TKDE
Research focuspaper titles that use each topic's words
- Graph learning26 papers
- Graph mining & networks21 papers
- graph20 papers
- Generative & foundation models7 papers
- shanghai0 papers
Paper list
From the lab homepage.
202611 papers
- —ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2026
- ACL (T1)AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning (opens in a new tab)
- EMNLP (T1)ChronoClinQA: A Longitudinal Multi-Admission Benchmark for Clinical Reasoning over Electronic Health Records (opens in a new tab)
- ICLR (T1)Coarse-to-Fine Learning of Dynamic Causal Structures (opens in a new tab)
- NeurIPS (T1)DAGent: Evaluate-then-Grow Planning for Deep Research Agents (opens in a new tab)
- EMNLP (T1)GraphRAG-Router: Learning Cost-Efficient Routing over GraphRAGs and LLMs with Reinforcement Learning (opens in a new tab)
- EMNLP (T1)GraphVerse: A Comprehensive Visual Graph Reasoning Benchmark for Multimodal Large Language Models (opens in a new tab)
- CVPR (T1)GraphVLM: Benchmarking Vision Language Models for Multimodal Graph Learning (opens in a new tab)
- CVPR (T1)Mario: Multimodal Graph Reasoning with Large Language Models (opens in a new tab)
- EMNLP (T1)One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models (opens in a new tab)
- EMNLP (T1)SinoGlyphBench: A Diagnostic Benchmark for Chinese Glyph-level Obfuscation in LLM Moderation (opens in a new tab)
20259 papers
- ICML (T1)Concept-Centric Token Interpretation for Vector-Quantized Generative Models (opens in a new tab)
- EMNLP (T1)CrystalICL: Enabling In-Context Learning for Crystal Generation (opens in a new tab)
- NeurIPS (T1)DGCBench: A Deep Graph Clustering Benchmark (opens in a new tab)
- Findings of the Nations of the Americas Chapter of the Association for Computational LinguisticsGraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design (opens in a new tab)
- —IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025
- NeurIPS (T1)Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness–Generalization Perspective (opens in a new tab)
- ICML (T1)Towards a Unified Framework of Clustering-based Anomaly Detection (opens in a new tab)
- KDD (T1)TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks (opens in a new tab)
- WSDM (T1)UniGLM: Training One Unified Language Model for Text-Attributed Graphs (opens in a new tab)
20246 papers
- TISTDIRECT: Dual Interpretable Recommendation with Multi-aspect Word Attribution (opens in a new tab)
- KDD (T1)GAugLLM: Improving Graph Contrastive Learning for Text-attributed Graphs with Large Language Models (opens in a new tab)
- NeurIPS (T1)Gradient Rewiring for Editable Graph Neural Network Training (opens in a new tab)
- TKDE (T1)Graph Contrastive Learning with Personalized Augmentation (opens in a new tab)
- Findings of the Association for Computational LinguisticsReasoning Like a Doctor: Improving Medical Dialogue Systems via Diagnostic Reasoning Process Alignment (opens in a new tab)
- CIKM (T1)Retrieval-Enhanced Knowledge Editing for Multi-Hop Question Answering in Language Models (opens in a new tab)
202314 papers
- WSDM (T1)Active Ensemble Learning for Knowledge Graph Error Detection (opens in a new tab)
- WSDM (T1)Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection (opens in a new tab)
- TKDE (T1)Collaborative Graph Neural Networks for Attributed Network Embedding (opens in a new tab)
- ICDMDouble Wins: Boosting Accuracy and Efficiency of Graph Neural Networks by Reliable Knowledge Distillation (opens in a new tab)
- CIKM (T1)GiGaMA: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction (opens in a new tab)
- WWW (T1)Hierarchy-Aware Multi-Hop Question Answering over Knowledge Graphs (opens in a new tab)
- CIKM (T1)Homophily-enhanced Structure Learning for Graph Clustering (opens in a new tab)
- TKDE (T1)Integrating Entity Attributes for Error-Aware Knowledge Graph Embedding (opens in a new tab)
- PAKDDInterest Driven Graph Structure Learning for Session-based Recommendation (opens in a new tab)
- AMIAMulti-task Learning for Post-transplant Cause of Death Analysis (opens in a new tab)
- NeurIPS (T1)OpenGSL: A Comprehensive Benchmark for Graph Structure Learning (opens in a new tab)
- ICDMReinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection (opens in a new tab)
Where alumni went4 listed
Apply to NYUFall 2027
Computer Science PhD (Courant / Tandon)
- Round
- Single round (December)
- Opens
- —
- GRE
- See program page
- English
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- Fee
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Not yet confirmed for this intake.
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