Tingsong Xiao
Ph.D. Candidate in Computer Science · University of FloridaI work on LLM post-training, agentic AI, and machine learning for health. I am a final-year Ph.D. candidate at the University of Florida, advised by Dr. Zhe Jiang, with expected graduation in May 2027.
I have worked as an Applied Scientist Intern at Amazon and an Applied Researcher Intern at eBay. My research develops methods for temporal and graph learning, with publications at ICLR, ICML, NeurIPS, AAAI, and MICCAI, and an ACM SIGSPATIAL Best Paper Award.
Industry Experience
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Amazon
Applied Scientist InternMay 2026 - Aug 2026Developed LLM agentic planning methods for long-horizon tasks, using test-time search and cross-trajectory feedback to guide multi-step decisions. Explored world models for simulating action outcomes and built evaluation pipelines to assess planning quality, constraint satisfaction, and search efficiency.
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eBay
Applied Researcher InternMay 2025 - Aug 2025Worked on LLM post-training for product attribute extraction, including knowledge distillation and fine-tuning of compact language models. Constructed domain-specific training datasets from e-commerce product descriptions and evaluated models for extraction quality and inference efficiency.
Open Source Contribution
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Qwen-AgentWorld
A language world model that simulates interactive environments for training and evaluating general-purpose AI agents.
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XTSFormer (AAAI 2025)
A cross-temporal-scale Transformer for predicting irregularly timed clinical events from electronic health records.
Selected Publications View all
Temporally Detailed Hypergraph Neural ODE for Disease Progression Modeling
Tingsong Xiao, Yao An Lee, Zelin Xu, Yupu Zhang, Zibo Liu, Yu Huang, Jiang Bian, Jingchuan Guo, Zhe Jiang
The 14th International Conference on Learning Representations (ICLR 2026)
We propose Temporally Detailed Hypergraph Neural Ordinary Differential Equation (TD-HNODE), which represents disease progression on clinically recognized trajectories as a temporally detailed hypergraph and learns the continuous-time progression dynamics via a neural ODE framework.
Temporally Detailed Hypergraph Neural ODE for Disease Progression Modeling
Tingsong Xiao, Yao An Lee, Zelin Xu, Yupu Zhang, Zibo Liu, Yu Huang, Jiang Bian, Jingchuan Guo, Zhe Jiang
The 14th International Conference on Learning Representations (ICLR 2026)
We propose Temporally Detailed Hypergraph Neural Ordinary Differential Equation (TD-HNODE), which represents disease progression on clinically recognized trajectories as a temporally detailed hypergraph and learns the continuous-time progression dynamics via a neural ODE framework.
XTSFormer: Cross-Temporal-Scale Transformer for Irregular-Time Event Prediction in Clinical Applications
Tingsong Xiao, Zelin Xu, Wenchong He, Zhengkun Xiao, Yupu Zhang, Zibo Liu, Shigang Chen, My T. Thai, Jiang Bian, Parisa Rashidi, Zhe Jiang
The 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025)
Modeling irregularly timed clinical events from EHRs is challenging due to time irregularity, cyclical patterns, and multi-scale event interactions. We propose XTSFormer, a transformer-based model with cycle-aware time encoding and multi-scale attention to address these challenges.
XTSFormer: Cross-Temporal-Scale Transformer for Irregular-Time Event Prediction in Clinical Applications
Tingsong Xiao, Zelin Xu, Wenchong He, Zhengkun Xiao, Yupu Zhang, Zibo Liu, Shigang Chen, My T. Thai, Jiang Bian, Parisa Rashidi, Zhe Jiang
The 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025)
Modeling irregularly timed clinical events from EHRs is challenging due to time irregularity, cyclical patterns, and multi-scale event interactions. We propose XTSFormer, a transformer-based model with cycle-aware time encoding and multi-scale attention to address these challenges.
CoastalBench: A Decade-Long High-Resolution Dataset to Emulate Complex Coastal Processes
Zelin Xu, Yupu Zhang, Tingsong Xiao, Maitane Olabarrieta Lizaso, Jose M Gonzalez-Ondina, Zibo Liu, Shigang Chen, Zhe Jiang
The 42nd International Conference on Machine Learning (ICML 2025)
We introduce a decade-long, high-resolution (<100m) coastal circulation modeling dataset in southwest Florida with ~6 million cells. The dataset contains key oceanography variables and external forcings. We evaluate a customized Vision Transformer model for predicting ocean variables at varying lead times, providing a benchmark for deep learning models in high-resolution coastal simulations.
CoastalBench: A Decade-Long High-Resolution Dataset to Emulate Complex Coastal Processes
Zelin Xu, Yupu Zhang, Tingsong Xiao, Maitane Olabarrieta Lizaso, Jose M Gonzalez-Ondina, Zibo Liu, Shigang Chen, Zhe Jiang
The 42nd International Conference on Machine Learning (ICML 2025)
We introduce a decade-long, high-resolution (<100m) coastal circulation modeling dataset in southwest Florida with ~6 million cells. The dataset contains key oceanography variables and external forcings. We evaluate a customized Vision Transformer model for predicting ocean variables at varying lead times, providing a benchmark for deep learning models in high-resolution coastal simulations.
DecoyDB: A Dataset for Graph Contrastive Learning in Protein-Ligand Binding Affinity Prediction
Yupu Zhang, Zelin Xu, Tingsong Xiao, Gustavo Seabra, Yanjun Li, Chenglong Li, Zhe Jiang
The 39th Annual Conference on Neural Information Processing Systems (NeurIPS 2025)
We propose DecoyDB, a large-scale, structure-aware dataset for self-supervised graph contrastive learning on protein–ligand complexes. DecoyDB consists of high-resolution ground truth complexes and diverse decoy structures with RMSD annotations. We design a customized GCL framework to pretrain graph neural networks and demonstrate superior accuracy, sample efficiency, and generalizability.
DecoyDB: A Dataset for Graph Contrastive Learning in Protein-Ligand Binding Affinity Prediction
Yupu Zhang, Zelin Xu, Tingsong Xiao, Gustavo Seabra, Yanjun Li, Chenglong Li, Zhe Jiang
The 39th Annual Conference on Neural Information Processing Systems (NeurIPS 2025)
We propose DecoyDB, a large-scale, structure-aware dataset for self-supervised graph contrastive learning on protein–ligand complexes. DecoyDB consists of high-resolution ground truth complexes and diverse decoy structures with RMSD annotations. We design a customized GCL framework to pretrain graph neural networks and demonstrate superior accuracy, sample efficiency, and generalizability.
Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagery
Zelin Xu, Tingsong Xiao, Wenchong He, Yu Wang, Zhe Jiang
The 31st ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2023) Best Paper Award
This paper proposes a Spatial Knowledge-Infused Hierarchical Learning (SKI-HL) framework for Earth imagery with limited training labels, addressing challenges like sparse input labels, spatial uncertainty, and high computational costs. SKI-HL iteratively infers labels within a multi-resolution hierarchy using uncertainty-aware modules for selective label inference and neural network training. Experiments on flood mapping datasets demonstrate superior performance compared to baseline methods.
Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagery
Zelin Xu, Tingsong Xiao, Wenchong He, Yu Wang, Zhe Jiang
The 31st ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2023) Best Paper Award
This paper proposes a Spatial Knowledge-Infused Hierarchical Learning (SKI-HL) framework for Earth imagery with limited training labels, addressing challenges like sparse input labels, spatial uncertainty, and high computational costs. SKI-HL iteratively infers labels within a multi-resolution hierarchy using uncertainty-aware modules for selective label inference and neural network training. Experiments on flood mapping datasets demonstrate superior performance compared to baseline methods.
Dual-Graph Learning Convolutional Networks for Interpretable Alzheimer’s Disease Diagnosis
Tingsong Xiao, Lu Zeng, Xiaoshuang Shi, Xiaofeng Zhu, Guorong Wu
The 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022) Early Accept & Oral
This paper proposes a dual-graph learning convolutional network (dGLCN) for interpretable Alzheimer’s disease diagnosis by jointly learning subject and feature graphs within a GCN framework. By iteratively updating these graphs, dGLCN enhances interpretability in both subjects and brain regions while improving generalizability despite limited or noisy data. Experiments on ADNI datasets demonstrate that dGLCN outperforms comparison methods in binary classification tasks.
Dual-Graph Learning Convolutional Networks for Interpretable Alzheimer’s Disease Diagnosis
Tingsong Xiao, Lu Zeng, Xiaoshuang Shi, Xiaofeng Zhu, Guorong Wu
The 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022) Early Accept & Oral
This paper proposes a dual-graph learning convolutional network (dGLCN) for interpretable Alzheimer’s disease diagnosis by jointly learning subject and feature graphs within a GCN framework. By iteratively updating these graphs, dGLCN enhances interpretability in both subjects and brain regions while improving generalizability despite limited or noisy data. Experiments on ADNI datasets demonstrate that dGLCN outperforms comparison methods in binary classification tasks.
Education
University of Florida
University of Florida
University of Electronic Science and Technology of China
Honors & Awards
- 2025
- 2024
- 2023
- 2023, 2025, 2026
- 2022
Recent News
Academic Service
Reviewer and program committee member for conferences including ICLR, ICML, NeurIPS, AAAI, and KDD, and journals including IEEE TPAMI and TKDE.
Program Committee
- Association for the Advancement of Artificial Intelligence (AAAI) (2024-2026)
- Conference on Neural Information Processing Systems (NeurIPS) (2023-2026)
- International Conference on Learning Representations (ICLR) (2022-2026)
- International Conference on Machine Learning (ICML) (2024-2026)
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2024-2026)
- American Medical Informatics Association Annual Symposium (AMIA) (2024-2025)
- International Conference on Artificial Intelligence and Statistics (AISTATS) (2025)
- International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2025)
- International Joint Conference on Neural Networks (IJCNN) (2025)
- European Conference on Artificial Intelligence (ECAI) (2025)
- International Conference on Intelligent Systems for Molecular Biology (ISMB) (2024)
- IEEE International Symposium on Biomedical Imaging (ISBI) (2023)
- IEEE International Conference on Data Mining (ICDM) (2023)
- International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) (2022)
Journal Reviewer
- IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
- ACM Transactions on Intelligent Systems and Technology (TIST)
- IEEE Transactions on Knowledge and Data Engineering (TKDE)
- IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
- IEEE Transactions on Image Processing (TIP)
- IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)
- IEEE Transactions on Computational Social Systems (TCSS)
- IEEE Transactions on Multimedia (TMM)
- Information Processing & Management (IP&M)
- Communications of the ACM (CACM)
- Journal of Medical Internet Research (JMIR)
- Journal of the International Measurement Confederation
- Future Generation Computer Systems
- Information Sciences
- Neural Networks
- Neural Processing Letters
- Pattern Recognition Letters