Dan Li

Orcid: 0000-0002-3787-1673

Affiliations:
  • National University of Singapore, Institute of Data Science, Singapore
  • Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore (PhD 2017)


According to our database1, Dan Li authored at least 24 papers between 2015 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2025
Soft label enhanced graph neural network under heterophily.
Knowl. Based Syst., 2025

2024
FD-LLM: Large Language Model for Fault Diagnosis of Machines.
CoRR, 2024

eWAPA: An eBPF-based WASI Performance Analysis Framework for WebAssembly Runtimes.
CoRR, 2024

Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs.
Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, 2024

GLA-DA: Global-Local Alignment Domain Adaptation for Multivariate Time Series.
Proceedings of the Database Systems for Advanced Applications, 2024

eWAPA: An eBPF-based WASI Performance Analysis Framework for Web Assembly Runtimes.
Proceedings of the IEEE International Conference on Software Services Engineering, 2024

2023
Adversarial Maritime Trajectory Prediction with Real-time Spatial-Temporal Mutual Influence.
Proceedings of the IEEE International Conference on Data Mining, 2023

Fault Detection and Diagnosis of Air Handling Units Based On Adversarial Multi-task Learning.
Proceedings of the IEEE International Conference on Data Mining, 2023

CB-GAN: Generate Sensitive Data with a Convolutional Bidirectional Generative Adversarial Networks.
Proceedings of the Database Systems for Advanced Applications, 2023

MAD-SGS: Multivariate Anomaly Detection with Multi-scale Self-learned Graph Structures.
Proceedings of the Bio-Inspired Computing: Theories and Applications, 2023

2022
MAD-SGCN: Multivariate Anomaly Detection with Self-learning Graph Convolutional Networks.
Proceedings of the 38th IEEE International Conference on Data Engineering, 2022

2021
Electrocardiogram Classification and Visual Diagnosis of Atrial Fibrillation with DenseECG.
CoRR, 2021

2020
Handling Incomplete Sensor Measurements in Fault Detection and Diagnosis for Building HVAC Systems.
IEEE Trans Autom. Sci. Eng., 2020

VC-GAN: Classifying Vessel Types by Maritime Trajectories using Generative Adversarial Networks.
Proceedings of the 32nd IEEE International Conference on Tools with Artificial Intelligence, 2020

2019
Identifying Unseen Faults for Smart Buildings by Incorporating Expert Knowledge With Data.
IEEE Trans Autom. Sci. Eng., 2019

Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks.
Proceedings of the 2019 IEEE International Conference on Prognostics and Health Management, 2019

A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection.
Proceedings of the 2019 IEEE International Conference on Prognostics and Health Management, 2019

MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019: Text and Time Series, 2019

2018
Design Automation for Smart Building Systems.
Proc. IEEE, 2018

Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series.
CoRR, 2018

2017
Optimal Sensor Configuration and Feature Selection for AHU Fault Detection and Diagnosis.
IEEE Trans. Ind. Informatics, 2017

2016
Optimal Training and Efficient Model Selection for Parameterized Large Margin Learning.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2016

Fusing system configuration information for building cooling plant Fault Detection and severity level identification.
Proceedings of the IEEE International Conference on Automation Science and Engineering, 2016

2015
Learning Optimization Friendly Comfort Model for HVAC Model Predictive Control.
Proceedings of the IEEE International Conference on Data Mining Workshop, 2015


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