Yun Li

I am a Professor in the School of Computer Science at Nanjing University of Posts and Telecommunications, China. My research focuses on machine learning and data mining. Specifically, much of our work aims at feature selection, trustworthy (robust, fair and interpretable) artificial intelligence, time series forecasting and privacy preservation. Our work directly benefits applications such as computer vision, natural language process, software engineering, speech recognition, cloud computing and real-world robust learning systems.

<< Chinese Resume

LIDATA News

[2024/09/09] Our paper " Rethinking the Validity of Perturbation in Single-Step Adversarial Training" has been accepted for publication in Pattern Recognition.

[2024/09/07] Our paper " MNN: Mixed Nearest-Neighbors for Self-Supervised Learning" has been accepted for publication in Pattern Recognition.

[2023/09/03] Our paper “Enhancing Bug Localization through Bug Report Summarization” has been accepted for presentation in the International Conference on Data Mining (ICDM 2023).

[2023/08/01] Our paper "Attribution of Adversarial Attacks via Multi-Task Learning" has been accepted for presentation at the International Conference on Neural Information Processing (ICONIP 2023)

[2023/07/13] Our paper "Advancing Example Exploitation Can Alleviate Critical Challenges in Adversarial Training" has been accepted for presentation at the International Conference on Computer Vision (ICCV 2023)

[2023/06/22] Our paper "CSSBA: A Clean Label Sample-Specific Backdoor Attack" has been accepted for presentation at the International Conference on Image Processing (ICIP 2023).

[2023/06/01] Our paper "Trusted Detection for Parkinson's Disease based on Multi-type Speech Fusion" has been accepted for presentation at the International Conference on Systems, Man, and Cybernetics (SMC 2023).

[2023/04/08] Our paper "Decomposing Source Codes by Program Slicing for Bug Localization" has been accepted for presentation at the International Joint Conference on Neural Networks (IJCNN 2023).

[2023/04/04] Our paper “PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks” has been accepted by IEEE Transactions on Dependable and Secure Computing.

[2023/03/26] Our paper “PM²VE: Power Metering Model for Virtualization Environments in Cloud Data Centers” has been accepted by IEEE Transactions on Cloud Computing.

[2022/11/12] Our paper “BL-GAN: Semi-supervised Bug Localization via Generative Adversarial Network” has been accepted by IEEE Transactions on Knowledge and Data Engineering.

[2022/10/12] Our paper “Cost-Sensitive Tensor-Based Dual-stage Attention LSTM with Feature Selection for Data Center Server Power Forecasting” has been accepted by ACM Transactions on Intelligent Systems and Technology.

[2022/09/01] Our paper “Robustness May be at Odds with Stability in Adversarial Training based Feature Selection”has been accepted for publication in the International Conference on Data Mining (ICDM-22).

[2022/04/15] The data set about server energy is released.

[2022/04/10] Our paper “Enhancing bug localization with bug report decomposition and code hierarchical network” has been accepted for publication in Knowledge-Based Systems.

[2022/02/24] Our paper "Nested Named Entity Recognition: A Survey" has been accepted for publication in ACM Transactions on Knowledge Discovery from Data.

[2022/01/17] Our paper "Transferable Interpolated Adversarial Attack with Random-Layer Mixup " has been accepted for publication in the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-22).

[2022/01/06] Our paper "Data Characteristics Aware Prediction Model for Power Consumption of Data Center Servers", has been accepted for publication in Concurrency and Computation: Practice and Experience.

[2021/09/04] Our paper " (AD)^2: Adversarial Domain Adaptation to Defense with Adversarial Perturbation Removal", has been accepted for publication in Pattern Recognition.

[2021/04/21] Our paper "A Deep Multimodal Model for Bug Localization", has been accepted for publication in Data Mining and Knowledge Discovery.