@article{lu2025tbanile26,title={TBA},author={Lu, Qingjie and TBA},journal={Submitted to OSDI: USENIX Symposium on Operating Systems Design and Implementation},year={2026},note={Under Review}}
OSDI (under review)
TBA
Qingjie
Lu, and
TBA
Submitted to OSDI: USENIX Symposium on Operating Systems Design and Implementation, 2026
@article{lu2025tbameta26,title={TBA},author={Lu, Qingjie and TBA},journal={Submitted to OSDI: USENIX Symposium on Operating Systems Design and Implementation},year={2026},note={Under Review}}
PETS (under review)
TBA
Qingjie
Lu, and
TBA
Submitted to PETS: Privacy Enhancing Technologies Symposium, 2026
@article{lu2025tbanines26,title={TBA},author={Lu, Qingjie and TBA},journal={Submitted to New Ideas in Networked Systems: NiNES},year={2026},note={Under Review}}
2025
HotNets ’25
Modeling Metastability
Ali
Farahbakhsh, Andreas
Haeberlen, Qingjie
Lu, and
3 more authors
In Proceedings of the 24th ACM Workshop on Hot Topics in Networks (HotNets ’25), 2025
Recently, there has been increasing concern about a new failure mode in data-center systems: when there is an external shock, such as a sudden load spike or some machine failures, systems will sometimes respond with reduced throughput - but, in contrast to a traditional overload situation, the throughput does not recover once the external shock disappears, and remains permanently degraded. This phenomenon has been called a metastable failure. In this paper, we sketch a simple model that could help to explain how and why metastability arises. We also show how our model can be used to predict the presence or absence of metastable states in a given system.
@inproceedings{lu2025modeling,title={Modeling Metastability},author={Farahbakhsh, Ali and Haeberlen, Andreas and Lu, Qingjie and Alvisi, Lorenzo and van Renesse, Robbert and Gahtan, Shir Cohen},booktitle={Proceedings of the 24th ACM Workshop on Hot Topics in Networks (HotNets '25)},year={2025},doi={10.1145/3772356.3772426},note={<b>Author Names in Alphabetic Order</b>}}
2022
ISET
Neural Network-Based Approaches for Aspect-Based Sentiment Analysis
Qingjie
Lu
Highlights in Science, Engineering and Technology, 2022
Proceedings of the 4th International Conference on Information Science and Electronic Technology
The research of Aspect-based Sentiment Analysis which is a process that has a more specific focus than general sentiment analysis is trending upwards in numbers. Stemming from Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), novel approaches introduced new components like Graph Convolutional Networks (GCNs) and Transformers that improved the overall accuracy dramatically. Along with summarizing the models, the focus of this survey will be on comparing the several novel methods. Although this paper found that Dependency graph enhanced dual-transformer network (DGEDT) coupled with Bidirectional Encoder Representations from Transformers (BERT) is the best performing model thus far, this paper also identified challenges that needed to be addressed in order to better evaluate current and future models.
@article{lu2022aspectbased,title={Neural Network-Based Approaches for Aspect-Based Sentiment Analysis},author={Lu, Qingjie},journal={Highlights in Science, Engineering and Technology},volume={12},pages={222--229},year={2022},doi={10.54097/hset.v12i.1457},url={https://drpress.org/ojs/index.php/HSET/article/view/1457},note={Proceedings of the 4th International Conference on Information Science and Electronic Technology}}