Tung Kieu
💻 Assistant Professor
🏛 Department of Computer Science
The Technical Faculty of IT and Design
Aalborg University
🚪 Office 3.2.03
Selma Lagerlöfs Vej 300
DK-9220, Aalborg Øst, Denmark
📧 tungkvt at cs dot aau dot dk
📧 kvttung at gmail dot com
| Profile | Research | Education | Work | Publication | Preprint | Teaching | Collaboration | Students | Services |
Profile
I am an Assistant Professor in Department of Computer Science at Aalborg University. I am a faculty member in Center for Data-Intensive Systems. I am also affiliated with the department's team on Artificial Intelligence and Machine Learning and the university's Center on AI for the People.
I obtained my Ph.D. degree from Aalborg University in April 2021 under supervision of Prof. Christian S. Jensen and Prof. Bin Yang. During April 2021 to May 2021, I was a Research Assistant at Aalborg University. During June 2021 to August 2021, I was a Postdoctoral Fellow at Aalborg University. All the time, I have worked in the Data-Intensive Systems (Daisy) group headed by Prof. Christian S. Jensen.
Research Interests
Data mining and machine learning, in particular, on spatio-temporal data, time series, graphs, and uncertain data.
Education
- B.Sc. in Computer Science, University of Science, Vietnam National University, Ho Chi Minh City, Vietnam.
- B.Eng. in Civil Engineering, University of Architecture, Ho Chi Minh City, Vietnam.
- M.Sc. in Computer Science, University of Science, Vietnam National University, Ho Chi Minh City, Vietnam.
- Ph.D. in Computer Science, Aalborg University, Aalborg, Denmark.
Work Experience
- Data Entry Operator at FTA Research & Consultant, Ho Chi Minh City, Vietnam.
- Web Developer at BeRich.vn, Ho Chi Minh City, Vietnam.
- Data Warehouse Specialist at FPT, Ho Chi Minh City, Vietnam.
- Database Specialist at FPT, Ho Chi Minh City, Vietnam.
- Researcher at Aalborg University, Aalborg, Denmark.
- Assistant Professor at Aalborg University, Aalborg, Denmark.
Publication
- Huy Le, Tung Kieu, Anh Nguyen, and Ngan Le. WAVER: Writing-style Agnostic Video Retrieval via Distilling Vision-Language Models Through Open-Vocabulary Knowledge [pdf].
ICASSP 2024 - David Campos, Miao Zhang, Bin Yang, Tung Kieu, Chenjuan Guo, and Christian S. Jensen. LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation [pdf].
SIGMOD 2023 - Razvan-Gabriel Cirstea, Tung Kieu, Chenjuan Guo, Xuanyi Dong, Shirui Pan, and Bin Yang. Triformer: Triangular, Variable-Specific Attention for Long Sequence Multivariate Time Series Forecasting [pdf].
IJCAI 2022 - Razvan-Gabriel Cirstea, Tung Kieu, Chenjuan Guo, Shirui Pan, and Bin Yang. Towards Spatio-Temporal Aware Traffic Time Series Forecasting [pdf].
ICDE 2022 - Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen, Yan Zhao, Feiteng Huang, and Kai Zheng. Robust and Explainable Autoencoders for Time Series Outlier Detection [pdf].
ICDE 2022 - Tung Kieu, Bin Yang, Chenjuan Guo, Razvan-Gabriel Cirstea, Yan Zhao, Yale Song, and Christian S. Jensen. Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders [pdf].
ICDE 2022 - Yan Zhao, Xuanhao Chen, Liwei Deng, Tung Kieu, Chenjuan Guo, Bin Yang, Kai Zheng, and Christian S. Jensen. Outlier Detection for Streaming Task Assignment in Crowdsourcing [pdf].
WWW 2022 - David Campos, Tung Kieu, Chenjuan Guo, Feiteng Huang, Kai Zheng, Bin Yang, and Christian S. Jensen. Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles [pdf].
VLDB 2022 - Razvan-Gabriel Cirstea, Tung Kieu, Chenjuan Guo, Bin Yang, and Sinno Jialin Pan. EnhanceNet: Plugin Neural Networks for Enhancing Correlated Time Series Forecasting [pdf].
ICDE 2021 - Tung Kieu, Bin Yang, Chenjuan Guo, and Christian S. Jensen. Outlier Detection for Time Series with Recurrent Autoencoder Ensembles [pdf].
IJCAI 2019 - Tung Kieu, Bin Yang, Chenjuan Guo, and Christian S. Jensen. Distinguishing Trajectories from Different Drivers using Incompletely Labeled Trajectories [pdf].
CIKM 2018 - Tung Kieu, Bin Yang, and Christian S. Jensen. Outlier Detection for Multidimensional Time Series Using Deep Neural Networks [pdf].
MDM 2018 - Tung Kieu, Bay Vo, Tuong Le, Zhi-Hong Deng, and Bac Le. Mining Top-k Co-occurrence Items with Sequential Pattern [pdf].
Expert Syst. Appl. 85 2017
Preprint
- Huy Le, Tung Kieu, Anh Nguyen, and Ngan Le. WAVER: Writing-style Agnostic Video Retrieval via Distilling Vision-Language Models Through Open-Vocabulary Knowledge [pdf].
- David Campos, Miao Zhang, Bin Yang, Tung Kieu, Chenjuan Guo, and Christian S. Jensen. LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation--Extended Version [pdf].
- Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen, Yan Zhao, Feiteng Huang, and Kai Zheng. Robust and Explainable Autoencoders for Time Series Outlier Detection--Extended Version [pdf].
- Razvan-Gabriel Cirstea, Tung Kieu, Chenjuan Guo, Xuanyi Dong, Shirui Pan, and Bin Yang. Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Extended Version [pdf].
- Razvan-Gabriel Cirstea, Tung Kieu, Chenjuan Guo, Shirui Pan, and Bin Yang. Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Extended Version [pdf].
- David Campos, Tung Kieu, Chenjuan Guo, Feiteng Huang, Kai Zheng, Bin Yang, and Christian S. Jensen. Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles--Extended Version [pdf].
- Yan Zhao, Liwei Deng, Xuanhao Chen, Chenjuan Guo, Bin Yang, Tung Kieu, Feiteng Huang, Torben Bach Pedersen, Kai Zheng, and Christian S. Jensen. A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis [pdf].
Teaching
Courses
- Advanced Algorithm (shared with Prof. Bin Yang and Prof. Chenjuan Guo).
- Algorithms and Computability (shared with Prof. Bin Yang, Prof. Dalin Zhang, and Prof. Alvaro Torralba).
- Algorithms and Satisfiability (shared with Prof. Bin Yang, Prof. Dalin Zhang, and Prof. Alvaro Torralba).
Supervision
- Software 5 (SW5).
- Software 6 (SW6).
- Specialization course in Database Technology (SpDT).
- Master Thesis (shared with Prof. Bin Yang).
Teaching Assistant
- Algorithms and Data Structures (shared with Sean Bin Yang).
- Advanced Algorithm (shared with Sean Bin Yang).
- Algorithms and Computability (shared with Jákup Odssonur Svöðstein).
- Algorithms and Satisfiability (shared with Jákup Odssonur Svöðstein).
Collaboration
- Collaborating with TADAA! (in aSTEP project).
- Collaborating with the BioX (in aSTEP project).
- Collaborating with Huawei (in Huawei project)
Students
M.Sc.
- David Campos (co-supervised with Prof. Bin Yang)
- Mik Christensen (co-supervised with Prof. Bin Yang)
Ph.D.
- David Campos (co-supervised with Prof. Bin Yang)
Scientific Services
Conferences
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD)
- Conference on Neural Information Processing Systems (NEURIPS)
- International Joint Conference on Artificial Intelligence (IJCAI)
- AAAI Conference on Artificial Intelligence (AAAI)
- International Conference on Very Large Data Bases (VLDB)
- IEEE International Conference on Data Engineering (ICDE)
- ACM International Conference on Information and Knowledge Management (CIKM)
- ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL/GIS)
- International Conference on Advanced Data Mining and Applications (ADMA)
- APWeb-WAIM International Joint Conference on Web and Big Data (APWEB-WAIM)
- IEEE International Conference on Mobile Data Management (MDM)
Journals
- Journal of Artificial Intelligence Research (JAIR)
- ACM Transactions on Information Systems (TOIS)
- IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
- IEEE Transactions on Knowledge and Data Engineering (TKDE)
- IEEE Transactions on Intelligent Transportation Systems (TITS)
- IEEE Transactions on Network Science and Engineering (TNSE)
- IEEE Transactions on Industrial Informatics (TII)
- Data Mining and Knowledge Discovery (DMKD)
- Pattern Recognition
- Neural Networks
- Machine Learning
- Expert Systems with Applications
- Neural Processing Letters
- IEEE Access