Nguyen Duc Thien


PhD student
Department of Information and Communications Engineering, Tokyo Institute of Technology

Research

Signal processing, machine learning, data analytics

Education

Ph.D. (April 2024 -- Now)
Tokyo Institute of Technology, Japan
Advisor: Prof. Konstantinos Slavakis [website]

MEng. (April 2022 -- March 2024)
Tokyo Institute of Technology, Japan
Advisor: Prof. Konstantinos Slavakis [website]

BEng. (April 2018 -- March 2022)
Tokyo Institute of Technology, Japan
Thesis: Multi-city spatial projection of a dengue vector’s suitability considering urbanization and climate change
Advisor: Prof. Alvin C.G. Varquez [website]

Undergraduate (August 2017 -- March 2018)
University of Engineering and Technology, Vietnam National University, Ha Noi, Viet Nam
Computer Science

Work Experience

[Tensor Learning Team], RIKEN AIP (April 2024 -- Now)
Junior Research Associate, Part-time, Tokyo, Japan

FPT Japan (April 2022 -- March 2024)
Machine Learning Engineer, Part-time, Tokyo, Japan

Rakuten Travel (September -- November 2021)
Software Engineer, Internship, Tokyo, Japan

Visual Alpha (April 2020 -- April 2022)
Software Developer, Part-time, Tokyo, Japan

Publications

Peer-reviewed conferences

  1. Duc Thien Nguyen and Konstantinos Slavakis. Multilinear kernel regression and imputation via manifold learning. IEEE Open Journal of Signal Processing, vol. 5, pp. 1073-1088, 2024.

  2. Duc Thien Nguyen and Konstantinos Slavakis. Multilinear kernel regression and imputation via manifold learning: The dynamic MRI case. ICASSP 2024, pp. 9466-9470, Seoul, Korea, 14-19 April, 2024.

  3. Duc Thien Nguyen*, Manh Duc Tuan Nguyen*, Truong Son Hy*, and Risi Kondor. Fast Temporal Wavelet Graph Neural Networks. NeurIPS 2023 (NeurReps Workshop).

Non-peer-reviewed conferences

  1. Duc Thien Nguyen and Konstantinos Slavakis. Imputation of Time-varying Edge Flows in Graphs by Multilinear Kernel Regression and Manifold Learning. SIP 2024.

  2. Duc Thien Nguyen and Konstantinos Slavakis. Multi-linear kernel regression and imputation in data manifolds. SIP 2023.