dr.ir. J. Dauwels
Signal Processing Systems (SPS), Department of Microelectronics
Expertise: Machine learning, with applications to autonomous vehicles and biomedical signal processing
Themes: Biomedical signal processing/wavefield imaging, Distributed autonomous sensing systems, Machine learning for image and video understanding, Signal processing for communicationBiography
Justin Dauwels starts in January 2021 as Associate Professor at TU Delft. Prior to this, he was Associate Professor with the School of Electrical & Electronic Engineering at Nanyang Technological University (NTU), Singapore.
He obtained a PhD degree in electrical engineering at the Swiss Polytechnical Institute of Technology (ETH) in Zurich in December 2005. Next, in 2006-2007 he was a postdoc at the RIKEN Brain Science Institute, Japan (Prof. Shun-ichi Amari and Prof. Andrzej Cichocki), and a research scientist during 2008-2010 in the Stochastic Systems Group (SSG)at the Massachusetts Institute of Technology (MIT), led by Prof. Alan Willsky. He joined NTU in 2010.
His research interests are in Bayesian statistics, iterative signal processing, and computational neuroscience.
EE4685 Machine learning, a Bayesian perspective
Mathematical foundation for machine learning algorithms, presented from a statistical (Bayesian) and optimization point of view.
EE4C12 Machine learning for Electrical Engineering
Introduction at MSc level
ET4386 Estimation and detection
Basics of detection and estimation theory, as used in statistical signal processing, adaptive beamforming, speech enhancement, radar, telecommunication, localization, system identification, and elsewhere.
Atmospheric Turbulence Informed Machine Learning for Laser Satellite Communications
Physics-informed machine learning algorithms to formulate the optical link performance map
Reliable POwerDown for Industrial Drives
The pioneering EU research project R-PODID started on the 1st of September 2023. This KDT JU co-funded project aims to develop an automated, cloudless, short-term fault-prediction for electric drives, power modules, and power devices, that can be integrated into power converters.
Last updated: 29 Nov 2022
Justin Dauwels
PhD students
MSc students
- Thomas Evers
- Zhixuan Ge
- Jiarui Zhou
- Xavier Morales Rivero
- Zhewen Gao
- Varun Sarathchandran
- Joris Van de Weg
- Konstantinos Andriopoulos
- Giacomo Zanardini
- Arjan Pater
- Milla Rahmadiva
- Christos Spiliadis
Alumni
- Yash Mirwani (2024)
- Annefleur Kluft (2024)
- Zeineh Bou Cher (2024)
- Femke Leenen (2024)
- Junzhe Yin (2024)
- Ankush Roy (2024)
- Tamir Themans (2024)
- Yiheng Chang (2024)
- Zipeng Wang (2024)
- Jingwen Dun (2023)
- Doruk Barokas Profeta (2023)
- Shuoyan Zhao (2023)
- Sinian Li (2023)
- Alan Hamo (2023)
- Jonathan Dijkstra (2023)
- Luuk Bolhuis (2022)
- Chuhan Wang (2022)
- Haoran Bi (2022)
- Renjie Dai (2022)
- Enpu Chen (2022)
- Jinchen Zeng (2022)
- Zhiyi Wang (2022)
- Yanan Hu (2022)
- Yingfeng Jiang (2022)
- Qi Zhang (2022)
- Nan Lin (2022)
- Yuanyuan Yao (2022)
- Ruben Wijnands (2022)
- Pallas Koers (2022)
MSc project proposals
- Object-centric deep generative models
- Machine learning for laser satellite communications
- Machine learning for Optimizing Workflow in the Operating Room (MLOR)
- Automatic analysis of acoustic and semantic aspects of speech in psychiatric disorders
- Epilepsy diagnosis using multimodal machine learning
- Generative AI for suppressing tinnitus
- EEG-based Brain Signatures for Personalized Therapeutic Intervention in Chronic Pain
- Towards brain-inspired AI hardware
- Transformer-based World Models for continuous actions environments
- Reliable Powerdown for Industrial Drives (R-PODID)
- Nowcasting of Extreme Rainfall (NER)
- Modelling perception failures in autonomous vehicles