dr.ir. J. Dauwels

Associate Professor
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 communication


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

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