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, Distributed autonomous sensing systems, Machine learning for image and video understanding, Signal processing for communication

Biography

Dr. Justin Dauwels is an Associate Professor at the TU Delft (Signals Processing Systems GroupDepartment of Microelectronics), and serves as co-Director of the Safety and Security Institute at the TU Delft. He was an Associate Professor of the School of Electrical and Electronic Engineering at the Nanyang Technological University (NTU) in Singapore till the end of 2020. At the TU Delft, he serves as scientific lead of the Model-Driven Decisions Lab (MoDDL), a first lab for the Knowledge Building program between the police and the TU Delft. He also serves as Chairperson of the EE Board of Studies at the TU Delft, and is a board member of the Co van Ledden Hulsebosch Center (Netherlands Center for Forensic Science and Medicine).

His research interests are in machine learning and generative AI with applications to autonomous systems, and analysis of human behavior and physiology.

He obtained his PhD degree in electrical engineering at the Swiss Polytechnical Institute of Technology (ETH) in Zurich in December 2005. Moreover, he was a postdoctoral fellow at the RIKEN Brain Science Institute (2006-2007) and a research scientist at the Massachusetts Institute of Technology (2008-2010).

He has been elected as IEEE SPS 2024 - 2025 Distinguished Lecturer. He served as Chairman of the IEEE CIS Chapter in Singapore from 2018 to 2020, and served as Associate Editor of the IEEE Transactions on Signal Processing (2018 - 2023), and serves currently as Associate Editor (2021-2023) and Subject Editor (since 2023) of the Elsevier journal Signal Processing, Area Editor C&F for the IEEE Signal Processing Magazine (since 2023), member of the Editorial Advisory Board of the International Journal of Neural Systems (since 2021), and organizer of IEEE conferences and special sessions. He was also Elected Member of the IEEE Signal Processing Theory and Methods Technical Committee and IEEE Biomedical Signal Processing Technical Committee (both in 2018-2023), and is currently Elected Member of the IEEE Machine Learning for Signal Processing Technical Committee and the IEEE Emerging Transportation Technology Testing (ET3) Technical Committee. He has been a JSPS postdoctoral fellow (2007), a BAEF fellow (2008), a Henri-Benedictus Fellow of the King Baudouin Foundation (2008), and a JSPS invited fellow (2010, 2011). His research team has won several best paper awards at international conferences and journals.

His research on intelligent transportation systems has been featured by the BBC, Channel 5, national newspapers (Straits Times, Lianhe Zaobao, and others), and numerous technology websites. Besides his academic efforts, the team of Dr. Justin Dauwels also collaborates intensely with local start-ups, SMEs, and agencies, in addition to MNCs, in the field of data-driven transportation, logistics, and medical data analytics. His academic lab has spawned four startups across a range of industries, ranging from AI for healthcare to autonomous vehicles.

EE2G1 Electrical Engineering for the Next Generation

BSc 2nd year project

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: 26 Jan 2025