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, Distributed autonomous sensing systems, Machine learning for image and video understanding, Signal processing for communicationBiography
Dr. Justin Dauwels is an Associate Professor at the TU Delft (Signals Processing Systems Group, Department 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

Justin Dauwels
PhD students
MSc students
- Hendrik Bosma
- Judith Essenburg
- Thomas Evers
- Zhixuan Ge
- Jiarui Zhou
- Xavier Morales Rivero
- Zhewen Gao
- Varun Sarathchandran
- Joris Van de Weg
- Giacomo Zanardini
Alumni
- Christos Spiliadis (2025)
- Paul Van der Kleij (2025)
- Arjan Pater (2024)
- Konstantinos Andriopoulos (2024)
- 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
- Instrument-based measurements of movement problems for detection and prediction the risk of psychiatric disorders
- 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