Academic Profile

Academic Profile

Assoc Prof Justin Dauwels

Associate Professor, School of Electrical & Electronic Engineering

Assoc Prof Justin Dauwels

Justin Dauwels is an Assistant Professor with School of Electrical & Electronic Engineering at Nanyang Technological University (NTU). His research interests are in Bayesian statistics, iterative signal processing, and computational neuroscience. He enjoys working on real-world problems, often in collaboration with medical practitioners. He also tries to bring real-world problems into the classroom.

Prior to joining NTU, Justin was a research scientist during 2008-2010 in the Stochastic Systems Group (SSG) at the Massachusetts Institute of Technology, led by Prof. Alan Willsky. He received postdoctoral training during 2006-2007 under the guidance of Prof. Shun-ichi Amari and Prof. Andrzej Cichocki at the RIKEN Brain Science Institute in Wako-shi, Japan.

He obtained a PhD degree in electrical engineering at the Swiss Polytechnical Institute of Technology (ETH) in Zurich in December 2005, supervised by Prof. Hans-Andrea Loeliger, and was a teaching and research assistant at the Signal and Information Processing Laboratory (ISI) of the Department of Information Technology and Electrical Engineering at ETH Zurich from 2000 to 2005. In 2000 he received the engineering physics degree from the University of Ghent. From 1999 to 2000, he was an exchange student at ETH, and completed his master's thesis at the Institute of Neuroinformatics in Zurich.

Justin was a visiting researcher at the MIT Media Lab (Physics and Media Group) in Fall 2003 and the University of Ghent (Digital Communications Research Group) in January 2004. In Spring 2004 he was an intern at the Mitsubishi Electric Research Lab (Cambridge, MA) under supervision of Dr. Jonathan Yedidia.

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).

He is a member of the IEEE and the IMS. He is a research affiliate with Stochastic Systems Group (SSG) at the Massachusetts Institute of Technology, the Neurology Department at Massachusetts General Hospital, and the RIKEN Brain Science Institute.
Research Interests
His research interests are in Bayesian statistics, iterative signal processing, and computational neuroscience.

Some of the projects include:
- Mathematical modeling of the start and ending of epileptic seizures
- Diagnosis of Alzheimer's disease from EEG signals
- Machine learning techniques for guiding neurosurgery
- Detection of mental states from EEG signals
- Tracking and predicting traffic in dynamic urban networks
- Data-driven dynamical models of human behavior
- Tracking and control of synthetic cell tissue
- Copula-based modeling of extreme events
- Copula-based graphical models
Current Projects
  • Advanced Multi-Sensor Anomaly Monitoring and Analytics for Gas Pipeline
  • Automated Seizure Detection from Neurology ICU Patient’s Long-term EEG Recordings
  • Automatic Analysis of EEG for Neurological Patients by Deep Learning Methods
  • BeingTogether
  • CETRAN Autonomous Vehicle Test Centre
  • Cognitive team theoretic approach for Dynamic Airspace Management (CDAM)
  • Development Of A Real-Time Machine Learning Approach ToImprove Surgical Outcomes During Deep Brain Stimulation OfThe Subthalamic Nucleus In Parkinson Diseases
  • Development of Framework for Functional Safety and Performance Evaluation of Avs
  • Energy Intensity Study
  • Evaluating the Clinical Utility of Speech and Motor Characteristics in Psychiatry
  • Hierarchical Bayesian Models for Time-Lapse Images of Cell Migration
  • How language mixes contribute to effective bilingualism and biliteracy in Singapore
  • Language and Bilingualism (Infancy)
  • Machine Learning Based Monitoring System for Anomaly Detection In Autonomous Vehicle
  • Multimodal Brain Imaging and Network Inference to Capture Changes Due to Normal Ageing and Disease
  • Neurotechnologies for the Next Decade
  • Probabilistic Routing Based on Travel Time Distributions
  • Stochastic Optimization for Sparse Inference: Frequentist and Bayesian Methods
  • Towards Socio-And Neuro-Feedback Treatment for Schizophrenia
  • Uncovering the science of collaborative learning through audio and video anlytics in team-based learning
  • Utilization of Virtual Singapore for validation and verification of Autonomous vehicle technology and services
Selected Publications
  • Chang CC, Shi WY, P Mehta, J. Dauwels. (2019). Life cycle energy assessment of university buildings in tropical climate. Journal of Cleaner Production , (239).
  • Chua, Y. H. V., Rajalingam, P., Tan, S. C., Dauwels, J. (2019). EduBrowser: A multimodal automated monitoring system for co-located collaborative learning. 8th International conference on Learning Technology for Education Challenges (LTEC 2019).
  • Chua, Y. H. V., Dauwels, J., Tan, S. C. (2019). Proceedings of 9th International Conference on Learning Analytics and Knowledge (LAK19): Technologies for automated analysis of co-located, real-life, physical learning spaces: Where are we now?. 9th International Conference on Learning Analytics and Knowledge (LAK19) (pp. 11 - 20)Tempe, AZ, USA: ACM New York.
  • Nagacharan Teja Tangirala, Anuj Abraham, Apratim Choudhury, Pranjal Vyas, Rongkai Zhang and Justin Dauwels. (2019). 2018 IEEE Symposium Series on Computational Intelligence (SSCI): Analysis of Packet drops and Channel Crowding in Vehicle Platooning using V2X communication. 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 281-286)Bengaluru, India: IEEE.
  • Chua YHV, Dauwels J, Tan SC. (2018, December). Review of Technologies for Automated Analysis of Co-located, Real-Life, Physical Learning Environments. Paper presented at International Conference on Teaching, Assessment and Learning for Engineering 2018 (IEEE TALE2018), Wollongong, Australia.

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