Academic Profile

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Academic Profile

Assoc Prof Justin Dauwels


Associate Professor

School of Electrical & Electronic Engineering
College of Engineering

Email: JDAUWELS@NTU.EDU.SG
Phone: (+65)6790 5410
Office: S2.2-B2-15

Education
  • PhD (ElectEng) Swiss Federal Institute of Technology 2006
  • Dip (EngPhysics) (GR DI) Ghent University 2000
Biography
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
  • A Cloud Based Automatic EEG Data Interpretation System
  • Advanced Multi-Sensor Anomaly Monitoring and Analytics for Gas Pipeline
  • Automated Detection of Seizure-Spectrum Patterns and Quantitative Neurological Outcome Prediction
  • Automatic Analysis of EEG for Neurological Patients by Deep Learning Methods
  • Behavioural Analytics of Audio-Visual Cues Through Social Signal Processing
  • BeingTogether
  • Clinical Development and Validation of Automated EEG Data Interpretation System
  • Cognitive team theoretic approach for Dynamic Airspace Management (CDAM)
  • Cognitive team theoretic approach for Dynamic Airspace Management (CDAM)
  • Copula Graphical Models for Non-Gaussian Data: Algorithms, Theory, and Applications to Earth Sciences
  • Development of Framework for Functional Safety and Performance Evaluation of Avs
  • Development of NTU/ NXP- Intelligent Transport Systems Test-Bed
  • Development of Real-time Machine Learning Approach to Improve Surgical Outcomes During Deep Brain Simulation of the Subthalamic Nucleus in Pakinson Disease
  • Direct Contracting for the Development of Electronic ‘Online Violent Extremism Screening Tools’ (OVEST) System
  • 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)
  • MOSCATO
  • Machine Learning Based Monitoring System for Anomaly Detection In Autonomous Vehicle
  • Multi-Robot Optimization, Scheduling and Allocation
  • Multimodal Brain Imaging and Network Inference to Capture Changes Due to Normal Ageing and Disease
  • Robust Real-Time Routing in Tranpsportation Networks
  • Scene Understanding Through Bottom-up and Top-down Processing
  • Sensor Fusion Frameworks for Objects Classification, Terrain Classification and Localization
  • Technologies for 21st Century Learning and Education
  • Towards Socio-And Neuro-Feedback Treatment for Schizophrenia
Selected Publications
  • Michael Hoy, Justin Dauwels and Junsong Yuan. (2017). Efficient Tracking of Closely Spaced Objects in Depth Data using Sequential Dirichlet Process Clustering. 2017 IEEE Intelligent Vehicles Symposium.
  • Kang Dang and Micheal Hoy and Justin Dauwels and Junsong Yuan. (2017). Real-time Hierarchical Fusion System for Semantic Segmentation in Offroad Scenes. 20th International Conference on Information Fusion (FUSION).
  • J. Thomas, N. Sinha, N. Shaju, T. Maszczyk, J. Jin, S. Cash, J. Dauwels, B. Westover. (2017). Convolutional Neural Network-based Interictal Epileptiform Discharge Detection.
  • Moore, J. D. P., H. Yu, C.-H. Tang, T. Wang, S. Barbot, D. Peng, S. Masuti, J. Dauwels, Y. Hsu, V. Lambert, P. Nanjundiah, S. Wei, E. O. Lindsey, L. Feng, B. Shibazaki. (2017). Imaging the distribution of transient viscosity after the 2016 Mw 7.1 Kumamoto earthquake. Science, 356(6334), 163-167.
  • Hang Yu, Wayne Uy, and Justin Dauwels. (2017). Modeling Spatial Extremes via Ensemble-of-Trees of Pairwise Copulas. IEEE Transactions on Signal Processing, 65(3), 571--586.

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