|Academic Profile |
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Assoc Prof Justin Dauwels
School of Electrical & Electronic Engineering
College of Engineering
Phone: (+65)6790 5410
- PhD (ElectEng) Swiss Federal Institute of Technology 2006
- Dip (EngPhysics) (GR DI) Ghent University 2000
|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.
|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
- 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
- Clinical Development and Validation of Automated EEG Data Interpretation System
- 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
- 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
- Development of a Real-time Machine Learning Approach to Improve Surgical Outcomes During Deep Brain Stimulation of The Subthalamic Nucleus In Parkinson 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)
- 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
- Robust Real-Time Routing in Tranpsportation Networks
- Stochastic Optimization for Sparse Inference: Frequentist and Bayesian Methods
- Technologies for 21st Century Learning and Education
- Towards Socio-And Neuro-Feedback Treatment for Schizophrenia
- Utilization of Virtual Singapore for validation and verification of Autonomous vehicle technology and services
- S Ho, SC Nagavarapu, RR Pandi, J Dauwels. (2018). Improved Tabu Search Heuristics for Static Dial-A-Ride Problem: Faster and Better Convergence. 25th ITS World Congress.
- Subhasree Basu, Yi Han Victoria Chua, Wanyu Geraldine Lim, Tomasz Maszczyk, Lingxi Xiao, Justin Dauwels. (2018). Predicting insider threat-related behaviour from live inner state data using machine learning. Singapore Conference on Applied Psychology Proceedings.
- Anusha James, Victoria Yi Han Chua, Tomasz Maszczyk, Justin Dauwels, Rebecca Bull, Kerry Lee, Ana Moreno-Núñez. (2018). Automated Classification of Classroom Climate by Audio Analysis. IWSDS Conference.
- Yasir Tahir, Justin Dauwels, Daniel Thalmann, Nadia Magnenat Thalmann. (2018). A User Study of a Humanoid Robot as a Social Mediator for Two-Person Conversations. International Journal of Social Robotics, .
- Soumya Dasgupta, Varunkumar Raghuraman, Apratim Choudhury, Nagacharan Teja Tangirala, Justin Dauwels. (2018). 2017 IEEE Symposium Series on Computational Intelligence (SSCI): Merging and splitting maneuver of platoons by means of a novel PID controller. IEEE Symposium Series on Computational Intelligence (pp. Page no. 2574)Hawaii, USA: IEEE.