Academic Profile : No longer with NTU

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Assoc Prof Justin Dauwels
Associate Professor, School of Electrical & Electronic Engineering
Deputy Director, ST Engineering-NTU Corporate Laboratory
 
Justin Dauwels is an Associate 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
 
  • How Language Mixes Contribute To Effective Bilingualism And Biliteracy In Singapore
US 2013/0202151 A1: Methods And Apparatus For Recovering Phase And Amplitude From Intensity Images (2015)
Abstract: An intensity image is collected at each of a plurality of locations spaced apart in a propagation direction of a light beam. Information from the intensity images is combined using a Kalman filter which assumes that at least one co-variance matrix has a diagonal form. This leads to considerable reduction in computational complexity. An augmented Kalman filter model (augmented space state model) is used in place of the standard Kalman filter model. The augmented Kalman filter improves the robustness to noise.