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Current research (Theoretical)

1) Supervised machine learning 

   a) Deep learning

   b) Bayesian inference.

   c) Support vector machines.

Current research (Applications)

1) Driver behavior modeling.

2) Road risk assessment. 

3) Naturalistic driving data mining.



Previous research 

1) Adaptive signal processing 

2) Nonlinear behavior modeling.

3) Predistortion design techniques for RF power amplifiers. 

4) Spectrum sensing in cognitive radio networks.

Research projects

1) Robust Crowdsensing for Intelligent Road Services (2016).

2) Digital Predistortion of Broadband Transmitters for LTE-Advanced Applications", in collaboration with University of Bristol, UK, and University of Calgary, Canada (2013).


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