People

Dr Haider Raza

Postdoctoral Research Fellow (IADS)
School of Computer Science and Electronic Engineering (CSEE)
Dr Haider Raza
  • Email

  • Telephone

    +44 (0) 1206 876145

  • Location

    PARKSIDE UNIT C2, Colchester Campus

Profile

Biography

I received the B.Tech. degree in Computer Science & Engineering from the Integral University, India, in 2008, the M.Tech. degree in Computer Engineering from the Manav Rachna International University, India, in 2011, and the PhD degree in computer science from University of Ulster University, Derry~Londonderry, U.K., in 2016. I worked as Postdoctoral Research Assistant at the University of Ulster in Neural Systems and Neuro-technology research team for EEG and MEG-based Brain-Computer Interfaces in 2016. Later, I joined as a Postdoctoral Research Officer in the Farr Institute of Health Informatics Research, Swansea University Medical School, U.K (2016-2017). From Nov-2017, I am working at the University of Essex.

Qualifications

  • PhD University of Ulster, (2016)

  • Master of Technology Manav Rachna International University, (2011)

  • Bachelor of Technology Integral University, (2008)

Appointments

Other academic

  • Post-Doctorate Research Officer, Medical School, Swansea University (1/7/2016 - 20/11/2017)

  • Research Assistant in Brain-Computer Interfacing, School of Computing and Intelligent Systems, University of Ulster (7/12/2015 - 30/6/2016)

  • Visiting Researcher, Center of Mechantronics, Indian Institute of Technology Kanpur (6/4/2015 - 16/7/2015)

  • Assistant Professor, School of Computer Science, Dilla University (3/10/2011 - 31/8/2012)

  • Lecturer, Department of Information Technology, Manav Rachna International University (1/7/2009 - 30/9/2011)

Research and professional activities

Research interests

Machine Learning

Big Data and Analytics

Brain-Computer Interface

Non-Stationary Learning

EEG and MEG Signal Processing

Deep Learning

Teaching and supervision

Current teaching responsibilities

  • Team Project Challenge (CE101)

  • Team Project Challenge (CS) (CE291)

  • Team Project Challenge (CSE) (CE292)

  • Team Project Challenge (EE) (CE293)

  • Team Project Challenge (WBL) (CE299)

  • Data Science and Decision Making (CE888)

Publications

Journal articles (9)

Raza, H., Rathee, D., Zhou, S-M., Cecotti, H. and Prasad, G., (2019). Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface. Neurocomputing. 343, 154-166

Chowdhury, A., Raza, H., Meena, YK., Dutta, A. and Prasad, G., (2019). An EEG-EMG Correlation-based Brain-Computer Interface for Hand Orthosis Supported Neuro-Rehabilitation. Journal of Neuroscience Methods. 312, 1-11

Raza, H., Zhou, S., Todd, S., Christian, D., Merchant, E., Morgan, K., Khanom, A., Hill, R., Lynos, R. and Brophy, S., (2019). Predictors of Objectively Measured Physical Activity in 12 month-Old Infants: A Study of Linked Birth Cohort Data with Electronic Health Records. Pediatric Obesity. 14 (7), e12512-e12512

Chowdhury, A., Raza, H., Meena, YK., Dutta, A. and Prasad, G., (2018). Online Covariate Shift Detection based Adaptive Brain-Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation. IEEE Transactions on Cognitive and Developmental Systems. 10 (4), 1070-1080

Chowdhury, A., Meena, YK., Raza, H., Bhushan, B., Uttam, AK., Pandey, N., Hashmi, AA., Bajpai, A., Dutta, A. and Prasad, G., (2018). Active Physical Practice Followed by Mental Practice Using BCI-Driven Hand Exoskeleton: A Pilot Trial for Clinical Effectiveness and Usability. IEEE Journal of Biomedical and Health Informatics. 22 (6), 1786-1795

Raza, H., Zhou, S., Hill, R., Lyons, RA. and Brophy, S., (2017). Identification of predictors of objectively measured physical activity in 12-month-old British infants: a machine learning driven study. The Lancet. 390, S74-S74

Rathee, D., Raza, H., Prasad, G. and Cecotti, H., (2017). Current Source Density Estimation Enhances the Performance of Motor-Imagery-Related Brain–Computer Interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 25 (12), 2461-2471

Raza, H., Cecotti, H., Li, Y. and Prasad, G., (2016). Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface. Soft Computing. 20 (8), 3085-3096

Raza, H., Prasad, G. and Li, Y., (2015). EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments. Pattern Recognition. 48 (3), 659-669

Book chapters (1)

Raza, H. and Rathee, D., (2018). Covariate shift detection-based nonstationary adaptation in motor-imagery-based brain–computer interface. In: Signal Processing and Machine Learning for Brain-Machine Interfaces. Editors: Tanaka, T. and Arvaneh, M., . Institution of Engineering and Technology. 125- 141. 9781785613982

Conferences (13)

Chowdhury, A., Raza, H., Dutta, A. and Prasad, G., (2017). EEG-EMG based Hybrid Brain Computer Interface for Triggering Hand Exoskeleton for Neuro-Rehabilitation

Raza, H., Cecotti, H. and Prasad, G., (2016). A combination of transductive and inductive learning for handling non-stationarities in motor imagery classification

Raza, H., Chowdhury, A., Dutta, A. and Prasad, G., (2015). Cortico-Muscular-Coupling and Covariate Shift Adaptation based BCI for Personalized Neuro- Rehabilitation of Stroke Patients

Raza, H., Cecotti, H., Li, Y. and Prasad, G., (2015). Learning with Covariate Shift-Detection and Adaptation in Non-Stationary Environments : Application to Brain-Computer Interface

Raza, H., Cecotti, H. and Prasad, G., (2015). Optimising Frequency Band Selection with Forward-Addition and Backward-Elimination Algorithms in EEG-based Brain-Computer Interfaces

Chowdhury, A., Raza, H., Dutta, A., Nishad, SS., Saxena, A. and Prasad, G., (2015). A Study on Cortico-muscular Coupling in Finger Motions for Exoskeleton Assisted Neuro-Rehabilitation

Raza, H., Prasad, G., Li, Y. and Cecotti, H., (2014). Covariate shift-adaptation using a transductive learning model for handling non-stationarity in EEG based brain-computer interfaces

Raza, H., Prasad, G., Li, Y. and Cecotti, H., (2014). Toward Transductive Learning Classifiers for Non-Stationary EEG

Raza, H., Prasad, G. and Li, Y., (2014). Adaptive Learning with Covariate Shift- Detection for Non-Stationary Environments

O Doherty, D., Meena, YK., Raza, H., Cecotti, H. and Prasad, G., (2014). Exploring gaze-motor imagery hybrid brain-computer interface design

Raza, H., Prasad, G. and Li, Y., (2013). Dataset shift detection in non-stationary environments using EWMA charts

Raza, H., Prasad, G. and Li, Y., (2013). EWMA based two-stage dataset shift-detection in non-stationary environments

Raza, H., Nandal, P. and Makker, S., (2010). Selection of cluster-head using PSO in CGSR protocol

Grants and funding

2019

The development of a new CPD tracker using AI and embedded machine learning to track and enhance performance of all staff.

Innovate UK (formerly Technology Strategy Board)

AI-Assisted Decision-Making System for Cancer Pathways of the Colchester Hospital

East Suffolk and North Essex NHS Foundation Trust

2018

Provide KTP 2018

Provide

Business and Local Government Data Research Centre (BLG DRC)

Economic and Social Research Council

Provide KTP 2018

Innovate UK (formerly Technology Strategy Board)

Contact

h.raza@essex.ac.uk
+44 (0) 1206 876145

Location:

PARKSIDE UNIT C2, Colchester Campus

More about me
My teaching and research: http://sagihaider.ml/