Selection of cluster-head using PSO in CGSR protocol
H Raza, P Nandal, S Makker. (2010). Selection of cluster-head using PSO in CGSR protocol IEEE-ICM2CS-2010. 1(1).
CV
Education
- B.Tech. in Computer Science and Engineering, Integral University, India, 2004-2008
- M.Tech. in Computer Engineering, Manav Rachna International University, India, 2009-2011.
- Ph.D in Computer Science, University of Ulster, UK, 2016.
Work experience
- Postdoctoral Research Fellow (Nov/2017 - Present)
- Institue for Analytics and Data Science, School of Computer Science and Electronics Engg.
- University of Essex, Colchester, Essex, England, UK
- Postdoctoral Research Officer (Machine Learning) (July/2016 - Oct/2017)
- School of Medicine, Swansea University, Swansea, Wales, UK
- Post-Doctoral Research Assistant (Dec/2015 - Jun/2016)
- Intelligent System Research Centre, School of Computing and Intelligent Systems, Ulster University, UK
- Visiting Researcher (April/2015 - July/2015)
- Centre for Mechatronics, Indian Institute of Technology Kanpur, India
- Supervisor: Professor Ashish Dutta
- Funder: UKIERI: UK-India Education and Research Initiative
- Assistant Professor (Oct/2011 - Aug/2012)
- Department of Computer Science, Dilla University, Ethiopia.
- Assistant Professor (July/2009 - Sept/2011)
- Department of Information Technology, Manav Rachna International University, Delhi-NCR, India
- Lecturer (July/2008 - June/2009)
- Department of Information Technology, Cosmic Business School, New Delhi, India
Research Skills
- Data Science, Big Data Analytics, Deep Learning
- Machine Learning for Biomedical Signals and Health Care Datasets
- Signal Processing: EEG, EMG, and MEG decoding (Feature Extraction and Features Selection); Blind Source Separation; Dimensional Reduction
- Brain-Computer Interfaces (BCI) for Communication and Stroke-Rehabilitation
- Non-Stationary Learning for Dynamic and Varying Systems (Active and Passive Learning Approaches)
- Dataset Shift Detection (Change Detection): Covariate Shift and Concept Shift.
- Social Data Analytics, Sentiment Analysis, Stock-trade Prediction
Publications
GBG Approach for Connectivity and Coverage Control in Wireless Sensor Network
B Singh, H Raza, Ritu. (2011). "GBG Approach for Connectivity and Coverage Control in Wireless Sensor Network." International Journal of Computer Applications. 1(1).
EWMA Based Two-Stage Dataset Shift-Detection in Non-stationary Environments
H Raza, G Prasad, Y Li (2011). "EWMA Based Two-Stage Dataset Shift-Detection in Non-stationary Environments." 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus. 625-635.
Dataset shift detection in non-stationary environments using EWMA charts.
Raza, H., Li,Y., and Prasad,G. (2013). "Dataset shift detection in non-stationary environments using EWMA charts." IEEE-SMC-2013. 1(1).
Covariate shift-adaptation using a transductive learning model for handling non-stationarity in EEG based brain-computer interfaces.
Raza, H., Prasad, G., and Cecotti, H. (2014). "Covariate shift-adaptation using a transductive learning model for handling non-stationarity in EEG based brain-computer interfaces." IEEE BIBM, 2014. 1(1).
EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments
Raza, H., Cecotti, H., and Prasad, G. (2015). "EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments" Pattern Recognition 2015. 48(3), pp 659-669.
A study on cortico-muscular coupling in finger motions for exoskeleton assisted neuro-rehabilitation.
Chwodhury, 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." IEEE-EMBC-2015. pp. 4610-4614.
Learning with covariate shift-detection and adaptation in non-stationary environments: Application to brain-computer interface.
Raza, H., Cecotti, H., and Prasad, G. (2015). "Learning with covariate shift-detection and adaptation in non-stationary environments: Application to brain-computer interface." IEEE-IJCNN-2015. 1(1).
Optimising frequency band selection with forward-addition and backward-elimination algorithms in EEG-based brain-computer interfaces.
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." IEEE-IJCNN-2015. 1(1).
Adaptive Learning with Covariate Shift Detection for Motor Imagery based Brain-Computer Interfaces
Raza, H., Cecotti, H., Li, Y., and Prasad, G. (2016). "Adaptive Learning with Covariate Shift Detection for Motor Imagery based Brain-Computer Interfaces" Soft Computing 2016. 20(8), pp 63085-3096.
Cortico-Muscular-Coupling and Covariate Shift Adaptation based BCI for Personalized NeuroRehabilitation of Stroke Patients.
Chwodhury, A., Raza, H., Dutta, A., and Prasad, G. (2016). "Cortico-Muscular-Coupling and Covariate Shift Adaptation based BCI for Personalized NeuroRehabilitation of Stroke Patients." BCI Meeting, 2016. pp. 4610-4614.
Adaptive learning for modelling non-stationarity in EEG-based brain-computer interfacing.
Raza, H. (2016). "Adaptive learning for modelling non-stationarity in EEG-based brain-computer interfacing." PhD Thesis, 2016.
A combination of transductive and inductive learning for handling non-stationarities in motor imagery classification.
Raza, H., Cecotti, H., and Prasad, G. (2016). "A combination of transductive and inductive learning for handling non-stationarities in motor imagery classification." IEEE-IJCNN, 2016. 1(1).
Current source density estimation enhances the performance of motor-imagery-related brain–computer interface.
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-TNSRE, 2017. 25(12), 2461 - 2471.
Identification of predictors of objectively measured physical activity in 12-month-old British infants: a machine learning driven study.
Raza, H., Zhou, SM., Hill, R., Lyons, RA., Brophy, S. (2017). "Identification of predictors of objectively measured physical activity in 12-month-old British infants: a machine learning driven study." The Lancet, 2017. 390, S74.
Online Covariate Shift Detection based Adaptive Brain-Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation.
Chowdhury, A., Raza., H., Meena, Y.K., Dutta, A., and Prasad,G. (2017). "Online Covariate Shift Detection based Adaptive Brain-Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation." IEEE-TCDS-2017. 1(1).
Covariate Shift Estimation and Adaptation based Ensemble Learning for Handling Inter-or-Intra Session Non- Stationarity in EEG based Brain-Computer Interface
Raza, H., Rathee, D., Zhou, SM., Cecotti, H., and Prasad, G. (2018). Covariate Shift Estimation and Adaptation based Ensemble Learning for Handling Inter-or-Intra Session Non- Stationarity in EEG based Brain-Computer Interface.; Neurocomputing, 2018.
Active Physical Practice Followed by Mental Practice Using BCI-Driven Hand Exoskeleton: A Pilot Trial for Clinical Effectiveness and Usability
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, 2018.
An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation
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, 2019.
Predictors of Objectively Measured Physical Activity in 12 month-Old Infants: A Study of Linked Birth Cohort Data with Electronic Health Records
Raza, H., Zhou, S., Todd, S., Christian, D., Merchant, E., Morgan, K., Khanom, A., Hill, R., Lynos, R., and Brophy, S. Predictors of objectively measured physical activity in 12‐month‐old infants: A study of linked birth cohort data with electronic health records. Pediatric obesity, p.e12512..
Bagging Adversarial Neural Networks for Domain Adaptation in Non-Stationary EEG
Raza, H., and Samothrakis, S. Bagging Adversarial Neural Networks for Domain Adaptation in Non-Stationary EEG. 2019 International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary, 2019, pp. 1-7.
Single-Trial EEG Classification with EEGNet and Neural Structured Learning for Improving BCI Performance
Raza, H., Chowdhury, A., Bhattacharyya, S., and Samothrakis, S. Single-Trial EEG Classification with EEGNet and Neural Structured Learning for Improving BCI Performance. IEEE-IJCNN, Accepted 20-March-2020.
Deep Learning based Prediction of EEG Motor Imagery of Stroke Patients’ for Neuro-Rehabilitation Application
Raza, H., Chowdhury, A., and Bhattacharyya, S. Deep Learning based Prediction of EEG Motor Imagery of Stroke Patients’ for Neuro-Rehabilitation Application. IEEE-IJCNN./i>.</p> </li> </article> </div>
A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface
Rathee, D., Raza, H., Roy, S., and Prasad, G. A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface. Scientific Data, Nature.
Emojional: Emoji Embeddings
Barry, E., Jameel, S., and Raza, H. Emojional: Emoji Embeddings UK Workshop on Computational Intelligence. (pp. 312-324). Springer
</ul> Talks ======Event Classification and Intensity Discrimination for Forest Fire Inference With IoT
Singh, V K., Singh, C., and Raza, H. Event Classification and Intensity Discrimination for Forest Fire Inference With IoT. IEEE Sensors Journal.
Machine Learning and Brain-Computer Interface
Talk at Integral University, Lucknow, India, Lucknow, India
Artificial Intelligence and its application in Brain-Computer Interface
Talk at Shri Ramswaroop Memorial Public School, Lucknow, India, Lucknow, India
Non-Stationary Learning in EEG-based Brain-Computer Interface
Talk at Swansea University, Swansea, Wales
Learning Under Dataset Shifts
Talk at Integral University, Lucknow, India, Lucknow, India
Learning Under Different Training and Testing Distributions
Tutorial at Big Data and Analytics Summer School-2018, University of Essex, Colchester, England
Machine Learning and Data Science
Tutorial at Data Science Intense (DSI) Training Program 2018, African Institute for Mathematical Sciences, Cape Town, South Africa
Introduction to Python: Minerva Analytics Ltd
Tutorial at University of Essex, UK, Colchester, Essex
Introduction to R: Suffolk County Council
Tutorial at Suffolk County Council , Ipswich, Ipswich, UK
Introduction to R: Southend County Council
Tutorial at University of Essex (Southend Campus), Southend, Southend, UK
Introduction to Data Analytics: Essex Police
Tutorial at University of Essex, Colchester, Colchester, UK
Web Scraping and Mining: Southend Council
Tutorial at University of Essex (Southend Campus), Southend, Southend, UK
Intermediate Level Data Analysis: Essex Police (2 days)
Tutorial at University of Essex, Colchester, Colchester, UK
Artificial Intelligence & Machine Learning
Talk at Taj MG Road, Bengaluru, India, Bengaluru, India
Artificial Intelligence and it`s application in Industry and Research
Talk at St. Joseph`s College, Bengaluru, India, Bengaluru, India
Artificial Intelligence and it`s application in Industry and Research
Talk at St. Joseph`s College, Bengaluru, India, Bengaluru, India
Artificial Intelligence & Machine Learning
Talk at VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India, Hyderabad, India
Introduction to R: Essex County Council (2 days)
Tutorial at University of Essex, Colchester, Colchester, UK
Introduction to R: Southend-on-Sea Borough Council
Tutorial at Civic Centre, Southend-on-Sea, Southend-on-Sea, UK
Intermediate Level Data Analysis: Essex Police (2 days)
Tutorial at University of Essex, Colchester, Colchester, UK
Data Analysis and Predictive Analytics : Southend-on-Sea Borough Council (2-Days)
Tutorial at Civic Centre, Southend-on-Sea, Southend-on-Sea, UK
Managing Covariate Shift in EEG/MEG-based BCI
Talk at University of Sheffield, Sheffield, England, UK
Bagging Adversarial Neural Networks for Domain Adaptation in Non-Stationary EEG
Talk at IJCNN Conference, Budapest, Hungary
Introduction to Deep Learning
tutorial at IADS Summer School, University of Essex, Colchester, Essex, England
Learning Under Distribution Shift: Transfer Learning
tutorial at IADS Summer School, University of Essex, Colchester, Essex, England
Getting started with Python and GitHub using Google Colab
Talk at Integral University, 2020, India
Brain-Computer Interfacing
Tutorial at BDG LifeSciences [Online], India
[Keynote] Brain-Computer Interfacing
Keynote at International Conference on Computing, Communication, and Intelligent Systems, 2021, India
Brain Computer Interface for Communication in Completely Locked in State Patients
Talk at Research Trends in Computer Vision and NLP, NorthCap University, India, NorthCap University, India
Python Intro Course
Tutorial at IADS: Data Science and Decision Making Summer School, University of Essex, 2021, Colchester, UK
Introduction to Deep Learning & Neural Networks
Tutorial at IADS: Data Science and Decision Making Summer School, University of Essex, 2021, Colchester, UK
Learning under Different Training and Testing Distributions
Tutorial at IADS: Data Science and Decision Making Summer School, University of Essex, 2021, Colchester, UK