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The University of Southampton

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Email:: skc1v12@soton.ac.uk

Dr Shre Kumar Chatterjee B.Eng, MSc, PhD

Visiting Academic

Dr Shre K. Chatterjee is an active researcher in Machine Learning, Signal Processing and Data Science and working for Roke Manor Research Ltd.

Dr. Shre Kumar Chatterjee passed his B.Eng (Electronics and Communications) from Visvesvaraya Technological University, Belgaum, India in 2004. He worked as a trainee consultant and soon headed a newly formed division (which included training students in Microprocessor/Microcontroller programming etc., and liasing with corporates and educational organizations for recruitment of engineering graduates).

Thereafter he pursued an MSc in Nanotechnology at University of Southampton during 2005-06. After the successful completion of his MSc, he was involved in several high profile projects in the industry in the microfabrication domain, specializing in Organic Thin Film Transistors (for active matrix flexible and rollable displays).

In 2012, he left the industry to pursue a full time PhD under Prof. Koushik Maharatna to pursue his interests in Machine Learning and Signal Processing.

Upon successful completion of his PhD, Dr Chatterjee has worked as a Research Fellow in both school of ECS and Engineering, and is currently working in Industry. He is actively looking forward to building strong ties between the University of Southampton's School of Business and the industry.

Research

Publications

Contact

Research interests

Applications of Machine Learning, Data Science, Embedded Systems and IoT

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Articles

Chatterjee, S. K., Malik, O., & Gupta, S. (2018). Chemical sensing employing plant electrical signal response-classification of stimuli using curve fitting coefficients as features. Biosensors, 8(3), [83]. https://doi.org/10.3390/bios8030083
Chatterjee, S. K., Das, S., Maharatna, K., Masi, E., Santopolo, L., Colzi, I., Mancuso, S., & Vitaletti, A. (2017). Comparison of decision tree based classification strategies to detect external chemical stimuli from raw and filtered plant electrical response. Sensors and Actuators B: Chemical, 249, 278-295. https://doi.org/10.1016/j.snb.2017.04.071
Snow, S., Gough, H., Chatterjee, S., Soska, A., & schraefel, M. C. (2016). Exploring the influence of social factors on indoor environment quality. Indoor Air, 1-8.
Das, S., Ajiwibawa, B. J., Chatterjee, S. K., Ghosh, S., Maharatna, K., Dasmahapatra, S., Vitaletti, A., Masi, E., & Mancuso, S. (2015). Drift removal in plant electrical signals via IIR filtering using wavelet energy. Computers and Electronics in Agriculture, 118, 15-23. https://doi.org/10.1016/j.compag.2015.08.013
Chatterjee, S. K., Das, S., Maharatna, K., Masi, E., Santopolo, L., Mancuso, S., & Vitaletti, A. (2015). Exploring strategies for classification of external stimuli using statistical features of the plant electrical response. Journal of the Royal Society Interface, 12(104), 20141225. https://doi.org/10.1098/rsif.2014.1225
Chatterjee, S. K., Ghosh, S., Das, S., Manzella, V., Vitaletti, A., Masi, E., Santopolo, L., Mancuso, S., & Maharatna, K. (2014). Forward and inverse modelling approaches for prediction of light stimulus from electrophysiological response in plants. Measurement, 53, 101-116. https://doi.org/10.1016/j.measurement.2014.03.040

Conference

Biswas, D., Bono, V., Scott-South, M., Chatterjee, S., Soska, A., Snow, S., Noakes, C., Barlow, J. F., Maharatna, K., & schraefel, M. C. (2016). Analysing wireless EEG based functional connectivity measures with respect to change in environmental factors. 1-4. Paper presented at The IEEE International Conference on Biomedical and Health Informatics (BHI2016), United States. https://doi.org/10.1109/BHI.2016.7455969

Thesis

Chatterjee, S. (2017). An approach towards plant electrical signal based external stimuli monitoring system. University of Southampton.

Dr Shre Kumar Chatterjee
Southampton Business School,
Building 2,
University of Southampton,
Highfield,
Southampton
SO17 1BJ,
UK

Email:S.K.Chatterjee@soton.ac.uk

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