Dr. Maitreyee Dey is a lecturer in Computer Science and Applied Computing at the School of Computing and Digital Media.
Maitreyee Dey
Dr. Maitreyee Dey is a lecturer specialising in Computer Science and Applied Computing within the School of Computing and Digital Media.
She received her Ph.D. degree from London South Bank University, United Kingdom. Her doctoral research formed part of Innovate UK's initiative on Machine Learning Techniques for Building Energy Management Systems under the guidance of Prof. Sandra Dudley. Her academic journey commenced with a Bachelor's degree (B. Tech) and culminated with a Master's degree (M. Tech) from West Bengal University of Technology, India.
In addition to her academic role, Dr. Dey plays a pivotal role as a Lead Machine Learning Engineer at Neuville Grid Data, and she holds an honorary position as a Visiting Fellow at London South Bank University.
Before joining LondonMet, she worked as a post-doctoral research fellow in the Department of Electrical and Electronics Engineering at London South Bank University, concurrently holding the role of Data Mining Lead at Neuville Grid Data. Her responsibilities primarily revolve around the application of machine learning and data mining techniques to analyze solar farm data for projects such as Access to Innovation (A2i) and Low Carbon London (LCLDN), funded by the EU Commission through its regional development fund. These projects are executed in collaboration with London South Bank University and Neuville Grid Data.
Dr. Dey's academic journey includes a three-year tenure as a research fellow at Jadavpur University, India, where she focused on automatic target recognition from U.S. Army FLIR imagery. This research endeavor received funding from "The Technical Quality Improvement Program (TEQIP) project" and "The Tata Consultancy Research Scholar Program (TCS-RSP)."
Throughout her career, she has actively contributed to the preparation and management of various successful grants, including those from the European Regional Development Programs, InnovateUK Smart Grant, EPSRC, KTP, Access-to-Innovation (A2i), and Marie Skłodowska-Curie Actions.
Her research interests span a wide spectrum, encompassing machine learning, pattern recognition, human-computer interaction (HCI), big data analysis, deep learning, image processing, energy optimisation in smart buildings, renewable energy technology, power grid management, and computer-aided diagnosis using machine learning and artificial intelligence for applications in smart healthcare.
Teaching:
Dr. Dey is currently teaching in the following areas and conducts workshops while offering guidance to students in their project endeavours:
- Smart Data Discovery
- Programming for Data Analysis
- Data Analysis and Visualization
- Data Mining and Machine Learning
- Data Warehousing and Big Data
- Supervision of BSc/MSc Projects
Research interests:
Her research interests span across a wide spectrum encompassing machine learning, pattern recognition, human-computer interaction (HCI), big data analysis, deep learning, image processing, energy optimisation in smart buildings, renewable energy technology, power grid management, and computer-aided diagnosis using machine learning and artificial intelligence for applications in smart healthcare.
Patents:
[P1] M. Dey, S.P. Rana, “High-resolution electrical measurement data processing”
Patent granted: 21st December 2022, UK Patent No. 2599698
Journals:
[J15] S. P. Rana, M. Dey, R. Loretoni, M. Duranti, M. Ghavami, S. Dudley, and G. Tiberi (2023). Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model. Tomography, 9(1), 105-129.
[J14] M. Dey, S. P. Rana, R. Loretoni, M. Duranti, L. Sani, A. Vispa, et al. (2022) Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network. PLoS ONE 17(7): e0271377. https://doi.org/10.1371/journal.pone.0271377.
[J13] S. P. Rana, M. Dey, M. Ghavami, S. Dudley, “Markerless Gait Classification Employing 3D IR-UWB Physiological Motion Sensing” IEEE Sensors Journal 22 (7), 6931-6941.
[J12] S. P. Rana, M. Dey, R. Loretoni, M. Duranti, L. Sani, A. Vispa, M. Ghavami, S. Dudley, and G. Tiberi, "Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data", October 2021, Diagnostics 11(10):1930.
[J11] M. Dey, S. P. Rana, C. V. Simmons, & S. Dudley, Solar farm voltage anomaly detection using high-resolution μPMU data-driven unsupervised machine learning. Applied Energy, 303 (2021), 117656.
[J10] M. Dey, S. P. Rana, S. Dudley, “"Automated terminal unit performance analysis employing x-RBF neural network and associated energy optimisation–A case study-based approach." Applied Energy 298 (2021): 117103.
[J9] S. P. Rana, M. Dey, M. Ghavami, S. Dudley, “3-D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon”, IEEE Transactions on Instrumentation and Measurement 70 (2021): 1-10.
[J8] M. Dey, S. P. Rana, S. Dudley, “A Case Study Based Approach for Remote Fault Detection Using Multi-Level Machine Learning in A Smart Building”, Smart Cities 3 (2), 401-419.
[J7] S. P. Rana, M. Dey, G. Tiberi, L. Sani, A. Vispa, G. Raspa, M. Duranti, M. Ghavami, S. Dudley. “Machine Learning Approaches for Automated Lesion Detection in Microwave Breast Imaging Clinical Data”, Nature Scientific Reports 9 (1), 10510.
[J6] S. P. Rana, M. Dey, M. Ghavami, S. Dudley. "Non-Contact Human Gait Identification through IR-UWB Edge Based Monitoring Sensor." IEEE Sensors Journal (2019), DOI: 10.1109/JSEN.2019.2926238.
[J5] M. Dey, S. P. Rana, P. Siarry. "A robust FLIR target detection employing an auto-convergent pulse coupled neural network." Remote Sensing Letter (2019), DOI: 10.1080/2150704X.2019.1597296.
[J4] S. P. Rana, M. Dey, M. Ghavami, S. Dudley, “Signature Inspired Home Environments Monitoring System Using IR-UWB Technology” Sensors, 19(2).
[J3] S. P. Rana, M. Dey, P. Siarry, “Boosting Content-Based Image Retrieval Performance Through Integration of Parametric & Nonparametric Approaches”, Journal of Visual Communication and Image Representation, 58 (2019): 205-219.
[J2] S. P. Rana, J. Prieto, M. Dey, S. Dudley, J. Corchado, (2018). “A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi-Storey Building”, Sensors, 18(11), 3766.
[J1] M. Dey, S. P. Rana, S. Dudley, “Smart Building Creation in Large Scale HVAC Environments through Automated Fault Detection and Diagnosis”, Future Generation Computer System, 2018.
For an updated list of publications see the link below:
https://scholar.google.co.in/citations?user=UwvwfeMAAAAJ&hl=en
● Low Carbon London (LCLDN) project funded by EU Commission through its Regional Development Fund (ERDF), 2020. Grant number: ERDF 23R19P03064 (2020-2021)
● Access to innovation (A2i) fund, 2020. Grant number: ERDF 23R15S00027 (2020)
● Grants/awards from Innovate UK research project grant (EP/M506734/1), (Total amount £700,000) (2016-2019)
● H2020-MSCA-RISE-2019: Grant no:872752
● Innovate UK: Intelligent ear protection to address occupational hearing loss for use in heavy industry, EAVE.
● Grants/awards from “Tata Consultancy Services Research Scholar Program (TCS-RSP)”, (2015)
● Grants/awards from “Technical quality improvement program (TEQIP) phase-II”, (2012-2015
● Best Paper Award for the 6th International Applied Energy Symposium: Low Carbon Cities & Urban Energy Systems (CUE2020) October10-17, 2020, Volume - 8, Tokyo, Japan/ Virtual Conference.
● Awarded KIZUNA Bond Project (2013) –a Youth-Exchange Project with Asia-Oceania and North America, funded by the Government of Japan.
● Lead Machine Learning Engineer at Neuville Grid Data.
● Honorary position as a Visiting Fellow at London South Bank University.
● Topic Board Editor – Buildings Journal
● Reviewer Board Member – Climate Journal and Signals Journal
● Associate Member (0076794) – Energy Institute
● CIGRE NGN member (620220465) & KMS working group member
● IEEE Member
● International Association of Engineers (IAENG)
● Science and Engineering Institute (SCIEI)
● Technical Program Committee member, 17th International Conference on Ubiquitous Wireless Broadband (ICUWB'17), IEEE, Salamanca, Spain, 11th -16th September 2017
● Ad--hoc reviewers for Infrared Physics & Technology, IEEE Transactions on Cognitive and Developmental Systems, Applied Soft Computing, IET Electronics Letter, IET System Biology, Science and Technology for the Built Environment, ISPRS Journal of Photogrammetry and Remote Sensing, Nature Scientific Data, and many more
Dr. Maitreyee Dey
Lecturer in Computer Science and Applied Computing
Email: m.dey@londonmet.ac.uk