Supriya Supriya approved

Accepting PhD Students

PhD projects

Develop a real-time analysis system for the automated detection of abnormal patterns from biomedical signals. Epilepsy detection from Brain EEG signals using machine learning techniques Sleep stage classification from Brain EEG signals using machine learning techniques

Calculated based on number of publications stored in Pure and citations from Scopus

Research activity per year

Personal profile

Research interests

Health informatics, brain signal analysis, non-invasive health applications, and intelligent data analysis for statistical modeling, Machine Learning, Artificial Intelligence, Pattern Recognition, Time Series analysis, Big data, EEG signals Analysis and Classification, Biomedical Signal Processing, Data Mining, Deep Web, Search Engine, Web crawler, Search Engine.

Research Profile

Supriya is a dynamic and dedicated Lecturer in Design and Creative Technology (disciplines ranging from software engineering, cybersecurity, and information technology). Around six years of teaching experience (Australia and overseas) in postgraduate and undergraduate courses in information technology, Computer Science and Engineering, cloud computing, and Networking. Skilled in programming using MATLAB, C++, C# visual studio, and Python. She exemplifies the essence of empowerment and progress for women in STEM fields As a rising star in the academic realm of IT, Supriya exemplifies the fusion of passion, knowledge, and drive. Holds a Ph.D. in Information and Mathematical Science from Victoria University Melbourne, Australia, with a thesis Title “Brain Signal Analysis and Classification by Developing New Complex Network Techniques” where she demonstrated a strong proficiency in analysis of the Brain EEG signals data to detect the abnormalities such as Epilepsy, Alcohol use disorder, and Sleep stage classification etc. Her published work in reputable journals and conferences, such as IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Access, IEEE Reviews in Biomedical Engineering, Health Information Science and Systems etc. demonstrates a commitment to scholarly rigor and a keen ability to communicate complex ideas effectively. Regular reviewer for esteemed journals of high quality, including EEE Transactions and MDPI.

Education/Academic qualification

Ph.D. in Information and Mathematical Science Thesis Title: Brain Signal Analysis and Classification by Developing New Complex Network Techniques, Victoria University Melbourne, Australia Award Date: 23rd April 2020 Master of Engineering in Computer Science Thesis Title: A Novel Technique for Deep Web Pages Classification and Extraction, Punjab Technical University, India Award Date: April 2014 Bachelor of Engineering in Information Technology (With Distinction) Punjab Technical University, India Award Date: May 2010


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