
Ph.D. Research Scholar specializing in biomedical signal processing, with hands-on experience in machine learning and data analytics. Proficient in Python, MATLAB, SQL, and Power BI, with expertise in data preprocessing, feature engineering, statistical analysis, and predictive modeling. Experienced in developing reproducible data pipelines, model training and evaluation, and applying AI/ML techniques to real-world datasets. Author of multiple peer-reviewed publications, committed to delivering data-driven solutions to complex engineering challenges.
· Acquired, managed, and preprocessed EEG and other physiological datasets, ensuring data quality and developing reproducible analysis pipelines using Python.
· Performed statistical analysis, feature extraction, and machine learning-based modeling to identify patterns and support data-driven research outcomes.
· Prepared scientific manuscripts, technical reports, and publications while collaborating with multidisciplinary teams on AI/ML and biomedical signal processing projects.
Programming: Python (Pandas, NumPy, Scikit-learn), MATLAB
Machine Learning & AI: Supervised and Unsupervised Learning, Deep Learning, Predictive Modeling, Optimization Techniques
Data Analysis & Visualization: Power BI (DAX, Power Query), Matplotlib, Excel (Pivot Tables, VLOOKUP, AI-powered Automation)
Database: SQL (PostgreSQL)
Data Techniques: Data Cleaning and Preprocessing, Feature Engineering, Exploratory Data Analysis (EDA)
Analytics: Statistical Analysis, Hypothesis Testing and Experimentation
Tools & Platforms: Jupyter Notebook, Google Colab, BIOPAC Data Acquisition System
Model Development: Model Training , Evaluation and Validation
Domain Expertise: Biomedical Signal Processing ( EEG, ECG, Respiration, PPG, GSR)
Internship on “Accelerating the identification of antimicrobial resistance of human pathogens using artificial intelligence” (SERB Accelerate Vigyan). • Internship on “Accelerating the identification of antimicrobial resistance of human pathogens using artificial intelligence” (SERB Accelerate Vigyan).
Conferences
· S. Barik, S. Pal and G. Paul, “Mental health Correlates of Neural Dynamics During Passive Media Consumption and Resting State: An EEG Study”, IEEE International Conference on Computing, Intelligence and Application (CIACON), 18-19 July, 2025.
· S. Barikand S. Pal, “AI Enabled Stress Monitoring using PPG and GSR”, All India Seminar on Machine Learning and Soft Computing Application in Engineering and Science (MLSE-ES), April 6, 2024.
· S. Barikand S. Pal, “Enhancement of Stress Analysis Performance using Respiration Information”, 2023 IEEE 3rd Applied Signal Processing Conference (ASPCON), Nov. 24, 2023.
· S. Barik& M. Chattopadhyay, “An Evaluation on Global E-waste Management”, 7th International Conference on Microelectronics, Computing & Communication Systems (MCCS), July 10, 2022.
Journals
· S. Barik, V.K. Thakur, M.A. Miah, S. Pal, “Detection of Stress from PPG and GSR Signals using AI Framework”, Journal of The Institution of Engineers (India): Series B, 03 January, 2025. https://doi.org/10.1007/s40031-024-01191-z
· S. Barikand M. Chattopadhyay, “Analysis Empowering Home Safety in Underserved Regions: Introducing a Smart Gas Leakage Management System for Internet-Deprived Remote Area”, International Journal of Microsystems and IoT, Volume 2, Issue 2, pp. 580-585, Feb. 20, 2024.
· S. Barikand M. Chattopadhyay, “In House monitoring and measurement of respiration rate using Disposable Paper Sensor”, Neuroquantology, Volume 20, Issue 9, pp. 2072-2079, Sept. 27, 2022.