Dr. Parshuram Aarotale is currently serving as an Assistant Professor in the Department of Electronics and Telecommunication Engineering at Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering and Technology (VPKBIET), Baramati, India. He earned his M.E. in Electronics Engineering from the Government College of Engineering in 2008. He completed his M.S. in Biomedical Engineering from the University of North Dakota in Spring 2020. During his master's studies, his research focused on biomedical signal processing and analysis, with hands-on experience in the acquisition and analysis of EEG, ECG, EMG, Center of Pressure (COP), and blood pressure signals. He also served as a Graduate Teaching Assistant for courses including Signals and Systems, Control Systems, and Electronics Laboratory for two years. Dr. Aarotale completed his Ph.D. in Biomedical Engineering from Wichita State University in Spring 2025. His doctoral dissertation, titled “Advanced Deep Learning Techniques for Biomedical Signal and Image Analysis for Healthcare,” focused on the development of advanced artificial intelligence methods for healthcare applications. During his Ph.D., he also conducted research on a bio-inspired soft robotic glove designed to assist individuals with muscular dystrophy in performing activities of daily living. As a Graduate Teaching Assistant, he taught Biomedical Computer Applications (BME 335), Machine Learning (CS 770), and Applied Data Science courses. His research interests include biomedical signal processing, machine learning, deep learning, brain–computer interfaces (BCIs), affective computing, healthcare AI, wearable sensing technologies, rehabilitation engineering, and biomedical image analysis. Dr. Aarotale has over 18 years of teaching experience and has taught a wide range of undergraduate and postgraduate courses, including Digital Signal Processing, Signals and Systems, Electromagnetic Engineering, Machine Learning, and Deep Learning. He received a BCUD Research Grant from Savitribai Phule Pune University for his research project on EEG-based Emotion Recognition during the academic years 2016–2018. He has successfully guided numerous undergraduate projects in the areas of signal processing, communication systems, machine learning, and biomedical engineering. His current research focuses on the application of artificial intelligence and deep learning techniques to biomedical signals and images, cognitive state assessment using EEG, wearable healthcare systems, neuroengineering, and assistive rehabilitation technologies.