- Home
- Faculty
- School of Computer Science
- Dr. Pranshu Chandra Bhushan Singh Negi
Dr. Pranshu Chandra Bhushan Singh Negi
Assistant Professor
Profile Summary
Dr. Pranshu Chandra Bhushan Singh Negi is a researcher and academician specializing in Biomedical Signal Processing, Machine Learning, and data-driven healthcare technologies. He completed his Ph.D. in Biomedical Engineering from IIT (BHU), Varanasi.
His research focuses on deep learning-based multimodal signal classification, with applications in gait analysis, neurological disorder detection, and human activity recognition.
He has published in reputed journals and international conferences, contributing advanced computational methods for real-time biomedical data analysis and intelligent healthcare systems.
Work Experience
Dr. Pranshu Negi is currently serving as an Assistant Professor at the School of Computer Science, UPES Dehradun.
He brings experience in biomedical engineering research, interdisciplinary project development, and advanced computational modelling, supporting both academic teaching and collaborative research initiatives.
Research Interests
Biomedical Signal Processing | Machine Learning | Deep Learning | Data Science | Human Activity Recognition | Gait Analysis | Brain–Computer Interfaces
Teaching Philosophy
Dr. Negi emphasizes strong conceptual understanding, hands-on experimentation, and analytical thinking in his teaching approach.
He integrates real-world biomedical and computational case studies to help students connect theory with practical implementation, fostering curiosity, collaboration, and problem-solving skills.
Courses Taught
Machine Learning | Biomedical Signal Processing | Data Science | Image Processing | Programming Fundamentals | Database Management System
Awards and Achievements
- Recognized for research contributions in biomedical engineering, signal processing, and AI-enabled healthcare through multiple journal and conference publications
Scholarly Activities
Dr. Pranshu Negi is actively engaged in developing deep learning and signal processing models for biomedical applications, including gait abnormality detection, brain–computer interface systems, and human activity classification.
His research includes work on wavelet-based scalograms, attention-based CNN architectures, and multimodal data analysis techniques, with publications in SCI-indexed journals and IEEE conferences.
He also contributes as a reviewer for reputed journals and conferences, supporting academic research and knowledge advancement in computational healthcare.
Contact
