- Home
- Faculty
- School of Computer Science
- Arti Bahuguna
Arti Bahuguna
Assistant Professor
Profile Summary
Mrs. Arti Bahuguna is an Assistant Professor at UPES, Dehradun, with specialization in Computer Vision, Deep Learning, and Machine Learning.
She has completed her Ph.D. pre-synopsis at NIT Sikkim, focusing on advanced methodologies for hand gesture recognition using computer vision and AI techniques.
Her work integrates feature engineering, machine learning, and deep learning models to develop accurate, efficient, and robust gesture recognition systems for real-world applications.
Work Experience
Mrs. Arti Bahuguna joined UPES as an Assistant Professor in June 2025.
She has previously served as a Ph.D. Scholar at NIT Sikkim and as an Assistant Professor at Noida Institute of Engineering & Technology and H.N.B. Garhwal Central University, teaching AI, Machine Learning, and Computer Vision-related subjects.
Educational Qualification
Ph.D. (Pre-synopsis Completed), National Institute of Technology Sikkim.
Research Interests
Computer Vision, Deep Learning, Machine Learning, Image Processing, Static Hand Gesture Recognition, Hybrid Fusion Models, Lightweight Neural Networks, Attention Mechanisms, Pattern Recognition, AI-driven Human-Computer Interaction.
Teaching Philosophy
Mrs. Arti Bahuguna emphasizes nurturing curiosity, critical thinking, and confidence through a balanced approach combining theoretical knowledge with hands-on learning.
She integrates real-world applications and emerging AI technologies into her teaching, fostering an inclusive and engaging environment for innovation and skill development.
Courses Taught
Deep Learning, Machine Learning, Artificial Intelligence, Computer Vision, Image Processing, Python Programming, Design and Analysis of Algorithm, Theory of Automata and Computation.
Scholarly Activities
Mrs. Arti Bahuguna has published research in SCI and Scopus-indexed journals in the fields of Computer Vision and Image Processing.
Her work focuses on gesture recognition using hybrid models, feature extraction techniques, and lightweight neural networks to improve efficiency and accuracy.
She actively participates in conferences, workshops, and faculty development programs, while mentoring students and contributing as a reviewer in AI and Computer Vision domains.
Contact
