Dr. Nagendra Panini Challa
VIIM Current Workshops
Dr. Nagendra Panini Challa
Dr. Nagendra Panini Challa, Associate Professor Grade-2, School of Computer Science and Engineering (SCOPE), VIT-AP University, Andhra Pradesh, India
Mobile:9885716839.Email:nagendra.challa@vitap.ac.in
1. Name and Academic Designation
Dr. Nagendra Panini Challa
Associate Professor Grade-1, School of Computer Science and Engineering, VIT-AP University, Amaravati
2. VIIM Resource Category
Artificial Intelligence and Machine Learning
Computational Mathematics and Mathematical Software
Applied Mathematics and Interdisciplinary Applications
Research Methodology and Research Mentoring
3. Profile
Dr. Nagendra Panini Challa is an academician and researcher in Computer Science and Engineering whose work focuses on artificial intelligence, machine learning, deep learning, natural language processing, computer vision, and intelligent systems. His research applications span healthcare, agriculture, image recognition, speech and language processing, text summarisation, recommendation systems, explainable AI, and Internet of Things technologies. His work includes the use of AI techniques for medical data analysis, crop-related prediction, handwritten character recognition, and intelligent decision support. His broad research interests provide opportunities to connect computational methods with mathematical foundations, data analysis, model evaluation, and real-world problem-solving. He can contribute to programmes that introduce learners to AI and machine learning while emphasising critical thinking, appropriate model selection, and an understanding of system limitations.
4. Areas of Contribution to VIIM
Introduction to artificial intelligence and machine learning.
The mathematical foundations of AI and machine learning.
Hands-on machine learning using real-world datasets.
Deep learning and computer vision applications.
Natural language processing and text analysis.
AI applications in healthcare and agriculture.
Predictive analytics and intelligent decision-support systems.
Explainable AI, model evaluation, and responsible interpretation of results.
AI-assisted mathematical problem-solving.
UG/PG project guidance and research mentoring in AI and machine learning.