Dr. Prashanth CH – Assistant Professor | SR University

Dr. Prashanth CH

Dr. Prashanth CH

Assistant Professor • Department of Computer Science & Artificial Intelligence

PhD from

VIT-AP

Years of Experience

1 Years

Web Pages

Contact Details

prashanth@sru.edu.in

Availability

Mon-Fri, 1-4 PM, Room no: 1208, F2

Dr. Prashanth Choppara is an Assistant Professor at the School of Computer Science and Artificial Intelligence, SR University, Warangal. He holds a Ph.D. in Computer Science from VIT-AP University, specializing in agent-based deep reinforcement learning for task scheduling in fog computing. His research interests include machine learning, deep learning, quantum machine learning, reinforcement learning, cybersecurity, bioinformatics, and intelligent computing. He has published several research articles in reputed journals, including IEEE Access, IEEE Transactions on Computational Biology and Bioinformatics, and Cluster Computing. His work focuses on intelligent task scheduling, resource optimization, quantum-enhanced prediction, and computational biology. He is proficient in Python, C++, TensorFlow, PyTorch, and related machine learning tools, with experience in both academic teaching and software development.

Interests

deep learning quantum machine learning reinforcement learning cybersecurity bioinformatics

Research Areas

deep learning quantum machine learning reinforcement learning bioinformatics Fog Computing Task Scheduling

Student Supervision

Ph.D: Ongoing 10, B.tech Projects: 2

Ph.D in Computer Science

2026

VIT-AP

M.Tech in Computer Science

2021

GVPCE

B.Tech in Information Technology

2018

BEC

No work experience added.

→ Network Security
→ AR VR
→ Machine Learning
→ Python

Key Publications

P. Choppara and L. Bommareddy, “Ai-driven protein pocket detection through integrating deep q-networks for structural analysis,” Journal of Computer-Aided Molecular Design, vol. 39, no. 1, p. 90, 2025. doi:10.1007/s10822-025-00669-2
P.Choppara and B.Lokesh, “Efficient task scheduling and load balancing in fog computing for crucial healthcare through deep reinforcement learning,” IEEE Access, vol. 13, pp. 26542–26563, 2025. doi: 10.1109/ACCESS.2025.3539336
P.Choppara and B.Lokesh, “Leveraging quantum lstm for high-accuracy prediction of viral mutations,” IEEE Access, vol. 13, pp. 25282–25300, 2025. doi: 10.1109/ACCESS.2025.3539337.
P.Choppara and B.Lokesh, “Q-bafnet: A hybrid quantum classical approach for drug-target binding affinity prediction,” IEEE Transactions on Computational Biology and Bioinformatics, 2025. doi:10.1109/TCBBIO.2025.3603103
P.Choppara and S.S.Mangalampalli, “Adaptive task scheduling in fog computing using federated dqn and k-means clustering,” IEEE Access, vol. 13, pp. 75466–75492, 2025. doi: 10.1109/ACCESS.2025.3563487
P.Choppara and S.S.Mangalampalli, “Resource adaptive automated task scheduling using deep deterministic policy gradient in fog computing,” IEEE Access, vol. 13, pp. 25969–25994, 2025. doi: 10.1109/ACCESS.2025.3539606
P.Choppara and S.Mangalampalli, “An efficient deep reinforcement learning based task scheduler in cloud-fog environment,” Cluster Computing, vol. 28, no. 1, pp. 1–26, 2025. doi:10.1007/s10586-024-04712-z
P.Choppara and S.S.Mangalampalli, “A hybrid task scheduling technique in fog computing using fuzzy logic and deep reinforcement learning,” IEEE Access, vol. 12, pp. 176363–176388, 2024. doi: 10.1109/ACCESS.2024.3505546.
P.Choppara and S. S. Mangalampalli, “Reliability and trust aware task scheduler for cloud-fog computing using advantage actor critic (a2c) algorithm,” IEEE Access, vol. 12, pp. 102126–102145, 2024. doi: 10.1109/ACCESS.2024.3432642.
P.Choppara and S.Mangalampalli, “An effective analysis on various task scheduling algorithms in fog computing,” EAI Endorsed Transactions on Internet of Things, vol. 10, 2024. doi: 10.4108/eetiot.4589
P. Choppara and B. Lokesh, “A machine learning approach to genomic task scheduling in fog computing,” in 2025 IEEE 4th International Conference on Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE, 2025, pp. 1–6. doi: 10.1109/TEMSMET65536.2025.11467515
P.CHOPPARA and B.LOKESH, “Quantum machine learning for prediction of compound-protein interactions in drug discovery,” in 2024 12th International Conference on Intelligent Systems and Embedded Design (ISED), 2024, pp. 1–6. doi: 10.1109/ISED63599.2024.10956316.