Ms. Shaguna Gupta | Cloud Computing Computer Science Research | Best Researcher Award

Ms. Shaguna Gupta | Cloud Computing Computer Science Research | Best Researcher Award

National College of Ireland | Ireland

Shaguna Gupta is a dedicated computer science researcher specializing in cloud computing, distributed systems, and intelligent transportation systems. She is currently pursuing her Ph.D. at Trinity College Dublin (TCD), Ireland, focusing on highway traffic flow optimization using deep reinforcement learning to improve traffic throughput and travel time reliability. She holds a Master of Technology in Cloud Computing with Queuing Theory from Shri Mata Vaishno Devi University (SMVDU), India, and a Bachelor of Engineering from Jammu University, India. Her research experience spans multiple international institutions, including a postgraduate studentship under Science Foundation Ireland’s CRT ADVANCE project, a research internship at the University of Virginia, USA, and project work at the Indian Institute of Science, Bangalore. She has contributed to multiple publications in areas such as rideshare optimization, queuing theory for cloud services, and microservice resilience. As an educator, she has served as an associate faculty member, demonstrator, and lecturer, supervising cloud computing projects and teaching modules on AI, programming, and cybersecurity. Her research interests include cloud computing, traffic management, multi-agent reinforcement learning, and distributed systems. She has received several academic awards, including gold medals, scholarships, and best paper presentations. Committed to advancing technology for societal benefit, she actively participates in workshops, conferences, and community outreach, reflecting a balance of innovation, leadership, and practical impact.

Profile:  Google Scholar 

Featured Publications

Gupta, S., Narang, R., Krishnaswami, K., & Yadav, S. (1994). Plasma selenium level in cancer patients. Indian Journal of Cancer, 31(3), 192–197.

Fusco, V. F., Sancheti, S., & Gupta, S. (1994). Active antenna element design issues. IEE Colloquium on Smart Antennas (Digest No: 1994/182), 9/1–9/7.

Gupta, S., & Fusco, V. F. (1996). Low cross-polarized integrated mixer/phase shifter patch antenna for beamforming applications. 26th European Microwave Conference, 1, 397–400.

Gupta, S., & Pourush, R. K. S. (1999). Technical Notes-4 x 4 planar phased array of circular patch microstrip antenna in plasma environment for on-board applications. Space Technology-Abingdon, 19(2), 97–108.

Gupta, S., Fusco, V. F., & Sancheti, S. (1999). Self-phased re-transmitting integrated mixer antenna array. International Journal of Electronics, 86(2), 207–215.

Gupta, S., Narang, R., & Patel, M. K., Gupta, K., Kumar, K. (2025). Enhanced ECC-driven text encryption scheme using chaotic maps and Rhotrices. Palestine Journal of Mathematics, 14(3).

Gupta, S., & Arora, S. (2018). Queueing system in cloud services management: a survey. International Journal of Pure and Applied Mathematics, 119(12), 12741–12753.

Shaikh, R. (2025). Prediction of resource utilization in cloud computing using machine learning. Dublin, National College of Ireland.

Mr. Venkatesh Guntreddi | Machine Learning and Deep Learning | Best Researcher Award

Mr. Venkatesh Guntreddi | Machine Learning and Deep Learning | Best Researcher Award

Vellore Institute of Technology | India

Venkatesh Guntreddi is an emerging AI professional with a strong academic and industry background in Artificial Intelligence and Machine Learning. He is currently pursuing an M.Tech in Computer Science and Engineering with a specialization in AI and ML at Vellore Institute of Technology, where he has maintained an excellent academic record. He previously earned his B.Tech in Computer Science from Andhra University, building a solid foundation in programming, algorithms, and systems. Professionally, he has gained two years of experience as an AI Engineer at Tech Mahindra, where he developed and deployed end-to-end AI solutions spanning natural language processing, computer vision, and generative AI. His expertise covers the entire data science lifecycle, including data preprocessing, model building, optimization, and deployment using cloud platforms such as AWS and GCP, supported by MLOps best practices. He has worked extensively with advanced techniques in deep learning, transfer learning, and large language models, including Retrieval-Augmented Generation (RAG) and Agentic AI workflows. His research interests lie in scalable AI systems, applied NLP, generative AI, and real-world enterprise applications. Recognized for delivering impactful, production-ready solutions, he is committed to advancing data-driven innovation and seeks opportunities to contribute as an AI/ML Engineer or Data Scientist.

Profile:  ORCID

Featured Publications

“Deep Learning based Glaucoma Detection using Majority Voting Ensemble of ResNet50, VGG16, and Swin Transformer”