Mr. Soumyapriya Goswami | Industrial Internet of Things | Best Researcher Award
IT Researcher, Kalyani Government Engineering College, West Bengal
Mr. Soumyapriya Goswami is a dedicated B.Tech IT researcher at Kalyani Government Engineering College with a strong academic foundation and practical experience in emerging technologies, including Artificial Intelligence, Internet of Things (IoT), Wireless Sensor Networks, edge computing, reinforcement learning, and quantum security for medical devices. His education reflects consistent academic excellence, having completed his secondary and higher secondary studies at Asansol Ramakrishna Mission High School and Dhadka NCLahiri Vidyamandir, followed by his ongoing B.Tech IT program at Kalyani Government Engineering College. Professionally, Soumyapriya has developed expertise in AI/ML model deployment, prompt engineering for generative AI, cloud-based solutions, project management, and team leadership, with proficiency in programming languages (Python, C, C++, Java), AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn), and cloud platforms (Google Cloud, Docker, Jenkins). His research interests encompass energy-efficient scheduling for WSNs, reinforcement learning-based threat detection for IoT devices, quantum-aware security protocols for medical devices, digital twins, and cyber-physical systems. In conclusion, Mr. Soumyapriya Goswami demonstrates strong potential to bridge academic research with industry applications, delivering innovative solutions in AI, IoT, and quantum technologies, while contributing to knowledge dissemination, mentorship, and technological advancement in emerging research domains, positioning him as a promising early-career researcher with impactful scholarly and practical contributions.
Profile: GOOGLE SCHOLAR | SCOPUS | ORCID
Featured Publications
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Goswami, S. (Published). NashDQNSleep: Energy-efficient sleep scheduling for WSN using Nash Equilibrium and Deep Q-Networks. Elsevier EAAI. Citation count: unavailable
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Goswami, S. (Under Review). Polaris: Optimized power-aware GPU scheduling framework for cloud environments. IEEE TPDS. Citation count: unavailable
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Goswami, S. (Under Review). Qure: Quantum-aware protocols for medical device security using entanglement and root-of-trust designs. IEEE Cybernatics. Citation count: unavailable
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Goswami, S. (Ongoing). TinySurvive: Reinforcement Learning-based threat intelligence model for low-power IoT devices in hazardous environments. Citation count: unavailable