Ms. Raghi K R | Federated Learning | Research Excellence Award

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Ms. Raghi K R | Federated Learning | Research Excellence Award 

Ms. Raghi K R | Federated Learning | Sathyabama Institute of Science and Technology | India

Mrs. Raghi K.R. is a dedicated computer science educator and researcher with strong experience in both teaching and applied research. She holds a B.E. in Computer Science and Engineering (Anna University), an M.E. in Computer Science and Engineering (PSN Engineering College / Anna University), and has submitted her Ph.D. synopsis in Computer Science and Engineering at College of Engineering, Guindy, Anna University. Her professional journey includes roles as Assistant Professor and Teaching Fellow across several institutions: PSN Engineering College (CSE), College of Engineering Guindy, and currently at St. Joseph’s College of Engineering, Chennai giving her over a decade of teaching experience, spanning undergraduate and postgraduate courses. Her research interests lie in Artificial Intelligence, Deep Learning, Machine Learning, Cloud Security, and Web Mining. She possesses robust technical skills including programming in Python, Java, C, C++, web technologies (HTML), and experience with simulation platforms such as NS2 and MATLAB, as well as familiarity with open-source operating systems. Through her career she has mentored and guided multiple innovative and funded projects — for example leading a seed-research project titled “StepSmart: Design and Development of an Affordable IoT and Deep Learning Based Footwear for Diabetic Health Monitoring,” and supervising award-winning student projects such as “Trash Triage: Trailblazing Waste Management with Real-Time Street Waste Monitoring” and “Identification of Fake Medicinal Raw Materials Using Machine Learning.” These recognitions reflect her commitment to practical, socially relevant research. Her academic repertoire includes teaching diverse subjects like Artificial Intelligence, Mobile Computing, Information Security, Web Programming, Soft Computing, Software Project Management, Cyber Security, Web Technology, and more. She has also supervised substantial academic work: a Ph.D. thesis on “Privacy-Preserving Deep NN Classification over Signature Cryptosystem in Cloud Environments,” a secure payment-scheme design in multihop networks, and semantic similarity computation for natural language. As a scholar and mentor, Mrs. Raghi combines strong theoretical foundations with hands-on applied research, striving to develop secure, intelligent, and socially impactful computing solutions. Her involvement in both education and research along with project funding, awards, and diverse technical competencies — positions her as a proactive and forward-looking professional in the fields of AI, cybersecurity, and data-driven intelligent systems, committed to nurturing student talent and contributing to technological advancement.

Professional Profiles: ORCID | Google Scholar 

Selected Publications

  1. Thomas, R. K. L., Sanjay, G. J., Pandeeswaran, C., & Raghi, K. R. (2024). Advanced CCTV Surveillance Anomaly Detection, Alert Generation and Crowd Management using Deep Learning Algorithm.

  2. Vethavikashini, A. M., Jamal, S. M., & Raghi, K. R. (2024). Huntington’s Disease Prediction Using Xception CNN.

  3. Devi, S. R., Geetha Priya, S., Sathi, G., Naveen Kumar, S., Dinesh, M., & Raghi, K. R. (2024). Design and Development of a Touch Free Smart Home Controlling System Based on Virtual Reality (VR) Technology.

  4. Raghi, K. R., Sudha, K., Sreeram, A. M., Steve Joshua, S. (2024). Software Development Automation Using Generative AI.

  5. Raghi, K. R. (2023). Privacy-Preserving Deep NN Classification over Signature Cryptosystem in Cloud Environments.

  6. Anitha, T., Sai Srihitha, G. R. P. Lakshmi Aiswarya, & Raghi, K. R. (2025). Predictive Modeling of Social Media Data Using Machine Learning Techniques.

  7. (As mentor) StepSmart: Design and Development of an Affordable IoT and Deep Learning Based Footwear for Diabetic Health Monitoring.

Shaogang Hu | Inspired Computing | Best Researcher Award

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Prof. Shaogang Hu | Inspired Computing | Best Researcher Award

Prof. Shaogang Hu | Inspired Computing | University of Electronic Science and Technology | China

Prof. Shaogang Hu is a distinguished academic and researcher affiliated with the University of Electronic Science and Technology of China. Renowned for his work in neuromorphic computing, edge artificial intelligence, and spiking neural networks, he has established himself as a thought leader in energy-efficient computing systems. With a robust academic presence and strong publication record, Prof. Hu contributes significantly to the evolution of intelligent sensing technologies, particularly in the domains of hardware-software co-design, sensor fusion, and low-power AI processing. His interdisciplinary approach and collaboration with both academic and industrial partners position him as a leading figure in next-generation AI systems.

Academic Profile:

Scopus

Education:

Prof. Shaogang Hu holds a Ph.D. in Electronic Engineering, where he specialized in advanced chip architecture and intelligent signal processing. His academic training emphasized the development of computational models that bridge hardware limitations with evolving AI algorithms. Throughout his doctoral studies, Prof. Hu demonstrated a strong aptitude for interdisciplinary research, integrating concepts from neuroscience, electrical engineering, and computational theory. His academic background provided a solid platform for his current research into neuromorphic computing and low-energy embedded systems.

Experience:

Prof. Hu has gained significant experience in both academic and research environments. At the University of Electronic Science and Technology of China, he leads research teams focusing on neuromorphic circuits and edge AI applications. His academic role involves supervising graduate students, managing collaborative research projects, and developing experimental platforms for energy-efficient intelligent systems. He has worked closely with international research teams to push the boundaries of real-time computing, particularly in sensor-based systems, biomedical devices, and real-time video analytics. His active involvement in the broader academic community includes peer reviewing for indexed journals, technical committee memberships, and panel participation in various research forums.

Research Interest:

Prof. Shaogang Hu’s primary research interests include neuromorphic computing, spiking neural networks, energy-efficient AI chips, event-based sensors, and intelligent edge systems. He is particularly focused on optimizing hardware architectures to support real-time data processing with minimal energy consumption. His work in developing algorithms and chip systems that mimic neural behavior offers promising solutions for low-latency, low-power intelligent devices. Prof. Hu also explores hybrid models that combine frame-based and event-based sensor technologies to enhance system responsiveness in dynamic environments, such as robotics and smart surveillance systems.

Award:

Prof. Hu has been recognized for his contributions through various academic accolades, invitations to international conferences, and peer-reviewed editorial roles. His work has been consistently acknowledged for its originality and practical value in applied sciences. As a senior member of professional organizations such as IEEE and ACM, Prof. Hu continues to lead and contribute to the development of high-impact research. His efforts in mentoring early-career researchers and promoting scientific exchange further reflect his leadership in the academic and research landscape.

Selected Publications:

  • “YOLO-fall: a YOLO-based fall detection model with high precision, shrunk size, and low latency” (2025)

  • “An Image Encryption Algorithm Based on HNN with Memristor” (2025) – 1 Citation

  • “Spatio-Temporal Fusion Spiking Neural Network for Frame-Based and Event-Based Camera Sensor Fusion” (2024) – 4 Citations

  • “Floating-Point Approximation Enabling Cost-Effective and High-Precision Digital Implementation of FitzHugh-Nagumo Neural Networks” (2024) – 3 Citations

Conclusion:

Prof. Shaogang Hu is a highly accomplished researcher whose innovative contributions to neuromorphic systems and energy-efficient AI make him an outstanding candidate for this award. His scholarly output, leadership in collaborative research, and continued pursuit of intelligent sensing technologies have made a measurable impact in the field. With a focus on real-world application, Prof. Hu’s research advances the capabilities of AI in hardware-constrained environments. His academic integrity, technical leadership, and forward-looking vision make him not only a deserving recipient of this recognition but also a role model in shaping the future of intelligent systems research.