SIMAR SINGH RAYAT@SIMAR23
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Events attended
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Submissions made
India
2 years of experience
About me
Simar Singh Rayat is a visionary software engineer, researcher, and innovator, driven by a passion for cutting-edge technology and scientific exploration. Currently pursuing a Bachelor of Technology in Computer Science and Engineering at Graphic Art Hill University, Dehradun, he has already made significant contributions to the fields of AI, blockchain, neural computing, and embedded systems. A dedicated research scholar and IEEE author, Simar has published multiple research papers and filed over five patents, demonstrating his commitment to advancing technology. His work spans program synthesis, neural signal processing, ethical computing, and quantum energy transfer systems, with an emphasis on real-world applications. Simar has gained hands-on experience through internships at Puma and Palantir in AI research, and his expertise extends across cloud computing, machine learning, and full-stack development. His innovative mindset has led him to win four hackathons on campus, proving his ability to tackle complex challenges. His notable projects include Prinzo, a technological printing solution, and FinGenie, an interactive chatbot built with ReactJS and GPT API. He is also exploring brain-computer interfaces, smart UPI-enabled glasses, and wireless battery charging through surface currents, pushing the boundaries of current technology. With multiple certifications from AWS, Microsoft, and IBM, Simar continues to expand his technical repertoire, excelling in SQL, AI, and cloud infrastructure. He is currently competing in the IBM Granite AI Hackathon 2025, aiming to contribute breakthrough innovations in artificial intelligence. Aiming to earn the prestigious Turing Award and the ABL Award, Simar’s ultimate goal is to become a scientist and develop revolutionary technologies that redefine human-computer interaction and ethical AI.
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🤓 Submissions
AI-Powered Cybersecurity Threat Detection
AI-powered cybersecurity threat detection system leveraging IBM Granite 3.1 and RAG to analyze security logs, detect anomalies, generate automated security reports, and suggest mitigation strategies. Designed for enterprise use with Linux CLI or web dashboard deployment. Our solution addresses the growing complexity of cybersecurity threats by integrating AI-driven analytics to identify patterns in vast security datasets. Using IBM Granite 3.1’s advanced NLP capabilities, we provide real-time threat intelligence, anomaly detection, and automated response recommendations. The system processes structured and unstructured data, ensuring compliance and scalability. With seamless integration into existing security frameworks, enterprises can enhance their cyber defense strategies efficiently. This solution ingests logs from various sources, cleans and normalizes the data, and applies AI-based threat detection to uncover malicious activities. It offers a high level of automation in generating security alerts and providing recommendations, reducing the workload on security teams. The RAG-based approach ensures that past incidents and security patterns inform new threat analysis, continuously improving the system’s accuracy. The project includes a user-friendly interface, either as a command-line tool for system administrators or a web dashboard for broader enterprise usability. The AI-driven system not only detects threats but also provides predictive analytics to forecast potential cyberattacks before they occur. Enterprises can leverage this technology to strengthen their security postures, minimize breaches, and improve incident response times. The deployment is designed for scalability and efficiency, our cybersecurity system offers an advanced approach to enterprise security, ensuring businesses stay ahead of evolving cyber threats.