Deekshith SM
Assistant Professor
About
An Assistant Professor specializing in Computer Science and Applications, currently serving as an academic professional based in Bengaluru. I hold a Master of Computer Applications (MCA) degree from RV College of Engineering (RVCE) and am KSET qualified in Computer Science and Applications.
My academic journey is anchored in a deep passion for teaching, mentoring, and rigorous research. Over the years, I have actively guided students through complex engineering projects and research papers, cultivating an environment of inquiry and technical excellence. My core teaching portfolio includes foundational and advanced subjects such as Analysis and Design of Algorithms (ADA), Data Structures, AI/ML and Data Science.
Beyond traditional curriculum delivery, my research trajectory is intensely focused on cutting-edge developments in artificial intelligence. I am deeply invested in advancing brain-inspired adaptive AI systems, specifically looking at context aware memory, dynamic decision making, and structural explainability. By bridging the gap between theoretical computer science and practical, cognitive computing architectures, I aim to contribute meaningfully to next generation intelligent systems. I am committed to fostering academic rigor, inspiring future technologists, and driving impactful innovations within the academic community.
This research paper presents a groundbreaking framework for modernizing democratic processes by designing a secure and transparent voting system that integrates ubiquitous computing and blockchain technology. While exploring how ubiquitous environments and decentralized architectures intersect to reshape industries like smart energy, healthcare, and supply chains, the core focus of the study centers on revolutionizing digital elections. The paper addresses critical architectural hurdles such as scalability, interoperability, and security, emphasizing the need for robust consensus mechanisms. By evaluating real-world case studies and secure voting implementations, the research demonstrates how this technological synergy eliminates single points of failure, mitigates electoral fraud, and guarantees absolute immutability, transparency, and trust in digital governance.
Link: https://journals.stmjournals.com/joarb/article=2024/view=155738/
Brain-Inspired Adaptive Architecture: Designing neural systems that mirror biological cognitive functions to enhance machine adaptability, learning flexibility, and operational efficiency.
Context-Aware Memory Systems: Developing dynamic, tiered memory frameworks that allow models to retain, retrieve, and contextualize information seamlessly across shifting environments.
Explainable Decision-Making: Engineering transparent algorithmic pathways to ensure AI-driven choices are interpretable, verifiable, and aligned with human accountability standards.
Cognitive Computing Integration: Bridging the gap between theoretical computer science and brain-inspired computing models to build next-generation intelligent systems capable of autonomous reasoning.
Research Interests
Artificial Intelligence, Machine Learning, Human brain inspired cognitive Artificial Intelligence, Quantum Computing
NSS best leader cadet award
Awarded for exceptional leadership in successfully commanding 300 cadets, organizing camp operations, and delegating critical responsibilities like food preparation and flag hoisting etc during a 10 days camp.
Deekshith SM
Assistant Professor
MCA,KSETSchool of Computer Science and Engineering
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deekshithsm@rvu.edu.in -
Bengaluru
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