Dr. Debasis Acharya is an academician and researcher specializing in intelligent control systems, optimization algorithms, and advanced control engineering. He holds a Ph.D. in Electrical Engineering from the National Institute of Technology Nagaland, which he completed in 2024, along with an M.Tech. in Power Systems and a B.Tech. in Electrical Engineering.

His research focuses on the development of intelligent, optimization-driven control strategies for complex engineering systems, with particular emphasis on metaheuristic optimization, fuzzy logic control, fractional-order control, and adaptive intelligent controllers. His research applications include mechanical ventilator pressure regulation, energy and thermal systems, and advanced control methodologies. He is also exploring explainable and confidence-aware reinforcement learning for safety-critical and complex dynamic systems.

Dr. Acharya has an active research profile, with **40 Scopus-indexed publications, including 18 SCI/SCIE-indexed papers and 8 Q1-ranked publications**, as reported in his academic record. His scholarly interests extend to interdisciplinary research integrating artificial intelligence, machine learning, computational intelligence, and control engineering to address challenging real-world problems.

As an educator, he emphasizes concept-driven learning, practical problem-solving, and the integration of theoretical foundations with real-world engineering applications. He is committed to fostering critical thinking, research aptitude, and innovation among students while encouraging them to explore emerging technologies and interdisciplinary engineering solutions.

Dr. Acharya is particularly interested in collaborative research, postgraduate and doctoral research supervision, and the development of intelligent engineering solutions that contribute to technological advancement and societal well-being.

Research Interests: Intelligent Control Systems; Metaheuristic Optimization Algorithms; Fuzzy and Fractional-Order Control; Artificial Intelligence and Machine Learning; Explainable Reinforcement Learning; Computational Intelligence; and Modeling, Simulation, and Optimization of Dynamic Systems.