SS
Dr. Subrat Kumar Swain
Maulana Azad National Institute of Technology, Birla Institute of Technology, Kyungpook National University, Arizona State University, Capgemini Consulting India Private Limited
Employment
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Maulana Azad National Institute of Technology Assistant Professor2025 - Present
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Birla Institute of Technology Assistant Professor2015 - 2025
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Capgemini Consulting India Private Limited Associate Consultant2005 - 2007
Education
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Kyungpook National University Ph.D. in Intelligent Systems and Control Engineering2019 - 2024
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Arizona State University Master of Science (MS) in Computer Engineering2010 - 2013
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Wayne State University Masters of Science (MS) in Electrical Engineering2008 - 2009
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College of Engineering and Technology B.Tech in Electrical Engineering2001 - 2005
Projects & Funding
Projects & funding information is unavailable.
Publications (44)
- Enhanced Position Tracking of Maglev Using Data-Driven Adaptive Control With Neural Network Disturbance Estimator Save
- A Nonlinear Spatio-Temporal Dynamical Stabilization and Control for Inverter Based Networked Microgrids: Energy Function Based Approach Save
- Reinforcement Learning-Based Human Like Shared Control for Driver Vehicle Interactions Save
- Optimized neural network for soil moisture prediction in precision agriculture Save
- Data driven prediction based reliability assessment of solar energy systems incorporating uncertainties for generation planning Save
- Finite-Time Convergent Adaptive Sliding Mode Control for Integration of VSCs into Modernized Microgrids with Parametric and Dynamic Uncertainties Save
- Development of Indoor Autonomous Mobile BOT for Static Obstacle Avoidance Save
- LDCCAES: A Concomitant Perception Methodology Facilitating Real-Time Detection and Estimation of Median-Lane Positioning for Prototype Autonomous Vehicle Save
- Optimizing Lateral Motion Control of an Autonomous Ground Vehicle Using Modified Particle Swarm Optimization with Model Predictive Controller Save
- Product Length Predictions with Machine Learning: An Integrated Approach Using Extreme Gradient Boosting Save