Sourav Dey

About

Sourav Dey is a rising researcher at the forefront of applying advanced machine learning to building energy management. His work centers on developing intelligent control systems that dramatically improve energy efficiency in complex building environments. Dey’s major contribution lies in pioneering the use of reinforcement learning (RL) and inverse reinforcement learning (IRL) for building energy control. His most-cited paper, “Reinforcement learning building control approach harnessing imitation learning” (2023, 54 citations), demonstrates how RL can be enhanced by imitation learning to optimize energy use, drawing inspiration from successes in autonomous vehicles and robotics. This work, alongside his studies on inverse reinforcement learning for energy management (26 citations), establishes a novel framework where buildings learn optimal control policies from expert demonstrations rather than from scratch. By addressing the growing complexity of building systems, Dey’s research offers a scalable, data-driven path to significant energy savings and carbon reduction. His achievements are particularly notable for bridging cutting-edge AI techniques with practical, real-world sustainability challenges, positioning him as a key innovator in smart building technology.

Research Focus

Key Achievements

Top Papers

1. 1
   Reinforcement learning building control approach harnessing imitation learning

   54 citations · 2023
2. 2
   Inverse reinforcement learning control for building energy management

   26 citations · 2023
3. 3
   Inverse Reinforcement Learning Control for Building Energy Management

   3 citations · 2023

Key Collaborators

Contact & Links