Dr. Debaditya Chakraborty is an Assistant Professor in the Department of Construction Science at the University of Texas at San Antonio. His research focuses on the application of Artificial Intelligence and Machine Learning in construction science, building energy performance improvement, renewable energy and sustainability. Prior to joining the faculty of the University of Texas-San Antonio, Dr. Chakraborty was a postdoctoral research fellow at the University of Cincinnati. He holds a Ph.D. from the University of Cincinnati and a Bachelor of Technology from the National Institute of Technology Rourkela in India. In addition to his academic achievements, he has served as a consultant in the areas of software development and process optimization for a major technology firm in Mumbai, India.
Ph.D., Civil Engineering, University of Cincinnati, Dec. 2018
Field of Study:
Applied Artificial Intelligence and Machine Learning
Areas of Research Interest:
Energy Modeling and Optimization
Fault Detection and Diagnostics
Data-Driven Construction Estimation and Process Optimization
Data-Driven Renewable Energy Technology Selection
Sustainable and Resilient Buildings
Selected Articles in Journals:
Chakraborty, D., & Elzarka, H. (2019). Advanced machine learning techniques for building performance simulation: a comparative analysis. Journal of Building Performance Simulation, 12(2), 193-207.
Chakraborty, D., & Elzarka, H. (2019). Early detection of faults in HVAC systems using an XGBoost model with a dynamic threshold. Energy and Buildings, 185, 326-344.
Chakraborty, D., & Elzarka, H. (2018). Performance testing of energy models: are we using the right statistical metrics?. Journal of Building Performance Simulation, 11(4), 433-448.
Chakraborty, D., Elzarka, H., & Bhatnagar, R. (2016). Generation of accurate weather files using a hybrid machine learning methodology for design and analysis of sustainable and resilient buildings. Sustainable Cities and Society, 24, 33-41.
Elzarka, H. M., Yan, H., & Chakraborty, D. (2017). A vague set fuzzy multi-attribute group decision-making model for selecting onsite renewable energy technologies for institutional owners of constructed facilities. Sustainable cities and society, 35, 430-439.
Mansuri, D., Chakraborty, D., Elzarka, H., Deshpande, A., & Gronseth, T. (2017). Building information modeling enabled cascading formwork management tool. Automation in Construction, 83, 259-272.