Our paper on predicting ground motions using neural network based equations has been presented at the ASCE GEESD conference in June in Austin, TX. Abstract Parts of Texas, Oklahoma, and Kansas have experienced increased rates of seismicity in recent years, providing new datasets of earthquake Read more
Machine Learning in Science and Engineering
Our PhD student Jose speaks at the Machine Learning for Science and Engineering Symposium in Pittsburgh (June 6-8, 2018) during the Civil Engineering track. He's talk is entitled Deep Reinforcement Learning for Urban Energy Management and introduces his research in CityLearn: Abstract: The building Read more
Presentation at ACM Computer-Human-Interaction 2018
Our awesome PhD student June flew to Montreal this week to present his work on Reinforcement Learning for Occupant Centered Building Control at a workshop on Living Labs during the ACM Computer-Human-Interaction (CHI) conference. His talk was part of a workshop organized by the amazing folks over at Read more
Machine Learning for occupancy based cooling control
Applied Energy has published our work Using machine learning techniques for occupancy-prediction-based cooling control in office buildings. We propose a demand-driven control strategy that automatically responds to occupants’ energy-related behavior for reducing energy consumption and maintains Read more
Best Paper Award 2017 in Building & Environment !!
As one of three from over 2000 submissions, our work on occupancy learning-based demand-driven building control has been selected for a 2017 Best Paper Award from the journal Building and Environment. The research was led by our colleagues at ETH Zurich, Switzerland, and was implemented in the Read more
