Carnegie Mellon University

Mario Berges

Mario Berges

Professor, Civil and Environmental Engineering

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Address
Civil & Environmental Engineering
Carnegie Mellon University
Pittsburgh, PA 15213-3890

Bio

Mario Bergés is a professor in the Department of Civil and Environmental Engineering at Carnegie Mellon University (CMU). He is interested in making our built environment more operationally efficient and robust through the use of information and communication technologies, so that it can better deal with future resource constraints and a changing environment. Currently his work largely focuses on developing approximate inference techniques to extract useful information from sensor data coming from civil infrastructure systems, with a particular focus on buildings and energy efficiency.

Bergés is the faculty co-director of the Smart Infrastructure Institute at CMU, as well as the director of the Intelligent Infrastructure Research Lab (INFERLab). Among recent awards, he received the Professor of the Year Award by the ASCE Pittsburgh Chapter in 2018, Outstanding Early Career Researcher award from FIATECH in 2010, and the Dean's Early Career Fellowship from CMU in 2015.

Bergés received his B.Sc. in 2004 from the Instituto Tecnológico de Santo Domingo, in the Dominican Republic; and his M.Sc. and Ph.D. in Civil and Environmental Engineering in 2007 and 2010, respectively, both from Carnegie Mellon University.

Education

PhD 2010 - Carnegie Mellon University
MS 2007 - Carnegie Mellon University
Certificate 2005 - Instituto Tecnológico de Santo Domingo (Dominican Republic)
BS 2004 - Instituto Tecnológico de Santo Domingo (Dominican Republic)

Research

Research Group: AIS 
Research Centers: Sii

Areas of Interest

  • Infrastructure monitoring
  • Building energy management
  • Smart grid
  • Machine learning for signal processing
  • Sensor networks

Publications

Liu, J., Xu, S., Bergés, M., Noh, H Y. (2021). “HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosis.”  arXiv preprint arXiv:2107.11435

Choi,B., Bergés, M., Bou-Zeid, E., Pozzi, M. (2021). “Short-term probabilistic forecasting of meso-scale near-surface urban temperature fields.” Environmental Modeling & Software, 145: 105189

Chen, B., Cai, Z., Bergés, M. (2020). “Gnu-RL: A Practical and Scalable Reinforcement Learning Solution for Building HVAC Control using a Differentiable MPC Policy.” Frontiers in Built Environment, 6: 174

Chen, B., Francis, J., Pritoni, M., Kar, S., Bergés, M. (2020). “COHORT: Coordination of Heterogeneous Thermostatically Controlled Loads for Demand Flexibility.” Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. PP 31-40

Lange, H., Bergés, M. (2018). “Variational bolt: Approximate learning in factorial hidden markov models with application to energy disaggregation.” Thirty-Second AAAI Conference on Artificial Intelligence.

 

Recent Honors & Awards

  • 2015: Dean's Early Career Fellowship Award, CMU College of Engineering
  • 2018: ASCE Pittsburgh Section 2017 Professor of the Year Award

Courses

  • 12-740 Data Acquisition
  • 12-741 Data Management
  • 12-752 Data-Driven Building Energy Management
  • 12-770 Autonomous Sustainable Buildings: From Theory to Practice

Mario Bergés: Using Analytics to Understand Energy Consumption in Buildings

Energy bills show usage as a total number, leaving consumers, building managers and others to wonder which activities or appliances are using the most electricity. Civil and Environmental Engineering Professor Mario Bergés is using sensing and analytics to understand energy consumption in buildings and disaggregate the total usage into its parts.