Expertise

Topics:  Sensor Networks, Smart Grid, Infrastructure Monitoring, Building Energy Management, Machine Learning for Signal Processing

Industries: Research, Education/Learning, Civil Engineering

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.

Media Experience

Carnegie Mellon University: Where graduates land high-demand careers in AI  — Study International
“I’m excited to see what transformations that will happen to our profession once these students have graduated and taken on the workforce,” says Mario Bergés, a professor in the Department of Civil and Environmental Engineering. “This new generation of engineers will not only be able to master AI tools but to recognise how they can leverage engineering domain knowledge to extend them to be more practical and powerful.”

Brick Consortium Announces Inaugural Commercial Members  — ACHR News
“The Consortium will provide the community assurances that Brick has a long-term future and can be a contributing technology for improving the efficiency and comfort of buildings,” added Carnegie Mellon University Professor Mario Bergés.

Brick Consortium Announces Inaugural Commercial Members  — AutomatedBuildings.com
“We are excited to have these industry leaders joining the Brick Consortium and contributing to the development of the Brick schema” said Carnegie Mellon University professor Yuvraj Agarwal. “The Consortium will provide the community assurances that Brick has a long-term future and can be a contributing technology for improving the efficiency and comfort of buildings” said Carnegie Mellon University professor Mario Bergés. Both Agarwal and Bergés are Brick Consortium Steering Committee members.

Smart space habitat looks to put AI to work in deep space  — New Atlas
"How do you conduct automated fault detection and diagnosis without a lot of system data? This is where AI comes in," says Associate Professor Mario Bergés, who heads up the research team. "We have machines that learn by themselves if you give them enough data, but we don't have a lot of machines that can reason by using existing engineering knowledge, which can reduce the amount of data they need."

Dominican Republic Greens see ‘hope reborn’  — Dominican Today
"Today, in this esplanade of the Attorney General’s Office, we can express satisfaction because as a result of the organization and persistence of this people, actions are beginning to be seen to apply justice to those who have illegally enriched themselves from State coffers,” said Mario Bergés, reading a document on behalf of the civic movement Marcha Verde.

Research Using AI in Energy Applications at CMU Showcases the Frontier of Opportunities  — CMU News
Mario Bergés, professor in the Department of Civil and Environmental Engineering, examines the way existing buildings monitor and use energy in order to make them more efficient. His research involves what is known as non-intrusive load monitoring, which analyzes smart meter data, identifying appliance usage and predicting malfunctions. “If you can be smart about how to analyze the data that's coming from your smart meter, then you are going to be able to fingerprint individual appliances and also get to know a lot about the behavior of people in the home through their usage of devices that consume electricity,” Bergés said.