Mario Bergés

Professor Carnegie Mellon University

  • Pittsburgh PA

Mario Bergés is interested in making our built environment more operationally efficient through the use of communication technologies.

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Carnegie Mellon University

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Biography

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.

Areas of Expertise

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

Media Appearances

Research Using AI in Energy Applications at CMU Showcases the Frontier of Opportunities

CMU News  online

2025-03-24

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.

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Dominican Republic Greens see ‘hope reborn’

Dominican Today  online

2020-12-04

"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.

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Smart space habitat looks to put AI to work in deep space

New Atlas  online

2019-09-26

"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."

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Industry Expertise

Research
Education/Learning
Civil Engineering

Accomplishments

ASCE Pittsburgh Section 2017 Professor of the Year Award

2018

Dean's Early Career Fellowship Award

2015

CMU College of Engineering

Education

Instituto Tecnológico de Santo Domingo

B.S.

Civil Engineering

2005

Carnegie Mellon University

M.S.

Civil and Environmental Engineering

2007

Carnegie Mellon University

Ph.D.

Civil and Environmental Engineering

2010

Patents

Electrical meter system for energy desegregation

US11499999B2

2018-11-15

An energy meter is configured to determine component waveforms that form a measured waveform. The meter inputs the waveform into one or more entries of a data structure, each entry of the one or more entries of the data structure storing a weight value that is determined based at least in part on values of the data signatures representing the plurality of remote devices, each entry being connected to one or more other entries of the data structure.

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Articles

The Digital Twin Landscape at the Crossroads of Predictive Maintenance, Machine Learning and Physics Based Modeling

arXiv:2206.10462

2022

The concept of a digital twin has exploded in popularity over the past decade, yet confusion around its plurality of definitions, its novelty as a new technology, and its practical applicability still exists, all despite numerous reviews, surveys, and press releases. The history of the term digital twin is explored, as well as its initial context in the fields of product life cycle management, asset maintenance, and equipment fleet management, operations, and planning. A definition for a minimally viable framework to utilize a digital twin is also provided based on seven essential elements.

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Ten questions concerning human-building interaction research for improving the quality of life

Building and Environment

2022

This paper seeks to address ten questions that explore the burgeoning field of Human-Building Interaction (HBI), an interdisciplinary field that represents the next frontier in convergent research and innovation to enable the dynamic interplay of human and building interactional intelligence.

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The field of human building interaction for convergent research and innovation for intelligent built environments

Scientific Reports

2022

Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops.

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