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INFERLab

INtelligent inFrastructure rEseaRch Laboratory

Carnegie Mellon University

Who are we?

Welcome to the Intelligent Infrastructure Research Laboratory (INFER Lab), from the Civil and Environmental Engineering Department at Carnegie Mellon University.

We are interested in improving the operational efficiency of our physical infrastructure, as well as increasing its resilience, adaptiveness and autonomy. In an increasingly resource-constrained world, our infrastructure systems will need to be able to interact with their environment and with each other in order to maximize their efficiency and minimize risks. Hence, our lab interested in solving these challenges by providing answers to questions such as: (a) how can we utilize the data generated by instrumentation systems to provide better feedback, learn from experience and better plan for the future?, (b) how can we improve and leverage the interconnectedness of our infrastructure?, and © to what extent can we utilize the resources that are already present in our infrastructure to help solve these problems?

The INFERLab is led by Prof. Mario Bergés from the Department of Civil and Environmental Engineering at Carnegie Mellon University.

Interests

  • Smart Infrastructure
  • Cyber-Physical Systems
  • Structural Health Monitoring
  • Applied Machine Learning

Projects

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Autonomous Solutions for Self-Regulating Sustainable Habitats

This project will focus on the development of real-time monitoring, diagnostics, and intelligent control methods that come together …

Electricity Disaggregation

In this project we focus on a specific class of unsupervised algorithms based on deep learning techniques that can learn instantaneous …

Estimating Climate Change Impacts on Electricity Demand

In this project we focus on estimating the effects of climate change, particularly changes to temperature and humidity, to electricity …

GridBallast: Autonomous Load Control For Grid Resilience

The GridBallast project will create low-cost demand-side management technology to address resiliency and stability concerns …

Human-in-the-loop Control of HVAC Systems in Commercial Buildings

The primary goal of this project is to design, implement, and evaluate a human-in-the-loop sensing and control system for energy …

Infrastructure monitoring for damage assessment from sensors on-board vehicles

This project targets the development of a metadata inference framework to provide operational information, i.e., the metadata …

SHADE: Surface Heat Assessment for Developed Environments

Through this project, we seek to develop an integrated framework for predicting extreme temperature risks in urban areas.

Strategic Methane Gas Pipeline Replacement Planning: Analytics and Monitoring

This project focuses on the development of statistical models for relating pipeline infrastructure characteristics and the results of …

Meet the Team

Principal Investigators

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Mario Bergés

Professor of Civil and Environmental Engineering

Machine Learning, Statistical Inference, Smart Infrastructure

Graduate Students

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Francisco Fonseca

PhD Student

Energy Systems, Operations Research, Data Science, Machine Learning

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Bingqing Chen

PhD Student

Reinforcement Learning, System Identification, Heating, Ventilation and Air Conditioning Systems

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Byeongseong Choi

PhD Student

Smart Cities & Infrastructures, Statistical & Probabilistic Model, Regional Risk Analysis

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Elvin Vindel

PhD Student

Demand Flexibility, HVAC Control, Building Energy, Renewable Energy

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Jingxiao Liu

PhD Student

Structural Health Monitoring, System Identification, Machine Learning

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Ronald S. Holt

PhD Student

Applied Deep Learning, Signal Separation, Data Science, Sustainability

Visitors

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Alan Mazankiewicz

Visiting Scholar

Machine Learning on Data Streams, Interpretability in Machine Learning, Multivariate Statistical Dependence Estimation

Alumni

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Jingkun Gao

Senior Algorithm Engineer

Building Automation Systems, Statistical Inference, Semantic Technologies

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Emre Can Kara

VP of Engineering

Vehicle Electrification, Data Science, Machine Learning

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Henning Lange

Postdoc

Machine Learning, Variational Inference, Energy Systems

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Matineh Eybpoosh

Co-Founder & CEO

Energy Storage, Data Science, Machine Learning

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Suman Giri

Director, Data Science & Consulting

Non-Intrusive Load Monitoring, Data Science, Healthcare Analytics

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Jerry Lei

Postdoctoral Research Associate

HVAC Control Logic, Software Testing, Building Automation Systems

Contact

  • 412 268 4572
  • 119 Porter Hall, 5000 Forbes Ave., Pittsburgh, PA, 15213, United States
  • Tuesdays 11:00 to 12:00