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.
This project will focus on the development of real-time monitoring, diagnostics, and intelligent control methods that come together …
In this project we focus on a specific class of unsupervised algorithms based on deep learning techniques that can learn instantaneous …
In this project we focus on estimating the effects of climate change, particularly changes to temperature and humidity, to electricity …
The GridBallast project will create low-cost demand-side management technology to address resiliency and stability concerns …
The primary goal of this project is to design, implement, and evaluate a human-in-the-loop sensing and control system for energy …
This project targets the development of a metadata inference framework to provide operational information, i.e., the metadata …
Through this project, we seek to develop an integrated framework for predicting extreme temperature risks in urban areas.
This project focuses on the development of statistical models for relating pipeline infrastructure characteristics and the results of …
Machine Learning, Statistical Inference, Smart Infrastructure
Energy Systems, Operations Research, Data Science, Machine Learning
Reinforcement Learning, System Identification, Heating, Ventilation and Air Conditioning Systems
Smart Cities & Infrastructures, Statistical & Probabilistic Model, Regional Risk Analysis
Demand Flexibility, HVAC Control, Building Energy, Renewable Energy
Structural Health Monitoring, System Identification, Machine Learning
Machine Learning on Data Streams, Interpretability in Machine Learning, Multivariate Statistical Dependence Estimation
Building Automation Systems, Statistical Inference, Semantic Technologies
Vehicle Electrification, Data Science, Machine Learning
Machine Learning, Variational Inference, Energy Systems
Energy Storage, Data Science, Machine Learning
Non-Intrusive Load Monitoring, Data Science, Healthcare Analytics
HVAC Control Logic, Software Testing, Building Automation Systems