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 (c) 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 on electric power systems.
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 …
In this project we study the potential of existing datasets to provide information necessary for decision-making in different contexts: …
Machine Learning, Statistical Inference, Smart Infrastructure
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
Resilient Building Structures & System Design, Smart Infrastructure, Innovative Construction Materials & Material Testing
Smart Cities & Infrastructures, Structural Health Monitoring, Data Science
Applied Deep Learning, Signal Separation, Data Science, Sustainability
Machine Learning on Data Streams, Interpretability in Machine Learning, Multivariate Statistical Dependence Estimation
Building Automation Systems, Statistical Inference, Semantic Technologies
Energy Systems, Operations Research, Data Science, Machine Learning
HVAC Control Logic, Software Testing, Building Automation Systems
HVAC Systems, Information Modeling, Building Automation Systems, Fault Detection and Diagnosis
Vehicle Electrification, Data Science, Machine Learning
Machine Learning, Variational Inference, Energy Systems
Structural Health Monitoring, Data Mining, Dimensionality Reduction Techniques
Energy Storage, Data Science, Machine Learning
Non-Intrusive Load Monitoring, Data Science, Healthcare Analytics
Publications that we are especially proud of right now
This paper presents the Plug-Load Appliance Identification Dataset (PLAID), a labelled dataset containing records of the electrical voltage and current of domestic electrical appliances obtained at a high sampling frequency (30 kHz). The dataset contains 1876 records of individually-metered appliances from 17 different appliance types (e.g., refrigerators, microwave ovens, etc.) comprising 330 different makes and models, and collected at 65 different locations in Pittsburgh, Pennsylvania (USA). Additionally, PLAID contains 1314 records of the combined operation of 13 of these appliance types (i.e., measurements obtained when multiple appliances were active simultaneously). Identifying electrical appliances based on electrical measurements is of importance in demand-side management applications for the electrical power grid including automated load control, load scheduling and non-intrusive load monitoring. This paper provides a systematic description of the measurement setup and dataset so that it can be used to develop and benchmark new methods in these and other applications, and so that extensions to it can be developed and incorporated in a consistent manner.
Global urbanization projections suggest that a great majority of human beings will be living in urban areas by the middle of this century. This trend imposes significant strains on urban infrastructure systems and adds additional challenges to achieving environmental, social and economic sustainability goals set by many city governments. Smart city products and services, backed by IoT systems, have been proposed as effective solutions to increase efficiency, reduce costs and improve services. However, as with any technology, IoT solutions for smart cities bring about great opportunities and, at the same time, threats to, among others, governance, security, privacy and community autonomy. As we accumulate experience with these smart city deployments, we must ask ourselves: What would we later regret not regulating now? What good opportunities might certain types of regulation hold back and how can this be mitigated? We offer our perspective on these questions and argue in favor of human-centered IoT systems that are owned, operated and managed much in the same way that other public urban infrastructure systems (e.g., wastewater) are.