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 (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.


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



Towards Real-world Reinforcement Learning for Building Control

The project aims to develop enabling methods for practical deployment of reinforcement learning for building control.

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 to improve operational efficiency, increase autonomy, and enhance global performance objectives.

Electricity Disaggregation

In this project we focus on a specific class of unsupervised algorithms based on deep learning techniques that can learn instantaneous power waveforms for individual devices as well as their activation patterns given sufficient data from a single meter.

Estimating Climate Change Impacts on Water and Electric Power Infrastructure in the Southeast U.S.

In this project we focus on estimating the effects of climate change on electric power systems.

GridBallast: Autonomous Load Control For Grid Resilience

The GridBallast project will create low-cost demand-side management technology to address resiliency and stability concerns accompanying the growth of distributed energy resources.

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 efficiency of HVAC and lighting systems based on a novel depth-imaging occupancy sensor.

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 efficiency of HVAC and lighting systems based on a novel depth-imaging occupancy sensor.

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 associated with sensors and actuators.

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 methane leak detection surveys.

Understanding Electricity Demand Patterns Coupled With On-Site Solar Generation

In this project we study the potential of existing datasets to provide information necessary for decision-making in different contexts: from solar home systems for low-income rural residents in Africa, to utility net-metering datasets from United States households.

Meet the Team

Principal Investigators


Mario Bergés

Professor of Civil and Environmental Engineering

Machine Learning, Statistical Inference, Smart Infrastructure

Graduate Students


Bingqing Chen

PhD Student

Building Systems, Smart Grid, Reinforcement Learning, Distributed Optimization


Jingxiao Liu

PhD Student

Structural Health Monitoring, System Identification, Machine Learning


Byeongseong Choi

PhD Student

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


Elvin Vindel

PhD Student

Demand Flexibility, HVAC Control, Building Energy, Renewable Energy


Laura Simandl

PhD Student

Resilient Building Structures & System Design, Smart Infrastructure, Innovative Construction Materials & Material Testing


Min Hwang

Masters Student

Smart Cities & Infrastructures, Structural Health Monitoring, Data Science


Ronald S. Holt

PhD Student

Applied Deep Learning, Signal Separation, Data Science, Sustainability



Kaiwen Zhang

Independent Study Researcher

Smart Cities, Renewable Energy Applications



Francisco Fonseca

Research Associate

Energy Systems, Operations Research, Data Science, Machine Learning


Henning Lange


Machine Learning, Variational Inference, Energy Systems


Jingkun Gao

Senior Machine Learning Engineer

Building Automation Systems, Statistical Inference, Semantic Technologies


Jerry Lei

Postdoctoral Research Associate

HVAC Control Logic, Software Testing, Building Automation Systems


Xuesong (Pine) Liu

Co-founder and CEO

HVAC Systems, Information Modeling, Building Automation Systems, Fault Detection and Diagnosis


Emre Can Kara

VP of Engineering

Vehicle Electrification, Data Science, Machine Learning


In-Soo Jung

Senior Data Scientist & Team Lead

Structural Health Monitoring, Data Mining, Dimensionality Reduction Techniques


Matineh Eybpoosh

Co-Founder & CEO

Energy Storage, Data Science, Machine Learning


Suman Giri

Head of Platform Development

Non-Intrusive Load Monitoring, Data Science, Healthcare Analytics

Past Visitors


Alan Mazankiewicz

Visiting Scholar

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


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