James Garrett
Provost and Chief Academic Officer | Carnegie Mellon University
Pittsburgh, PA, UNITED STATES
James Garrett is instrumental in institutional and academic planning and implementation
Biography
James H. Garrett Jr. is the provost of Carnegie Mellon University. Prior to being named provost, Garrett served as dean of the College of Engineering from January 2013 - December 2018. He is also the Thomas Lord Professor of Civil and Environmental Engineering at Carnegie Mellon University. He was head of the Department of Civil and Environmental Engineering from June 2006-December 2012. Until 2013, he was the founding co-director of the Pennsylvania Smarter Infrastructure Incubator, which was a research center aimed at creating and evaluating sensing, data analytics, and intelligent decision support for improving the construction, management and operation of infrastructure systems. Garrett also served as the co-chief editor of the ASCE Journal of Computing in Civil Engineering from 2008-2013. Prior to becoming head, Garrett served as associate dean for Academic and Graduate Affairs from 2000-2006. Garrett joined Carnegie Mellon at the rank of assistant professor in 1990, was promoted to associate professor in 1993, and promoted to the rank of full professor with tenure in 1996. Prior to joining Carnegie Mellon, Garrett served at the rank of assistant professor in the Department of Civil Engineering at the University of Illinois at Urbana-Champaign from 1987-1990. Garrett received his B.S. in Civil Engineering in 1982, his M.S. in Civil Engineering in 1983 and his Ph.D. in Civil Engineering in 1996, all from the Department of Civil Engineering at Carnegie Mellon University. He worked for Schlumberger Well Services in the Houston Downhole Sensors Division from 1986-87. He is a registered professional engineer in the state of Texas.
Garrett's research and teaching interests are oriented toward applications of sensors and sensor systems to civil infrastructure condition assessment; applications of data mining and machine learning techniques for infrastructure management problems in civil and environmental engineering; mobile hardware/software systems for field applications; representations and processing strategies to support the usage of engineering codes, standards, and specifications; knowledge-based decision support systems. Garrett has published his research in over 60 refereed journal articles, over 80 refereed conference papers, over 90 other conference papers and 10 sections or chapters in books or monographs.
Areas of Expertise (5)
Smart Infrastructure
Remote Sensing
Civil Engineering
Data Mining
Signal Processing
Media Appearances (4)
New dean joins Carnegie Mellon science college
Trib Live online
2024-10-01
In announcing the appointment, Provost James Garrett Jr. pointed to her scholarly career and ability to lead. “Her distinguished research background and proven leadership skills position her to propel the future of science initiative forward and guide MCS toward even greater heights.” he said in a statement.
Carnegie Mellon boosts tuition to $64K per year
TribLive online
2024-02-12
The new $64,596 yearly tuition was adopted by school trustees and outlined in a message to the campus this month from Provost James H. Garrett Jr.
CMU raises undergraduate tuition, financial aid by 3.72%
The Tartan online
2024-02-12
On Feb. 1, all Carnegie Mellon undergraduate students were notified that next year’s undergraduate cost of attendance would be raised by 3.72 percent to $64,596. In an email statement, Jim Garrett, provost and chief academic officer for the university, noted that the annual tuition adjustment for the Academic Year 2024-25 was the product of an “expenditures and inflation” analysis. The annual U.S. inflation rate for 2023 was 3.4 percent, according to U.S. Labor Department data.
CMU Provost Appointed to Second Five-Year Term
Carnegie Mellon University News online
2024-01-10
Carnegie Mellon University President Farnam Jahanian has announced the reappointment of James H. Garrett Jr. to a second five-year term as provost and chief academic officer.
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Industry Expertise (2)
Research
Education/Learning
Accomplishments (3)
Alexander von Humboldt Research Prize (professional)
2012
Steven J. Fenves Award for Systems Research (professional)
2007
ASCE Computing in Civil Engineering Award (professional)
2006
Education (3)
Carnegie Mellon University: Ph.D., Civil Engineering 1986
Carnegie Mellon University: M.S., Civil Engineering 1983
Carnegie Mellon University: B.S., Civil Engineering 1982
Affiliations (2)
- Pennsylvania Smart Infrastructure Incubator
- Wilton E. Scott Institute for Energy Innovation
Links (7)
Patents (3)
Integrated information framework for automated performance analysis of heating, ventilation, and air conditioning (HVAC) systems
US9429960B2
2016-08-30
A computer-implemented method includes, in one aspect, retrieving HVAC system information; converting the retrieved HVAC system information from one or more first data formats to a second data format; storing the converted HVAC system information; identifying first portions of the stored HVAC system information that pertain to a particular component; and generating one or more associations among the identified, first portions of the stored HVAC system information; receiving a request for HVAC system information that is used by a performance analysis algorithm; determining a type of component of the HVAC system that is related to the requested HVAC system information; and identifying one or more items of the stored HVAC system information that is of the requested HVAC system information; and transmitting the identified one or more items of the stored HVAC system information for use in execution of the performance analysis algorithm.
System to enable rail infrastructure monitoring through the dynamic response of an operational train
US10351150B1
2019-07-16
This invention relates to a vehicle-based infrastructure diagnostic apparatus to assist in the maintenance of rail networks. This is a complete system—from the acquisition of vibration signals in a moving vehicle, to the presentation of the infrastructure changes to the maintenance workers. This apparatus relies on novel techniques to account for the variable speed of the train (making it useful even on non-dedicated rail vehicles) and novel techniques to normalize for various environmental conditions. This system could help maintenance departments identify and remediate anomalies more rapidly, as well as trace network deterioration over time. The apparatus consists of low-cost unobtrusive sensors, software for data analysis, and hardware that makes it easy to deploy widely even on existing rail networks.
Neural network-based vehicle detection system and method
US5448484A
1995-09-05
The present invention is directed to a neural network-based system for detecting the presence of a vehicle within a traffic scene. The vehicle detection system comprises an apparatus for producing an image signal representative of an image of the traffic scene and a trainable neural network for identifying the presence of a vehicle within the traffic scene. The present invention is also directed to a method for detecting the presence of a vehicle within a traffic scene. The vehicle detection method includes the steps of producing an image signal representative of an image of the traffic scene, collecting a training set of these image signals, training a neural network from this training set of image signals to correctly identify the presence of a vehicle within the traffic scene and performing surveillance of the traffic scene with the trained neural network to detect the presence of a vehicle.
Articles (5)
Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh
Scientific Data2019 We present DR-Train, the first long-term open-access dataset recording dynamic responses from in-service light rail vehicles. Specifically, the dataset contains measurements from multiple sensor channels mounted on two in-service light rail vehicles that run on a 42.2-km light rail network in the city of Pittsburgh, Pennsylvania. This dataset provides dynamic responses of in-service trains via vibration data collected by accelerometers, which enables a low-cost way of monitoring rail tracks more frequently.
Detecting anomalies in longitudinal elevation of track geometry using train dynamic responses via a variational autoencoder
Proc. SPIE 10970, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 20192019 Track geometry is one of the most important health indices in the maintenance of rail tracks. Visual inspection and inspection using a track-geometry car are two common approaches to inspect track geometry. Presently, using accelerations from in-service trains has become a popular track inspection approach, because it is a low-cost way to monitor the rail tracks more frequently. However, due to the noise presented in the collected accelerations, detecting anomalies using manually designed features often results in many false alarms.
Algorithms for automated generation of navigation models from building information models to support indoor map-matching
Automation in Construction2016 Navigation models are explicit representations of geometrical and topological information of physical environments that can be utilized for map-matching of indoor positioning data. This research paper presents algorithms for automated generation of three different types of navigation models, namely, centerline-based network, metric-based and grid-based navigation models, for map-matching of indoor positioning data. The abovementioned navigation models have been generated in an automated fashion from Industry Foundation Classes (IFC)-based building information models (BIM).
Exploration and evaluation of AR, MPCA and KL anomaly detection techniques to embankment dam piezometer data
Advanced Engineering Informatics2015 In the U.S., the current practice of analyzing the structural integrity of embankment dams relies primarily on manual a posteriori analysis of instrument data by engineers, leaving much room for improvement through the application of advanced data analysis techniques. In this research, different types of anomaly detection techniques are examined in an effort to propose which data analytics are appropriate for various anomaly scenarios as well as piezometer locations.
Effects of Positioning Data Quality and Navigation Models on Map-Matching of Indoor Positioning Data
Journal of Computing in Civil Engineering2014 With rising complexity of indoor environments and growing demand for positioning and tracking of people (such as occupants and field workers) indoors, there has been an increasing need to have accurate and reliable indoor positioning. In this paper, the effects of (1) the quality of positioning data and (2) the types of navigation models on the accuracy of map-matching of indoor positioning data are evaluated. Sensitivity analyses on the quality of two different types of positioning data, namely, (1) absolute point-positioning data and (2) relative point-positioning data have been carried out.
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