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  Dr. THOMAS MATARAZZO  
 
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Title : Dr.
First Name : THOMAS
Last Name : MATARAZZO
University/Institution : Massachusetts Institute of Technology
Phone # : 732 567 5849
Email ID : tomjmat@mit.edu
City : Cambridge
Country : United States
State : Massachusetts
Zipcode : 02139
Department : Senseable City Lab
Company Name :
Area of Research
Structural Engineering and Smart Cities
Area of Expertise
Structural Health Monitoring
Brief Description of Research Interest :
The focus of my Ph.D. was to develop a framework for processing mobile sensor data for system identification. From there I started to examine less traditional sensor data formats such as big data or those with missing observations. I spent a year as a postdoc at the Disaster Prevention Research Institute of Kyoto University to better quantify the retrieval of structural information despite data loss and study data-driven methods for post-earthquake structural condition assessments. At the Senseable City Lab, I am working towards using mass smartphone data in cities to quantify infrastructure health.
Representative Publications :

Matarazzo, T.J. & Pakzad, S.N. (2016). “Truncated Physical Model for Dynamic Sensor Networks with Applications in High-Resolution Mobile Sensing and BIGDATA”, (ASCE) Journal of Engineering Mechanics, 04016019.

Matarazzo, T.J. & Pakzad, S.N. (2016). “Structural Identification for Mobile Sensing with Missing Observations”, (ASCE) Journal of Engineering Mechanics, 04016021

Matarazzo, T.J. & Pakzad, S.N. (2016). “STRIDE for Structural Identification using Expectation Maximization: Iterative Output-Only Method for Modal Identification”, (ASCE) Journal of Engineering Mechanics, 04015109.

Matarazzo, T.J. & Pakzad, S.N. (2015). “Sensitivity Metrics for Maximum Likelihood Modal Identification”, (ASCE-ASME) Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering (Invited article in SPECIAL ISSUE on Uncertainty Quantification and Propagation in Structural Systems), B4015002.
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