About

Max Ferguson is a doctoral student in the Department of Civil Engineering at Stanford University. He currently studies how recent advances in computer vision can be used to automatically monitor construction progress.

Max grew up in New Zealand where he received Bachelor of Engineering in Civil and Environmental Engineering. In 2014, he was awarded a Fulbright Graduate Fellowship to investigate how computational techniques, such as machine learning, could be applied to structural damage prediction, which became the focus of his work at Stanford University. After completing a M.S at Stanford in 2016, his research focus shifted toward optimization and automation in the manufacturing and construction industries.

Selected Publications

• M. Ferguson, R. Ak, S. Jeong and K. H. Law, "Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning", Smart and Sustainable Manufacturing Systems. (Submitted).


• S. Jeong, M. Ferguson, R. Hou, J. P. Lynch, H. Sohn and K. H. Law, "Sensor Data Reconstruction using Bidirectional Recurrent Neural Network with Application to Bridge Monitoring", Smart Materials and Structures. (Submitted).


• M. Ferguson, R. Bhinge, Yung-Tsun T. Lee and K. H. Law, "A Data Processing Pipeline for Prediction of Milling Machine Tool Condition from Raw Sensor Data", Smart and Sustainable Manufacturing Systems. 2018.


• J. Park, M. Ferguson, K. H. Law, "Data Driven Analytics (Machine Learning) for System Characterization, Diagnostics and Control Optimization", 25th International Workshop on Intelligent Computing in Engineering. Lausanne, Switzerland, Jun 10-13, 2018.


• M. Ferguson, R. Ak, Yung-Tsun T. Lee and K. H. Law, "Automatic Localization of Casting Defects with Convolutional Neural Networks", 2017 IEEE International Conference on Big Data (IEEE BigData 2017). Boston, MA, USA, Dec 11-14, 2017.


• K. H. Law, S. Jeong and M. Ferguson, "A Data-driven Approach for Sensor Data Reconstruction for Bridge Monitoring", 2017 World Congress on Advances in Structural Engineering and Mechanics (ASEM17). Ilsan(Seoul), Korea, August 28 - September 1, 2017.


• M. Ferguson, R. Bhinge, Yung-Tsun T. Lee and K. H. Law", "A Generalized Method for Featurization of Manufacturing Signals, with Application to Tool Condition Monitoring", 37th Computers and Information in Engineering Conference (CIE). Cleveland, OH, USA, August 6-9, 2017.


• J. Park, D. Lechevalier, R. Ak, K. H. Law, M. Ferguson, Yung-Tsun T. Lee, and S. Rachuri, "Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)", Smart and Sustainable Manufacturing Systems. 2017.


• M. Ferguson, K. H. Law, R. Bhinge, D. Dornfeld, J. Park and Yung-Tsun T. Lee, "Evaluation of a PMML-Based GPR Scoring Engine on a Cloud Platform and Microcomputer Board for Smart Manufacturing", 2016 IEEE International Conference on Big Data (IEEE BigData 2016). Washington, DC, USA, Dec 5-8, 2016.

Awards & Scholarships

• CIFE Seed Research Award, Stanford University, 2018
• Blume Reseasrch Fellowship, Stanford University
• Best Paper Award, Manufacturing Symposium, IEEE Big Data 2016
• Fulbright Scholarship, 2014
• Concrete Design Prize, 2013
• Downer Group Engineering Fellowship, 2012
• University of Canterbury Alumni Fellowship, 2010
• Silver Medal (2nd Place), International Young Physicists' Tournament, 2010