Seminar: Applications of Data Driven (Machine Learning) Approach to System Characterization, Diagnostics and Control Optimization

Where: 
Mason Building, Room 1133
When: 
Monday, March 27, 2017 - 13:00

 

Dr. Kincho H. Law
Professor, Civil and Environmental Engineering
Stanford University
 
Abstract:

The purpose of this presentation is to discuss the potential use of machine learning techniques to build data-driven models to characterize an engineering system for performance assessment, diagnostic analysis and control optimization. Focusing on the Gaussian Process modeling approach, engineering applications on constructing predictive models for energy consumption analysis and tool conditioning monitoring of a milling machine tool will be presented. Furthermore, a cooperative control approach for maximizing wind farm power production by combining Gaussian Process modeling with Bayesian Optimization will be discussed.

Biography:

Kincho H. Law received his B.Sc. in Civil Engineering and B.A. in Mathematics from the University of Hawaii in 1976, and M.S. and Ph.D. in Civil Engineering from Carnegie Mellon University in 1979 and 1981, respectively. After serving as Assistant Professor at Rensselaer Polytechnic Institute from 1982 to 1988, he joined Stanford University in 1988 and is currently Professor of Civil and Environmental Engineering. Prof. Law’s professional and research interests focus on computational and information science in engineering. His work has dealt with various aspects of computational engineering; high performance computing; sensing, monitoring and control of complex systems; legal and engineering informatics; enterprise integration; smart manufacturing; web services, cloud and Internet computing. He has authored and co-authored over 400 articles in journals and conference proceedings.

Prof. Law was the recipient of the ASCE Computing in Civil Engineering Award in 2011. He has received a number of best paper awards from ASCE, ASME, IEEE and Digital Government Society; these include Best Paper (on Data Analytics for Advanced Manufacturing) at IEEE Big Data Conference in 2016, Best Paper at the ASME Manufacturing Science and Engineering Conference in 2015, Best Paper in the ASCE Journal of Computing in Civil Engineering in 2014, Best Research Paper (on Resilience and Smart Structures) at the International Workshop on Computing in Civil Engineering in 2013, Best Research and Practice Paper at 6th International Conference on Electronic Governance in 2012, Meritorious Paper at the 4th International Conference on Electronic Governance in 2010, Best Research Paper at the 9th International Conference on Digital Government Research in 2008, and others.