2021 International Conference on Intelligent Manufacturing and Industrial Automation
Prof. Mo Jamshidi



M. Mo Jamshidi is an F-IEEE, F-ASME, AF-AIAA, F-AAAS, F-TWAS F-NYAS. He received BSEE (Cum Laud) at Oregon State University in 1967, the MS and Ph.D. in EE from the University of Illinois at Urbana-Champaign in June 1969 and February 1971, respectively. He holds honorary doctorate degrees from the University of Waterloo, Canada, 2004, Technical University of Crete, Greece, 2004 and Odlar Yourdu University, Baku, Azerbaijan in 1999.  Currently, he is the Lutcher Brown Endowed Distinguished Chaired Professor at the University of Texas, San Antonio, TX, USA. He is currently involved in research on system of systems engineering with emphasis on robotics, drones, biological and sustainable energy systems, machine learning and AI applications in control and navigation.. He has over 12,000 citations on Scholar Google.  

Speech title: 

Complex System of Systems Engineering Paradigm for Smart Cities


Large data has been accumulating in all aspects of our lives for quite some time. Advances in sensor technology, the Internet, wireless communication, and inexpensive memory have all contributed to an explosion of “Big Data”. System of Systems (SoS) are integration of independent operatable and non-homogeneous legacy systems to achieve a higher goal than the sum of the parts. Today’s SoS are also contributing to the existence of unmanageable “Big Data”. Recent efforts have developed promising approach, called “Data Analytics”, which uses machine learning tools from statistical and soft computing (SC) such as principal component analysis (PCA), clustering, fuzzy logic, neuro-computing, evolutionary computation, Bayesian networks, deep architectures and deep learning, etc. to reduce the size of “Big Data” to a manageable size and apply these tools to a) extract information, b) build a knowledge base using the derived data, and c) eventually develop a non-parametric model for the “Big Data”. This keynote attempts to construct a bridge between SoS and Data Analytics to develop reliable models for such systems.  A photovoltaic energy-forecasting problem of a micro grid SoS, traffic jams forecasting and a system of autonomous vehicles will be offered for case studies. These tools will be used to extract a nonlinear model for a SoS-generated BIG DATA. Videos for autonomous vehicles will be shown.