Institute Lecture on "Reimagining Systems Science in the era of AI and ML"
Academic Event
 Dr. Sirish Shah, PhD, FCAE, FCIC, FIEEE, Emeritus Professor, Department of Chemical and Materials Engineering, University of Alberta

Room No.22, second floor, VMCC, IIT Bombay, Powai, Mumbai 400076

The Indian Institute of Technology Bombay is organizing an Institute lecture on February 7, 2024.
The details of the lecture are provided below:

Title: "Reimagining Systems Science in the era of AI and ML"

Speaker: Dr. Sirish Shah, PhD, FCAE, FCIC, FIEEE, Emeritus Professor, Department of Chemical and Materials Engineering, University of Alberta

About the speaker:
Dr. Sirish L. Shah has been with the University of Alberta since 1978, where he held the NSERC-Matrikon-Suncor-iCORE Senior Industrial Research Chair in Computer Process Control from 2000 to 2012. He is the recipient of the Albright & Wilson Americas Award of the Canadian Society for Chemical Engineering (CSChE), the Killam Professor in 2003, the D.G. Fisher Award of the CSChE for significant contributions in the field of systems and control, the ASTECH award in 2011, the 2015-IEEE Transition to Practice award and the 2017 RS Jane award of the CSChE.  He has held visiting appointments at Oxford University and Balliol College as a SERC fellow, Kumamoto University (Japan) as a senior research fellow of the Japan Society for the Promotion of Science (JSPS), the University of Newcastle, Australia, IIT-Madras, India and the National University of Singapore. The main areas of his current research are process data analytics for process performance monitoring and analysis and rationalization of alarm systems. He has co-authored three books, the first titled, Performance Assessment of Control Loops: Theory and Applications, a second titled ‘Diagnosis of Process Nonlinearities and Valve Stiction: Data Driven Approaches” and a more recent monograph on “Capturing Connectivity and Causality in complex industrial processes”. He is an Emeritus Professor at the University of Alberta, a fellow of the Canadian Academy of Engineering, the Chemical Institute of Canada and the IEEE.

With the explosion of applications of analytics in diverse areas (such as aircraft engine prognosis, battery materials for EV and energy storage, agriculture, drug discovery in medicine, sports, finance, social sciences and the advertising industry) machine learning skills are in high demand.  Industry and institutions are awash with all types of data archived over many years. Examples include: sensor data, binary alarm data with operator actions (categorical data) to ‘navigate’ the process to operate at desired conditions; image data that can be used to identify promising materials and design strategies for developing next-generation batteries; satellite imagery data for use in the design of precision irrigation systems. The fusion of information from such disparate sources of data is the key step in devising strategies for smart analytics platforms for the next generation of tools.  The focus in this seminar will be a simple introduction of tools and techniques that help in the process of analyzing and discovering information from data from operating processes, agriculture science, physical laws, chemistry and biology. Overall, AI and ML have the potential to revolutionize various industries, including agriculture, battery development for energy storage, and developing screening tools to identify diseases from stained slides and classify metal microstructures that lead to better welds and joins. By leveraging the power of these technologies, manufacturers and engineers can optimize their processes and achieve higher levels of efficiency, productivity, and quality leading to the design of more efficient, sustainable, and reliable systems