Saad Saleh of Shell Development Company (retired)

In almost every branch of the natural and social sciences, one often encounters systems where simple localized rules of interaction among individual units lead to the emergence of complex collective phenomena. Examples range from insect colonies to physical gas laws, and from macroeconomics to the behavior of large human crowds. This observation is naturally also of great interest to a wide range of engineering fields. A substantial amount of interdisciplinary research within the last forty years has been motivated by a desire to understand these emergent complex systems and to utilize them whenever appropriate for designing efficient engineering systems such as multi-agent robots and drones.

In this talk, we focus on one particular line of research within this area: collective motion. This macroscopic phenomenon typically emerges in large multi-particle systems by invoking a few simple alignment and consensus rules locally within small radii of influence around each individual. To this end, we review various attempts in the research literature at numerically simulating the collective motion of bird flocks and fish schools, and highlight the role played by basic physical concepts such as phase transitions, statistical mechanics, and scale-free correlations. In addition, we shed light on a number of contributions from the field of automatic control theory where several researchers have provided solid theoretical foundations in the last few years for certain consensus-based observations by formulating the problem within a feedback dynamic system framework and employing technical results from network theory and related disciplines. Finally, we review a few open research problems and suggest potential lines of attack to address them.

Biography:

Saad Saleh received a PhD degree in electrical and computer engineering from the University of Wisconsin-Madison in 1991 in the area of robust control theory. He joined Shell Development Company in Houston in 1991 and served as a research engineer until 2003 focusing on advanced applications of digital signal processing and systems theory to seismic data processing and interpretation, including data compression, noise attenuation, statistical pattern recognition for direct hydrocarbon detection, and novel applications of multi-dimensional hexagonal sampling to speed up seismic imaging. From 2004 to 2008, Saad led the New Detection Methods R&D team in Shell International E&P Inc in developing and deploying new geophysical exploration techniques such as controlled-source electromagnetics and high-resolution gravity and magnetic capabilities. From 2009 to 2018, he managed Shell’s Integrated Geoscience R&D program, a large multi-disciplinary team focused on creating new exploration technologies via the application of recent breakthroughs in computational science and machine learning to integrate the latest advances in geology, geophysics, and petrophysics. Since retiring from Shell in 2018, Saad’s research interests have revolved around emergent complex systems and their applications in engineering and the natural sciences.