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Saad Saleh

Professor of Practice

Dr. Saleh received a PhD in electrical and computer engineering from the University of Wisconsin-Madison in 1991 in the area of feedback control theory. He joined Shell Development Company in Houston in 1991 and served as a research engineer until 2003 focusing on applications of advanced digital signal processing and systems theory to seismic data processing and interpretation. Research areas included statistical pattern recognition, data compression, noise attenuation, and novel applications of multi-dimensional hexagonal sampling to speed up seismic imaging.

From 2004 to 2008, Dr. Saleh led the New Detection Methods R&D team in Shell International E&P Inc. with responsibility for developing and deploying new geophysical techniques beyond traditional seismic methods. Emphasis was focused on controlled-source electromagnetics, remote sensing systems, and high-resolution gravimetric and magnetic capabilities. From 2009 to 2018, Dr. Saleh was General Manager of Shell’s Integrated Geoscience R&D program, a large international multi-disciplinary team focused on creating new exploration technologies via the application of computational science and machine learning methods to integrate advances in geology, geophysics, and petrophysics. The program was also responsible for developing deep-water natural seep detection capabilities using autonomous underwater vehicles.

After retiring from Shell, Dr. Saleh joined Rice University in Houston as Professor in the Practice, where his teaching and research interests were focused on complex emergent systems and their applications in engineering and the natural sciences. In 2021, Dr. Saleh joined the Bureau of Economic Geology at the University of Texas in Austin, where he consults on developing new applications of subsurface imaging technologies to enable opportunity identification for renewable energy and the future low-carbon economy. In addition, Dr. Saleh joined the Chandra Family Department of Electrical and Computer Engineering at the University of Texas in Austin in 2023 as Professor of Practice, where his research and teaching interests are focused on control theory, multi-agent dynamic networks, machine learning, and engineering design methodologies.

Research Interests
Feedback Control Theory
Signal Processing
Machine Learning
Earth Science Applications