Moe Khalil
About Moe Khalil
Moe Khalil is a Senior Member of Technical Staff at Sandia National Laboratories and an Adjunct Research Professor at Carleton University's Faculty of Engineering and Design. He specializes in developing probabilistic transfer learning methodologies and applying Bayesian inference algorithms to various fields.
Current Position at Sandia National Laboratories
Moe Khalil currently holds the position of Senior Member of Technical Staff at Sandia National Laboratories. He has been in this role since 2015, bringing his expertise to the San Francisco Bay Area. His work at the laboratory focuses on various advanced research projects in the field of probabilistic transfer learning and Bayesian inference algorithms, with applications in fluid-structure interaction, combustion modeling, and near-shore wave forecasting for energy harvesting.
Adjunct Research Professor at Carleton University
Moe Khalil serves as an adjunct research professor at Carleton University’s Faculty of Engineering and Design. Since 2018, he has been contributing his knowledge and research skills to the institution, working on collaborative research projects in Bayesian inversion, machine learning, data assimilation, and model selection. His commitment to academic partnerships enables him to further the field through innovative research and development efforts.
Educational Background in Engineering and Science
Moe Khalil has an extensive educational background. He earned his Doctor of Philosophy (Ph.D.) in Civil Engineering from Carleton University, completing his studies in 2013. He also holds a Master of Applied Science (M.A.Sc.) in Civil Engineering from the same institution, completed in 2006. Prior to these accomplishments, he received a Bachelor of Engineering (BEng) in Electrical and Electronics Engineering from McGill University in 2004 and a Bachelor of Science (BSc) in Microbiology and Immunology from McGill University in 2000.
Previous Experience and Roles
Moe Khalil has held several significant positions in his career. He worked as a Postdoctoral Research Associate at Sandia National Laboratories from 2013 to 2015. Before that, he was a Research Intern at the Department of National Defence from 2009 to 2011. Khalil also spent time at the Royal Military College of Canada as a Research Assistant from 2007 to 2011. Additionally, he served as a Course Instructor and Research Assistant at Carleton University, contributing to their academic programs from 2004 to 2012.
R&D Efforts in Probabilistic Transfer Learning
Moe Khalil leads a research and development effort aimed at enhancing training methodologies for machine learning and physics-based models. His work focuses on developing novel probabilistic transfer learning techniques capable of functioning in environments characterized by sparse and noisy data. This pioneering research has significant implications for improving model accuracy and reliability in various scientific and engineering applications.