Sleiman Bassim
About Sleiman Bassim
Sleiman Bassim is a Senior Bioinformatics Scientist with over a decade of experience in biomarker discovery and risk prediction for complex diseases. He holds multiple advanced degrees in Genome Sciences and has worked at several prestigious institutions, including New York University and University Health Network.
Work at My Intelligent Machines
Sleiman Bassim has been employed at My Intelligent Machines as a Senior Bioinformatics Scientist since 2020. In this role, he applies his extensive knowledge in bioinformatics to contribute to projects focused on biomarker discovery and risk prediction for complex diseases. His work involves utilizing advanced data integration techniques and performance computing to enhance research outcomes.
Education and Expertise
Sleiman Bassim holds multiple degrees in the field of Genome Sciences and Bioinformatics. He earned a Doctor of Philosophy (PhD) in Genome Sciences/Genomics from Université du Québec à Rimouski, completing his studies from 2009 to 2014. He also obtained a PhD in the same field from Université de Bretagne Occidentale between 2010 and 2013. His educational background is complemented by a Master of Science in Cell/Cellular and Molecular Biology from Université Henri Poincaré, Nancy 1, and a Bachelor of Applied Science in Animal Biology from Lebanese University.
Background
Sleiman Bassim has over a decade of experience in the field of bioinformatics, focusing on biomarker discovery and risk prediction for complex diseases. He has worked in various research capacities, including as a Research Associate at the University of Montpellier and as a Postdoctoral Research Associate at New York University. His career also includes a position as a Senior Data Scientist and Machine Learning Engineer at University Health Network.
Achievements
Sleiman Bassim has developed specialized skills in DNA/RNA sequencing pipelines and machine learning, contributing to advancements in systems biology and gene networks. He has expertise in data warehousing, heterogeneous data integration, and automation through batch scripting. His work spans over 15 years, focusing on key organisms and non-model species, which enhances his contributions to the field.