Pouyan Khakbaz, Ph.D.
About Pouyan Khakbaz, Ph.D.
Pouyan Khakbaz, Ph.D., is a Computational Drug Designer at Bristol Myers Squibb in Cambridge, Massachusetts, with over 8 years of experience in computational chemistry and data analysis.
Current Position at Bristol Myers Squibb
Pouyan Khakbaz, Ph.D., is currently working at Bristol Myers Squibb as a Computational Drug Designer. He holds this position in Cambridge, Massachusetts, United States. His role involves leveraging computational chemistry and advanced data analysis to support drug discovery projects.
Previous Experience at Cargill
Pouyan Khakbaz worked at Cargill in Minneapolis, Minnesota, United States, starting as a Computational Chemist from 2020 to 2021 for 11 months. He was later promoted to Senior Computational Chemist, although he held this position for only 2 months in 2021. During his tenure, he applied computational chemistry techniques to solve complex chemical problems.
Academic Positions at University of Illinois and University of Maryland
Pouyan Khakbaz served as a Postdoctoral Fellow at the University of Illinois at Urbana-Champaign from 2018 to 2020. Prior to this, he was a Research Assistant at the University of Maryland for five years, from 2013 to 2018. His work in these roles focused on advanced computational methods and their applications in chemistry and biophysics.
Educational Background
Pouyan Khakbaz achieved his Doctor of Philosophy (Ph.D.) in Chemical Engineering with a focus on Biophysics from the University of Maryland College Park, where he studied from 2013 to 2018. He also holds a Master’s Degree in Biochemical Engineering from the University of California, Irvine, received in 2013 after studying there for two years. Prior to that, he completed his Bachelor’s Degree in Chemical Engineering from Sharif University of Technology in 2011.
Experience in Computational Chemistry and Drug Discovery
Pouyan Khakbaz has over 8 years of experience in computational chemistry and data analysis across both academic and industrial settings. He is highly motivated in utilizing data science applications, such as artificial intelligence and machine learning methods, to address complex challenges in drug discovery projects. His career reflects a strong interest in transforming theoretical research into practical applications in the field of drug design.