Muammar is a principal scientist applying machine learning to solve problems in drug discovery at Bristol-Myers-Squibb. His research interests include feature extraction, deep learning, and software solutions.
He is a chemist by training from the University of Zulia in Venezuela and started his graduate studies with a European Master in Theoretical Chemistry and Computational modeling of the Erasmus Mundus Program. His Ph.D. in theoretical chemical physics was about the characterization of metallic and insulating properties of low-dimensional systems using the theory of the insulating state of Walter Kohn applied with wave function theory.
He was a postdoctoral research associate at Brown University, where he worked in the acceleration of atomistic simulations with machine learning models in the group of Prof. Andrew A. Peterson in the Catalyst Design Laboratory. He acquired experience with neural networks and kernel ridge regression models to mimic quantum mechanics simulations using interatomic machine learning potentials in this appointment.
At Lawerence Berkeley National Laboratory, he was a postdoctoral scholar working towards the development of machine learning approaches, algorithms and data sets to solve chemical science problems.
He has published more than ten papers, given presentations at international conferences, and developed the ML4Chem machine learning package, a module for the MOLPRO quantum-chemistry package, and the atomistic machine-learning package (Amp). Additionally, he has participated in the free software community and is a Debian Linux developer.
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PhD in Theoretical Chemical Physics, 2015
Université Paul Sabatier, France
Master in Theoretical Chemistry and Computational Modeling, 2012
Université Paul Sabatier, France
BSc in Chemistry, 2010
University of Zulia, Venezuela
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Responsibilities include:
Responsibilities include:
Responsibilities include: