Karl Gemayel

Karl Gemayel has worked in the field of machine learning and data science since 2011. From 2011 to 2013, they worked as a Research Assistant at the American University of Beirut, researching and implementing various data structures to be used in large-scale polynomial arithmetic. From 2013 to 2021, they worked as a Graduate Research Assistant at the Georgia Institute of Technology, where they developed an unsupervised machine learner based on hidden semi-Markov models and Gibbs sampling to better predict locations of genes in DNA. Since 2021, they have been employed at Deep Genomics as a Research Scientist, where they have been discovering new gene regulatory mechanisms, developing deep learning models that design oligonucleotides targeting these mechanisms, leading work on pipeline for efficiently evaluating deep learning model performance for drug design, and serving as computational lead for a therapeutics drug program. In 2023, they joined Relation Therapeutics as Associate Director of Machine Learning.

Karl Gemayel completed their Doctor of Philosophy (PhD) and Master's degree in Computational Science and Engineering from the Georgia Institute of Technology in 2020. Prior to that, they obtained their Master's degree in Computer Science from the University of Oxford in 2013. Karl also earned their Bachelor of Science (B.S.) in Computer Science from the American University of Beirut between 2010 and 2012.