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A.P. Jason de Koning, Ph.D.
Postdoctorate in Computational Genomics and Bayesian Statistics, University of Colorado, Anschutz Medical Campus, Denver CO, USAPh.D. in Molecular Biology (Comparative Genomics), University at Albany (SUNY), Albany NY, USA
Bachelors in Computer Science and Biology, Trent University, Peterborough ON, Canada
Areas of Research
Computational biology, bioinformatics, and genomics
Scalable algorithms, probabilistic models, and data integration approaches for understanding genotype-phenotype relationships and their evolution over time.
Scalable algorithms, probabilistic models, and data integration approaches for understanding genotype-phenotype relationships and their evolution over time.
Systematic variant interpretation in human genetic disease
Phenotype-centric, systematic approaches to variant interpretation in poorly characterized rare genetic disorders. Methods for reducing circularity and increasing generalizability for the training of machine learning classifiers in genomics.
Phenotype-centric, systematic approaches to variant interpretation in poorly characterized rare genetic disorders. Methods for reducing circularity and increasing generalizability for the training of machine learning classifiers in genomics.
Bayesian statistics, large-scale inference, and machine learning
Development of high-performance computing approaches for rapid, scalable Bayesian inference. Explainable AI methods in machine learning.
Development of high-performance computing approaches for rapid, scalable Bayesian inference. Explainable AI methods in machine learning.
Human and computational population genetics
The population genetics of deleterious and disease variation in human populations. Computational methods for the direct interrogation of realistic population genetic models without approximation.
The population genetics of deleterious and disease variation in human populations. Computational methods for the direct interrogation of realistic population genetic models without approximation.
Molecular evolution
Enabling richer biological inferences from across-species sequence comparisons. Development of more realistic probabilistic models describing genome sequence evolution under complex spatiotemporal variation in functional constraint. Non-equilibrium molecular evolution and its consequences. Joint inference from population genetic, phylogenetic, and functional genomics data.
Enabling richer biological inferences from across-species sequence comparisons. Development of more realistic probabilistic models describing genome sequence evolution under complex spatiotemporal variation in functional constraint. Non-equilibrium molecular evolution and its consequences. Joint inference from population genetic, phylogenetic, and functional genomics data.
Supervising degrees
Biochemistry and Molecular Biology - Doctoral: Seeking Students
Biochemistry and Molecular Biology - Masters: Accepting Inquiries
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Working with this supervisor
Seeking excellent doctoral and post-doctoral candidates primarily with computational, mathematical, or statistical backgrounds at this time. Excellent biological trainees with some mathematical or computational expertise are also encouraged to inquire.
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