Joseph Bielawski
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PROFESSOR BSc (Southampton) MA (Hofstra) PhD (Texas A&M) Post doctoral fellow: University College London, UK |
Teaching & Research adaptive evolution, molecular evolution, molecular phylogenetics, genomics
I am broadly interested in statistical modeling of genes, genomes and metagenomes. My research group employs models to investigate the process of functional divergence from the gene level to the metagenome level. My current work involves novel Bayesian models dedicated to metagenome data. Research interests: Statistical modeling of molecular evolution: Research within this field is focused on (1) improving Markov models of codon and protein evolution, (2) assessment of model performance, and (3) combining such models with data mining methods for the purpose of genome-scale data analysis. The majority of the work has been with codon models, and my work in this area has had considerable influence on how statistical methods are used to detect adaptive molecular evolution. Microbial meta-genomics and eco-genomics: Research in this area utilizes statistical modeling to investigate the process of functional divergence in microbes. This research is driven by biological issues, and takes full advantage of both the power provided by the growing numbers of complete genomes from closely related prokaryotes and the large-scale datasets for uncultivable microorganisms that are being generated via environmental genomic methods. Current efforts are devoted to building novel Bayesian models for analyzing (1) microbial community structure and (2) the latent metabolic capacity of microbial communities. Model development is driven by pressing questions in the fields of human micorbiomics and marine microbial metagenomics. |
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am an associate professor cross-appointed in the Departments of Biology (75%) and Mathematics & Statistics (25%) at 麻豆传媒 (Halifax, NS). My PhD is in Genetics, from Texas A&M University, and I did my posdoctoral training at the University College London (UCL) under Dr. Ziheng Yang in the area of computational molecular evolution.
