Members from 2006-2015

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Dr. Thomas Vincent joined the MezeyLab as a Postdoctoral Associate at Weill Cornell Medicine after receiving degrees in Mathematics and Physics and then a PhD in Biostatistics from the University of Bristol (UK) where he worked on machine learning approaches for predicting peptide oligomer coiling.  He is currently Vice President of  Data Science at Getty Images.  While in the MezeyLab, he applied his machine learning skills to the problem of modeling changes in transcriptome profiles over time and to the learning of biomarkers from mixed omics data types. His publications include papers in Bioinformatics, the American Journal of Respiratory and Critical Care Medicine (the Blue Journal), the International Journal of Biochemistry and Cell Biology, the Journal of Biochemical Chemistry, among others.  

Dr. Gabriel (Gabe) Hoffman joined the MezeyLab as a Cornell University PhD student after completing his Bachelors of Science at the University of Maryland.  He is currently an Assistant Professor in Genetics and Genomics Sciences at the Icahn School of Medicine at Mount Sinai, where his work is focused on developing methods for statistical analysis, causal modeling, and machine learning to study psychiatric diseases when analyzing combined genomic, brain imaging, and clinical data. His PhD graduate work focused on developing frameworks for applying penalized Generalized Linear Models (GLMs)  and methods for optimal kernel selection in Mixed Models with fast associated algorithms for identifying genetic associations in Genome-Wide Association Studies (GWAS).  His publications include papers in Science, Nature Neuroscience, Nature Genetics, Cell Stem Cell, PLoS Computational Biology, Bioinformatics, among many others.

Pavel received his undergraduate degree in computer science at Mansfield University and his Masters degree from the St. Petersburg Electro-Technical University (Russia) and worked in a number of industries before joining the MezeyLab as a Senior Scientific Programmer.  In the MezeyLab he built pipelines for processing raw genotyping and other genomic data and for performing genome-wide association study (GWAS) analysis, as well as assisting with a number of collaborative projects.  Since leaving the MezeyLab, he continued working at Cornell with Professor Amnon Koren building pipelines for a RNA processing and analysis.  He has since joined Corning as a Senior Measurements Engineer.

Dr. Nicole Tignor joined the MezeyLab as a Postdoctoral Associate at Weill Cornell Medicine after receiving her PhD at the New York State University at Stony Brook.  She is currently an Assistant Professor at Icahn School of Medicine at Mount Sinai where her work is focused on developing machine learning algorithms for digital health.  While in the MezeyLab, she applied her computational statistics and scientific programming skills to develop methods for efficient multivariate mixed latent variable modeling of transcriptome data, as well as to a number of collaborative gene therapy projects.  Her publications include papers in Scientific Data, Bioinformatics, Human Gene Therapy, among many others.

Dr. Chuan Gao joined the MezeyLab as a Cornell University PhD student in the Computational Biology program.  He is currently a Principal Scientist and statistician at Bristol Myers Squibb.  His PhD graduate work focused on developing frameworks for learning latent factors from highly multivariate data and mapping of expression Quantitative Trait Loci (eQTL).  During his PhD, he developed HEFT (Hidden Factor Expression analysis), a multivariate regression and unspecified factor model with a ridge penalty for analyzing eQTL and he was involved in a number of collaborative projects.  His publications include papers in Bioinformatics, Genome Research, Molecular Vision, and PLoS One, among others.

Dr. Anthony (Tony) Greenberg joined joined the MezeyLab as a Postdoctoral Associate at Cornell University.  Upon leaving the MezeyLab, Dr. Greenberg founded the consulting and data analysis service company Bayesic Research, which develops statistical methods and software to interpret genetic data.  While in the MezeyLab he applied his computational statistics and programming skills to the development of a fully Bayesian multivariate, multiple generalized linear modeling framework with flexible priors for Genome-Wide Association Study (GWAS) analysis in any experiment design and an associated efficient Markov chain Monte Carlo (MCMC) inference algorithm that he applied to a number of collaborative projects, including several rice genomics projects performed in collaboration with Dr. Susan McCouch.  His publications include papers in Science, Nature, Nature Communications, PLoS Genetics, Molecular Biology and Evolution, Frontiers of Plant Science, and Genetics.

Dr. Rami Mahdi joined the MezeyLab as a Postdoctoral Associate at Weill Cornell Medicine after receiving his PhD from the University of Louisville in Computer Science.  He is currently a Senior Software Engineer at Google where his work includes machine learning research on search ranking and spam fighting.  While in the MezeyLab, he applied his machine learning and algorithm skills to the development of efficient Bayesian Network algorithms and learning network relationships from transcriptome data for the purposes of target ranking.  His publications include papers in the Journal of Machine Learning Research and Bioinformatics among others.

Dr. Larsson Omberg joined the MezeyLab as a Postdoctoral Associate at Cornell University after receiving his PhD in Physics at the University of Texas at Austin.  He is currently Vice President of Systems Biology at Sage Bionetworks where he leads a group dedicated to large collaborative efforts in Genomics and Digital Health.  His research focuses on using remote sensors and mobile phones to measure disease; and collaborative genomic research.  His work actively uses open and team based science to get a large number of external partners to collaborate on data intensive bioinformatics and data science problems.  This includes establishing norms and methods for measuring disease phenotypes using remote sensors and developing analytical approaches for turning raw signals from sensors into digital biomarkers.  While at the MezeyLab, he worked on a number of machine learning and statistics problems related to population genetics and pulmonary disease.  He has authored over 75 publications and has published in Nature Genetics, Nature Biotechnology, Cell , among many others.  

Dr. Benjamin (Ben) Logsdon joined the MezeyLab as a Cornell University PhD student in the Computational Biology program after completing his undergraduate degree in Biochemistry at Washington State University.  He is currently Director of Computational Biology at Cajal Neuroscience, where he is directing machine learning, statistical, and bioinformatics research aimed at accelerating the entire Cajal neurodegenerative disease treatment portfolio.  His PhD graduate work focused on developing causal modeling and mixture prior Probabilistic Graphical Model network recovery algorithms including Variational Bayes algorithms scalable to high dimensional data and he was involved in a numerous collaborative projects.  He developed multiple algorithms for generalized linear models and probabilistic graphical models for analyzing mixed genomic types to identify genetic and molecular network features important for a variety of diseases.  During his graduate career, he was a Cornell Presidential Life Sciences fellow and received numerous awards.  His publications include papers in Nature Neuroscience, Nature Genetics, American Journal of Human Genetics, PLoS Genetics, Nucleic Acids Research, Bioinformatics, BMC Bioinformatics, and PLoS Computational Biology, among many others.

Gerry Lorigan joined the MezeyLab as a Cornell University PhD student in the Genetics and Development program after completing his undergraduate degree in Genetics at Trinity College, Dublin. For his Master's degree graduate work, he developed molecular and statistical approaches for quantitatively analyzing molecular pathways.  During his graduate career, he also worked to set up initial lab pipelines and was involved in several collaborative projects.  His publications include a paper in the Journal of Experimental Zoology, among others.

Dr. Abra Brisbin joined the MezeyLab as a Cornell University PhD student in the Applied Mathematics program after completing her undergraduate degree in Mathematics from Carleton College.  She is currently a tenured Associate Professor at the University of Wisconsin at Eau Claire.  Her PhD graduate work focused on developing methods for performing Bayesian Linkage Analysis on complex pedigrees and accompanying efficient Markov chain Monte Carlo (MCMC) algorithms, and she was involved in a number of collaborative projects.  Among other methods, she developed LOCate (Linkage analysis of Ordinal and Categorical traits), a Bayesian Linkage Analysis method for discrete disease phenotypes making use of a simulated tempering MCMC algorithm for efficient inference.  Her publications include papers in Statistical Applications in Genetics and Molecular Biology, Frontiers in Genetics, Human Biology, PLoS One, and Genetic Epidemiology, among others.

 Fangfei received her Doctor of Medicine in China and joined the MezeyLab as a Senior Bioinformatics Technician. She is currently a Bioinformatics Technician in the Duke Genomics Center.  In the MezeyLab she was responsible for managing and collecting genomic and image data for a complex phenotype and pathway Drosophila model system, setting up and managing the initial compute and data collection systems for the MezeyLab, and was additionally involved in numerous collaborative projects.