Members from 2016-2022

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Dr. Mahboubeh (Roofya) Rostami joined as a postdoctoral associate and has continued her affiliation after being promoted to an instructor faculty position in the Department of Genetic Medicine at Weill Cornell Medicine.  She received her PhD from Tufts University where here work focused on differential equation theory and modeling, including systems biology applications.  Her research in the MezeyLab has been focused on analyzing single cell genomic data, where this work has included building a number of pipelines for processing single cell data for various applications and statistical modeling techniques for extracting biological insights from these data.  She also leads a collaborative bioinformatics and GWAS project analyzing 15K fully sequenced genomes of people from the Middle East. Her first / co-first author publications include papers in the American Journal of Respiratory and Critical Care Medicine (the Blue Journal), NPJ Genomic Medicine, Mobile DNA and she has additional papers currently in review.

 Beulah Agyemang-Barimah received her undergraduate degrees in Mathematics and Biology from Southwestern University before joining Cornell University as a graduate student in the Computational Biology PhD program. Her work is focused on developing scalable phylogenetic inference methods that are computationally efficient enough to simultaneously analyze tens of thousands of genetic markers and she is developing a frameworks for mixed model analysis of GWAS (Genome-Wide Association Study) data.  She is also driving a GWAS analysis project studying the impact of the X chromosome on complex human diseases.  Her initial phylogenetic methodology paper is in preparation along with a number of other papers from collaborative projects.

Yuxin Shi was a member of the MezeyLab 2009-2014, she then joined Professor Susan McCouch's lab at Cornell University where her work included collaborative projects between the McCouch and MezeyLab, and she has now rejoined as of January 2021.  Her work on rice has included numerous aspects of analysis and data management including GWAS (Genome-Wide Association Study), eQTL (expression Quantitative Trait Loci) and iQTL (ionomics Quantitative Trait Loci) projects and design of the McCouch Lab application MontyDB, a Genotype Browser web application for dynamically exploring rice genomic, haplotype, and GWAS/eQTL peak localization.  In the MezeyLab, she is currently working on several projects including structural equation pedigree prediction of disease risk where she is both developing the underlying modeling frameworks and applying these frameworks to UK biobank data.  Her publications include papers in Nature Communications, Frontiers in Plant Sciences, Theoretical and Applied Genetics, among others. 

Scott received his undergraduate degree in chemical engineering from The Cooper Union before joining Weill Cornell Medicine  , where he was co-advised by Professor Olivier Elemento and Professor Mezey. He is currently a Scientist at Invitae, where he is working on the development of polygenic risk scores. His PhD work focused on developing machine learning methods for developing disease prediction models that integrate multiple types of data, including genomic data, electronic health records and survey answers.  His research produced a comprehensive comparison and assessment of polygenic risk scores, multifactor clinical predictors of cancer risk, and genetic, clinical, and epidemiological based analyses of the determinants of COVID-19 infection.  His publications include papers in the American Journal of Human Genetics, JCO Clinical Cancer Informatics, Science Reports, among others.

 Dr. Martina Bradic was a Senior Research Associate in the MezeyLab  at Weill Cornell Medicine. She is currently a Senior Computational Biologist at Memorial Sloan Kettering Cancer Center (MSKCC) where her research is focused on computational oncology and cancer genomics.  She received her PhD from Nova University (Lisbon, Portugal), co-founded the start-up Savita Tech, and was concurrently an Adjunct Lecturer at New York University (NYC), where her research was focused on computational genomics of infectious disease.  While in the MezeyLab,  her work spanned analysis of the impact of transposable elements, development of pipelines for processing  epigenomic data and multiple computational and bioinformatics projects.  Her senior publications include papers in Lancet Infectious Diseases, PLoS Pathogens, Genome Biology and Evolution, Epigenomics, Mobile DNA, among others.

Dr. Afrah Shafquat joined the MezeyLab as a Cornell University PhD student in the Computational Biology PhD program after completing her S.B. in Biological engineering from MIT.  She is currently a Senior Data Scientist at Acorn AI, a Medidata company, where her work is focused on developing machine learning solutions trained on clinical trial data for simulating and predicting clinical trial outcomes.  Her PhD graduate work focused on developing statistical models to infer disease misdiagnosis errors using genetic datasets to improve GWAS phenotypes. She developed PheLEx (Phenotype Latent variable Extraction of disease misdiagnosis), a hierarchical Bayesian latent variable model to adjust differentially misdiagnosed disease phenotypes.  During her PhD studies, she was an Insight Data Science Fellow and has also served as a Data Science Consultant at Slalom Consulting.  Her publications include papers in Nature Reviews Microbiology, Trends in Microbiology, BMC Bioinformatics, PLoS One, Cell Metabolism, PeerJ, and mSystems, among others.

Dr. Zijun (Zoe) Zhao joined the MezeyLab as a Weill Cornell Medicine PhD student in the Physiology, Biophysics, and Systems Biology program.  She is currently a Bioinformatics and Quantitative Scientist at Prevail Therapeutics. Her PhD graduate work focused on out-of-sample assessment of Polygenic Risk Score (PRS) methods that were developed by cross-validation on a limited number of GWAS (Genome-Wide Association Study) data sets, where her work outlined improved training approaches to develop new PRS methods that achieve admissible robustness.  Also during her graduate career, she built components of the MezeyLab data management system and was involved in a number of collaborative GWAS and biomarker development projects.  Her publications include a paper in the Annals of Neurology among others.

Dr. Mark Spurgeon joined the MezeyLab  as a Weill Cornell Medicine PhD student in the Physiology, Biophysics, and Systems Biology program after completing his BA in physics and mathematics at SUNY Geneseo.  Upon graduation, he received further data science training as a Fellow at The Data Incubator before joining Oliver Wyman management consulting as a full-time Data Scientist.   He is currently a Senior Data Scientist on the Marketing Machine Learning team at Epsilon.  His graduate research work focused on developing a regularized multivariate methodology for detecting epistasis in expression Quantitative Trait Loci (eQTL).  He also worked on analyzing disease genome-wide methylation profiles, where he developed the initial MezeyLab Methyl-Seq processing and analysis pipeline.  His publications include a paper in Epigenomics among others.

Dr. Sushila Shenoy joined the MezeyLab as a Senior Research Associate and Senior Scientific Programmer after completing her PhD and postdoctoral work at Weill Cornell Medicine.  She is currently a Senior Data Scientist at Medidata Solutions, developing machine learning methods for extracting insights from clinical data to improve the efficiency, safety, and success rates of clinical trials.  While in the MezeyLab, she applied her machine learning, computational statistics, scientific programming, and developer skills to numerous projects, where her work spanned inference of human pedigree relationships from genome-wide data, development of machine learning approaches for analyzing expression Quantitative Trait Loci (eQTL), and development of statistical techniques for identifying biomarkers for detecting environmental stressors.  She also worked on a number of collaborative projects and built pipelines for processing and analyzing next-generation sequencing data.  Her publications include papers in the American Journal of Human Genetics, the American Journal of Respiratory and Critical Care Medicine (the blue journal), PLoS Computational Biology, among many others.

Dr. Jin Hyun Ju joined the MezeyLab as a Weill Cornell Medicine PhD student in the Physiology, Biophysics, and Systems Biology program after completing his B.Eng. degree at Yonsei University.  Upon graduation, he joined Illumina's Clinical Genomics department where he worked on the development of tumor sequencing products for tissue biopsies.  Afterwards, he focused on liquid biopsy products at Guardant Health working on monitoring disease progression. He is currently a Senior Computational Biologist at BridgeBio, where he is leading the computational genomics efforts for Oncology.  His PhD graduate work focused on developing learning methods for identifying confounding factors to enable the identification of broad-impact expression Quantitative Trait Loci (eQTL) from the analysis of genome-wide genotype and transcriptome data.  He developed CONFETI (Confounding Factor Estimation Through Independent component analysis), an algorithm incorporating Independent Component Analysis (ICA) for learning confounding factors from highly multivariate data.  His publications include papers in PLoS Computational Biology and PLoS One, among others. 

Dr. Abishek Sainath Madduri joined the MezeyLab as as a Weill Cornell Medicine PhD student in the Physiology, Biophysics, and Systems Biology program.  He is currently a Data Scientist at Mount Sinai Health Systems, where he is working on image feature extraction from biopsy and surgically resected pathology slides using deep learning techniques, as well as working on analytics for the FDA approval process for developed tests.  His PhD graduate work focused on developing mutual information methods for recovering directed and undirected probabilistic graphical network models, where he used these models to develop network constraint hypotheses when analyzing  data from TCGA (The Cancer Genome Atlas). During his graduate career, he was an instructor of discrete mathematics at Columbia University and an Insight Data Science Fellow.  His publications include papers in Bioinformatics and the Journal of Translational Medicine, among others. 

Sarah Brooks joined the MezeyLab as a Weill Cornell Medicine student in the Tri-Institutional Computational Biology and Medicine program.  For her Master's degree graduate work, she developed computationally efficient learning approaches for identifying covariates and composite covariates from high-dimensional genomic and epigenomic data to increase discovery performance.  During her graduate career, she was additionally the statistical lead on a mouse model gene therapy project.  Her publications include a paper in the Journal of Allergy and Clinical Immunology among others.

Dr. Monica Ramstetter joined the MezeyLab as a Cornell University PhD student in the Computational Biology program, where she was co-advised by Professor Amy Williams and Professor Mezey.  She is currently a Principal Scientist at Loxo Oncology at Lilly.  Her PhD graduate work focused on understanding the limits of inferring extended family relatedness and accurately reconstructing pedigrees  from genome-wide genotype data. She developed DRUID PheLEx (Deep Relatedness Utilizing Identity by Descent), an algorithm for high accuracy recovery of pedigrees beyond nuclear family relationships and was involved in a number bioinformatics, Genome-Wide Association Study (GWAS), and other collaborative projects. During her graduate career, she was selected as a National Science Foundation Graduate Student Fellow and a Cornell Presidential Life Sciences Fellow, and received many other individual grants and awards. Her publications include papers in the American Journal of Human Genetics, Genome Research, Human Genome Variation, among others. 

Dr. Juan Rodriguez-Flores joined the MezeyLab as a Postdoctoral Associate after receiving his PhD from the University of California at San Diego.  He previously held the position of Assistant Professor at Weill Cornell Medicine in the Department of Genetic Medicine before he was recruited to his current position as Senior Manager at Regeneron.  While in the MezeyLab he applied his statistical analysis and bioinformatics programming skills to the study of whole genome sequence data available from early next-generation driven sequencing efforts, where his work spanned building NGS data processing pipelines, to population genetic analysis of human migration, to the analysis of Mendelian and complex genetic diseases. His publications include papers in Nature, Nature Genetics, PLoS Genetics, as well as a publication selected for the cover of Genome Research.

Francisco Agosto-Perez received his undergraduate degree in Physics Applied to Electronics from the University of Puerto Rico-Humacao, his Masters degree in Biophysics and Computational Biology from the Ohio State University, and he joined the MezeyLab as a Senior Scientific Programmer.  In the MezeyLab he built numerous pipelines for processing and analyzing genomic data, developed infrastructure to manage data and maintain high performance compute (HPC) resources, and was involved in numerous collaborative projects, including work with Professor Susan McCouch on rice genome-wide association study (GWAS) analysis and resource development. Since leaving the MezeyLab, Francisco continued in the lab Professor McCouch and is currently in the lab of Professor Kelly Robbins at Cornell, where he is a software developer in the Breeding Informatics team within the Innovation Lab for Crop Improvement.  Beyond his work in genomics and bioinformatics, he builds Apps and maintains development interests in Data Science and Statistics applications in a number of industries.