Graduate Group in Genomics and Computational Biology

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Statistics and Applied Math

  Name Research
Yoseph Barash The lab develops machine learning algorithms that integrate high-throughput data (RNASeq, CLIPSeq , PIPSeq, etc.) to infer RNA biogenesis and function, followed by experimental verifications of inferred mechanisms.
Danielle Bassett Complex Systems | Network Science | Computational Neuroscience | Systems Biology | Dynamical Systems |
Soft Materials | Behavioral Network Science
Mary Regina Boland Developing novel data mining and machine learning algorithms that integrate data from Electronic Health Records, observational health data and genetics.
Casey Brown Our research focuses on how genotypes produce phenotypes and how they vary and evolve.
Pablo Camara The focus of our lab is on the development and application of innovative computational approaches to the study of cellular heterogeneity and its role in disease. 
Jennifer Phillips-Cremins Epigenetics | Genomics | Systems and Synthetic Bioengineering | Experimental Neuroscience | Molecular and Cellular Engineering
Charles Epstein Partial Differential Equations, Maxwell's Equations, Population Genetics, Medical Imaging, Several Complex Variables, Microlocal Analysis and Index Theory Numerical Analysis
Robert Babak Faryabi

Application of graph-theory to model epigenetic dysregulation in cancer.

Rui Feng Statistical genetics, family study, next generation sequence; Biostatistics
Casey Greene Integrative methods for noisy biological data.
Shane Jensen Bayesian hierarchical models, urban analytics, sports analytics
Junhyong Kim Single cell genomics, systems biology of cell function, evolution of cell function, population genetics and phylogenetics
Konrad Kording We are a group of data scientists with interest in brains and, nore general, biomedical research. Right at the moment, much of the research in the lab is about deep learning and its applications. However, we are now mostly interested in causality and its links with machine learning.
Michael Levy Optimal control of infectious diseases; statistical inference on spatial spread of insects
Mingyao Li Statistical genetics for gene mapping of complex traits. Focus is on the development of methods for genetic association studies using SNPs and copy number variations.
Hongzhe Li My method research has been mostly motivated by problems in genetics, genomics and metagenomics.
Iain Mathieson Association studies, the distribution of rare variants, the genetics of spatially structured populations and understanding human history using ancient DNA. Understanding the evolution of complex traits in humans, and their relationship to demographic history and natural selection.
Joshua Plotkin Molecular evolution, Population genetics, Mathematical biology
Marylyn Rtichie The mission of the Ritchie Lab is to improve our understanding of the underlying genetic architecture of common diseases such as cancer, diabetes, cardiovascular disease, and pharmacogenomic traits among others.
Li Shen The central theme of the Shen lab is focused on developing computational and informatics methods for integrative analysis of multimodal imaging data, high throughput omics data, cognitive and other biomarker data, electronic health record data, and rich biological knowledge (e.g., pathways and networks), with applications to various complex disorders.
Kai Tan Our lab is interested in Systems Biology of gene regulation in normal and disease development.
Sarah Tishkoff Statistical population genetic analyses of human evolutionary history and adaptation
Lyle Ungar Analyze social media to better understand what determines physical and mental well-being.
Benjamin Voight Modeling variability in rate of mutation genome wide in humans
faculty photo Li-San Wang Structural variant calling algorithms from GWAS and DNA-Seq data
Nancy Zhang Change-point methods, empirical bayes estimation, genomics., model and variable selection, scan statistics, statistical modeling
Yi Xing Yi Xing The long-term goal of our research is to elucidate alternative isoform complexity in mammalian transcriptomes and proteomes