Graduate Group in Genomics and Computational Biology

GCB Home » Faculty » Algorithms and machine learning

  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.
Mary Regina Boland Developing novel data mining and machine learning algorithms that integrate data from Electronic Health Records, observational health data and genetics.
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
Robert Babak Faryabi

Developing algorithms for integrative cancer genomics and epigenomics studies.

Casey Greene Integrative methods for noisy biological data.
John Holmes Naturally inspired algorithms for knowledge discovery and optimization including learning classifier systems, genetic algorithms, artifical immune systems, ant colony optimization, and swarm intelligence.
Shane Jensen Bayesian hierarchical models and their implementation
Sampath Kannan Algorithms and Complexity
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.
Jason Moore
Development of artificial intelligence and machine learning methods
Kate Nathanson Inherited and somatic genetics and genomics of cancer, developing piplines for analysis, integration of inherited and somatic genetics
Marylyn Ritchie 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.
David Roos
Studies in the Roos laboratory employ modern cell biological, molecular genetic, biochemical/pharmacological, immunological and genomic/bioinformatic techniques to study protozoan parasites, eukaryotic evolution, and the biology of host-pathogen interactions.
Jeffrey Saven Methods for molecular and biopolymer design
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.
faculty photo Kai Tan
Our lab is interested in Systems Biology of gene regulation in normal and disease development.
Lyle Ungar Scalable machine learning methods for data mining and text mining
Ryan Urbanowicz Ryan Urbanowicz My primary research interests focus on the development, evaluation, and application of novel computational, statistical, and visualization methods to facilitate classification and data mining in the complex, noisy domain of biomedical research.
Benjamin Voight Tools for locus discovery and fine-mapping using ENCODE
Kai Wang The research in our laboratory focuses on the development of bioinformatics methods to improve our understanding of the genetic basis of human diseases, and the integration of electronic health records and genomic information to facilitate genomic medicine on scale.
faculty photo Li-San Wang Annotate non-coding RNA using RNA-Seq data; detecting enhancers from functional genomics 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