Faculty

Mark Abney

Research Associate (Associate Professor)

Department of Human Genetics
920 E. 58th St
Chicago, IL 60637
phone: 773-702-3388
fax: 773-834-0505

 I am currently a Research Associate (Associate Professor) in the Department of Human Genetics at the University of Chicago. My primary research interest is in statistical and computational methods for genetic analysis, particularly methods for discovering causal variation for quantitative traits in genomic data and approaches for addressing cryptic relatedness and other forms of complex population structure.


Stefano Allesina

Professor, Department of Ecology & Evolution
Fellow, Computation Institute

Zoology 403
1101 E 57th st Chicago IL 60637
Phone: 1 773-702-7825

My laboratory develops new mathematical, statistical and computational methods for the analysis of ecological data.


Jorge Andrade

Director of Bioinformatics, Center for Research Informatics

Knapp Center for Biomedical Discovery, 900 E. 57th St., Chicago, IL 60637.

As the technical director responsible for planning and oversight of the Bioinformatics Core, Jorge has extensive training in bioinformatics as well as many years of experience applying these tools within the pharmaceutical industry. Most recently, he led a 70-person bioinformatics team at the Beijing Genome Institute. Since joining the CRI in 2012, he has built an 8-person team of PhD scientists, all focused on delivering high-quality analyses of genomic and proteomic data. He has instituted the development and deployment of over ten industry-grade analysis pipelines, all running in the CRI high-performance computing environment. He has engaged in over 150 collaborations with over 75 researchers from the University of Chicago and elsewhere. The rigorous analysis provided by his group has become the substrate for numerous grant applications and peer-reviewed manuscripts.


Lin Chen

Assistant Professor (Biostatistics)

Lin Chen’s overall research interests focus on the development of statistical methods for analyzing high-dimensional genomics data. Her methodology developments are always motivated by challenges in real data problems. She develops methods to analyze high-dimensional ‘omics’ data from genetic association studies, next-generation sequencing studies, gene transcriptional expression studies, proteomic studies and more recently, the integration of ‘big data’.


Mengjie Chen

Assistant Professor, Section of Genetic Medicine

The University of Chicago
Department of Medicine
900 East 57th Street
KCBD 3220A
Chicago, IL 60637 USA

Hi, my name is Mengjie Chen

I'm an assistant professor in Section of Genetic Medicine in the Department of Medicine at University of Chicago.

Before joining U Chicago, I was an assistant professor in the Department of Biostatistics and Genetics  at UNC-Chapel Hill from 2014 to 2016. I graduated from the interdisciplinary program of Computational Biology and Bioinformatics at Yale University in 2014. I was co-advised by Professor Hongyu Zhao in Department of Biostatistics and Professor Haifan Lin, who is a stem cell biologist.  Before that, I did my undergraduate majoring in Biotechnology from Huazhong University of Science and Technologyin China (09').

High-throughput technologies have been increasingly applied in a broad range of scientific research. These technologies present researchers with the task of extracting meaningful information from high-dimensional data spaces, where hundreds or thousands measurements concurrently obtained from a relative small number of samples. My primary research is driven by the need for powerful statistical methods to address the challenges those technologies have posed for data analysis and interpretation, particularly for data emerging from biological and biomedical studies, such as epigenetic and cancer genomics related research.

I hope my future methodology research will contribute to the integration of genomic feature into the prediction of clinical outcomes, which will potentially shed new lights on personalized disease diagnosis and treatment.


D. Allan Drummond

Assistant Professor of Biochemistry and Molecular Biology

Office: GCIS W234
Lab: GCIS W229
929 E. 57th Street
Chicago, Illinois 60637
Lab: (773) 834-0635
Office: (773) 834-2017
Fax: (773) 702-0439

Allan is an Assistant Professor at the University of Chicago in the Department of Biochemistry & Molecular Biology, with a secondary appointment in the Department of Human Genetics.

Allan started the group at Harvard University as a Bauer Fellow at the FAS Center for Systems Biology, where he spent five years. He received his Ph.D. in Computation and Neural Systems from Caltech.

Prior to entering science, Allan worked at Trilogy for seven years in multiple roles, the last as an HR director responsible for training and leadership development as director of Trilogy University.


A. Murat Eren

Assistant Professor, Department of Medicine, and the Committee on Microbiology

The University of Chicago
900 E. 57th St.
Knapp Center for Biomedical Discovery, RM 9118
Chicago, IL 60637
Phone: (504) 408-1980

From the human gut and oral cavity to pristine soils and marine ecosystems, we use our expertise in microbiology and computation to study microbial communities and their ecology. We strive to create advanced software platforms, and design appropriate experiments to contribute to a wide range of concurrent research questions in the domain of microbial ecology. We are constantly looking for collaborations that would challenge us with novel and intriguing questions.


Robert Grossman

Faculty and Senior Fellow, Institute for Genomics and Systems Biology (IGSB)
Chief Research Informatics Officer, Biological Sciences Division
Professor, Department of Medicine

University of Chicago
900 East 57th Street
KCBD 10146
Chicago, IL 60637

Phone: 773-834-4669

I am the Chief Research Informatics Officer (CRIO), the Director of the Center in Data Intensive Science and a Professor in the Division of Biological Sciences at the University of Chicago. I am also a Core Faculty and Senior Fellow at the Institute for Genomics and Systems Biology (IGSB) and the Computation Institute. My research group focuses on big data, data science, bioinformatics, cloud computing and related areas.


Xin He

Assistant Professor

Cummings Life Science Center (CLSC) 420, 920 E. 58th St, Chicago, IL 60637
Phone: 773-834-7678

Dr. Xin He (贺信)  is an Assistant professor in Department of Human Genetics at University of Chicago, with a secondary appointment in Department of Statistics. Xin completed his PhD in Computer Science in 2009 at University of Illinois at Urbana-Champaign, where he worked with Saurabh Sinha. Before his current position, Xin completed postdoc training with Dr. Hao Li at Department of Biochemistry of UCSF, and with Drs. Kathryn Roeder and Ziv Bar-Joseph at Lane Center for Computational Biology of Carnegie Mellon University. Xin is interested in statistical genetics and genomics, computational biology/bioinformatics and evolutionary genomics.


Hae Kyung Im

Assistant Professor, Genetic Medicine

900 East 57th Street
KCBD 3220
Chicago, IL 60637

Our goal is to develop quantitative and computational methods and tools to sift through the vast amounts of genomic data with the goal of making discoveries that can be translated into clinical practice.


Manyuan Long

Edna K. Papazian Distinguished Service Professor, Department of Ecology & Evolution
Senior Fellow, Institute for Genomics and Systems Biology

Office: Zoology 301E
Phone: (773) 702-0557
Fax: (773) 702-9740

An interesting problem in evolutionary biology is how genes with novel functions originate. My research focuses on this problem, although I am also interested in other issues of molecular evolution. Interest in evolutionary novelties can be traced back to the time of Darwin. However, studies of the origin and evolution of genes with new functions have only recently become possible and attracted increasing attention. Although conceptual revolution is always what we wish to pursue, the available molecular techniques and rapidly expanded genome data from many organisms mean that searching for and characterizing new genes is no longer a formidable technical obstacle. Molecular and evolutionary studies have provided powerful analytical tools for the detection of the processes and mechanisms that underlie the origin of new genes.

Three levels of questions about this process can be defined. First, at the level of individual new genes, what are the initial molecular mechanisms that generate new gene structures? Once a new gene arises in an individual genome in a natural population, how does it spread throughout an entire species to become fixed? And, how does the young gene subsequently evolve? Second, at the level of the genome, how often do new genes originate? If new gene formation is not a rare event, are there any patterns that underlie the process? And, what evolutionary and genetic mechanisms govern any such patterns? Third, what are functions and phenotypic effects of new genes? How are the detected patterns impacting the phenotypic evolution, e.g. e.g. the environmental adaptation and evolution of development?

I believe that an efficient approach to these questions is to examine young genes because their early processes of origination are directly observable. Pursuit of these problems requires an integrated approach incorporating molecular, genomic and population analyses. My lab applies such an approach to our studies. Using experimental and computational genomic analysis, we identified numerous new genes in Drosophila and mammalian genomes. Using molecular analysis, we revealed some important molecular evolutionary mechanisms responsible for their current gene structures. By evolutionary genetic analysis, we observed a significant role of the adaptive evolution in the determination of the fate of those new genes. Interesting patterns are observed associated with these new genes.

I see questions there, challenges there, joys there.


Mary Sara McPeek

Professor, Department of Statistics, Department of Human Genetics, and the College
Member, Committee on Genetics, Genomics and Systems Biology
Senior Fellow, Computation Institute

University of Chicago
Department of Statistics
5747 S. Ellis Avenue
Jones 318
Chicago, IL 60637 USA

My research interests are in statistical genetics and development of statistical
methods. Software for most of my projects is available on my website
Specific current research interests include:

  • methods for haplotype analysis in dependent samples
  • methods for genetic association analysis, including methods for binary, 
    quantitative, longitudinal and X-linked traits
  • spectral analysis of population structure and other approaches to unknown
    population structure, particularly in the context of genetic association
    analysis
  • methods to detect association with copy number variation
  • methods for genetic analysis in isolated populations
  • relationship inference
  • quasi-likelihood methods, problems of incomplete data, and 
    applications of random matrix theory in statistical genetics
Mary Sara McPeek

Dan Nicolae

Chair, Department of Statistics
Professor, Departments of Statistics; Human Genetics; Medicine; Section of Genetic Medicine; the College
Senior Fellow: Computation Institute

University of Chicago
Department of Statistics
5734 S. University Avenue
Jones 319
Chicago, IL 60637 USA
Phone: 773.702.4837
Fax: 773.702.9810

My major research interests are in the areas of statistical genetics and mathematical statistics. The problems I am studying are mainly motivated by applications to genetics and genomics studies on common/complex diseases, with a focus on asthma. Statistical genetics problems of interest include likelihood methods for mapping genetic risk factors, analysis of sequencing datasets, genome-wide strategies for testing gene-gene and gene-environment interaction, networks and systems biology. I am also interested in functional genomics and the analysis of gene expression data. Mathematical statistics topics I am working on include Bayesian and frequentist methods for quantifying information.


John Novembre

Associate Professor of Human Genetics

920 E. 58th Street, CLSC 5th floor, Chicago, IL 60637
fax 773.834.0505

I am an Associate Professor in the Department of Human Genetics at the University of Chicago.  My main research goal is to tackle statistical and analytical problems that will enable discoveries about evolutionary processes and the nature of disease variation.  


Mercedes Pascual

Professor, Department of Ecology and Evolution

Office: Zoology 206
Phone: (773) 795-2354
Fax: (773) 702-9740

I am a theoretical ecologist interested in population and community dynamics. 
Research in my lab is primarily on infectious diseases whose population dynamics are environmentally driven. We explore for example the impact of climate variability and climate change on water-borne and vector-borne infections such as cholera and malaria. We are interested in combining mathematical models and computational statistical methods to analyze the temporal and spatial variability of infectious diseases and to assess their predictability, based on extensive but incomplete and noisy data. We are also addressing the spatio-temporal dynamics of infectious diseases in large cities of the developing world, asking questions about the interaction of environmental and socio-economic drivers, including responses to climate forcing.


Michael Rust

Assistant Professor, Department of Molecular Genetics and Cell Biology
Assistant Professor, Department of Physics
Core Member, Institute for Genomics and Systems Biology

900 East 57th Street
KCBD 10124
Chicago, IL 60637
Phone: (773)-834-1463

My lab is interested in understanding how the properties of living cells emerge from the stochastic reactions of molecular components. We use a mixture of biophysical, biochemical, genomic, mathematical modeling, and single-cell microscopy approaches to link the properties of molecules to the systems-level behavior of cells. Most of our attention is currently focused on an oscillatory protein network found in the cyanobacterium Synechococcus elongates that the organism uses to predict the time of day. Remarkably, the biological rhythms generated by this circadian clock can be reconstituted in a test tube using three purified protein components - KaiA, KaiB, KaiC - making this the best-defined biological oscillator currently known. It is possible to reconstitute metabolic input signaling to this minimal clock by varying ATP and ADP concentrations (Rust et al, 2011).  We are actively pursuing a quantitative understanding of the reactions that generate oscillations and the robustness properties of this minimal circadian clock. We are also working outward to expand the functions that can be studied in a purified context and include additional components from the in vivo clock system. We seek to tie the biochemical and biophysical properties of these components back to physiologically relevant conclusions for the organism by making quantitative measurements of growth rate and single-cell behavior, including experiments that are uniquely possible in microbial model systems.


Andrey Rzhetsky

Professor, Department of Medicine, Section of Genetic Medicine
Senior Fellow, Computation Institute
Principal Investigator and Director, Conte Center for Neuropsychiatric Genomics

The University of Chicago
Knapp Center for Biomedical Discovery
900 East 57th Street, KCBD 10160B
Chicago, IL 60637

Dr. Rzhetsky’s interest is in (asymptotic) understanding how phenotypes, such as human healthy diversity and maladies, are implemented at the level of genes and networks of interacting molecules. To harvest as much information about known molecular interactions as possible, his group runs a large-scale text-mining effort aiming at analysis of a vast corpus of biomedical publications. Currently they can extract from text automatically about 500 distinct flavors of relations among biomedical entities (such as bind, activate, merystilate, and transport).Dr. Rzhetsky is a mathematician and theoretical biologist, and the leading expert in the development of novel bioinformatics approaches to complex biology and disease. He is the developer and inventor of the first automated literature extraction program for the prediction of molecular interactions, and a pioneer in development of bioinformatics strategies to map complex disease genes.


Matthew Stephens

Professor, Departments of Statistics and Human Genetics, and the College

Department of Human Genetics
University of Chicago
Cummings Life Science Center (CLSC), room 422
920 E. 58th Street
Chicago, IL 60637
Fax: (773) 834-0505
Phone: (773) 702-8327

My lab works on a wide variety of problems at the interface of Statistics and Genetics. We often tackle problems where novel statistical methods are required, or can learn something new compared with existing approaches. Thus, much of our research involves developing new statistical methodology, many of which have a non-trivial computational component. And because data sets are getting larger and larger our work often involves modern methods for "high-dimensional statistics". Our work often makes extensive use of Bayesian hierarchical models to borrow information across data sets or sampling units.

Recently my lab has been increasingly focussed on making its research more open, reproducible and extensible. This is because I see this as the first step towards greater cooperation of scientists to achieve common goals.


Barbara Stranger

Assistant Professor, Department of Medicine, Section of Genetic Medicine

Institute for Genomics and Systems Biology
The University of Chicago
900 E. 57th Street
KCBD 10134
Chicago, IL 60637

Dr. Barbara Stranger has a longstanding interest in population genetics and gene regulatory processes, and how these shape phenotypic variability. Her lab collects and analyzes multi-dimensional human genomics data, particularly transcriptome data and genetic variation data, in the context of health and disease. She had made many contributions to the field of expression QTL (eQTL) mapping in human populations, including large-scale genome-wide eQTL studies in humans, cross-tissue eQTL analysis of a single cohort, analysis of eQTLs in populations of diverse ancestry, sex-specific eQTLs in humans, and others. She has also successfully integrated eQTLs with disease, including cancer and inflammatory diseases, and published methods and resources for doing so. Current projects in her lab integrate regulatory genomics (including transcriptomics and proteomics) with human disease mapping, as well as characterizing the context-specificity of genetic and epigenetic effects on gene regulation (e.g., cell-type, sex, age).


Samuel Volchenboum

Associate Professor of Pediatrics
Associate Chief Research Informatics Officer
Director, Center for Research Informatics
Associate Director, Institute for Translational Medicine

University of Chicago
Institute for Molecular Pediatric Sciences
Room: Suite K160
5721 South Maryland Avenue
Chicago, IL 60637
Phone:(773) 702-4303
Fax: (773) 834-1329

Samuel L. Volchenboum, MD, PhD is a pediatric oncologist and informatacist. In addition to taking care of children with cancer and blood diseases, Dr. Volchenboum directs the University of Chicago Center for Research Informatics (CRI), a 40-person group that provides computational support and collaboration to researchers within the University of Chicago Biological Sciences Division.   Dr. Volchenboum has a special interest in assembling and integrating large data sets to enable clinical research. Under his direction, the Clinical Research Data Warehouse has provided data for over 500 research projects. He is working on methods to better connect disparate data sets in order to enable healthcare analytics.   Dr. Volchenboum’s main area of research is in utilizing large clinical data sets to make inferences about how disruptive events in the hospital can lead to downstream perturbations and alterations in healthcare delivery.    Dr. Volchenboum is also focused on creating data commons for pediatric cancer. Starting with an international neuroblastoma data commons, the CRI worked with the Center for Data Intensive Science to standardize the clinical trials data and connect it to other data sources, including genomic and bio-specimen information, making the data and tools available in a high-performance computing infrastructure.