Jeff Linderoth

Jeffrey Linderoth

Professor and Department Chair

  • Discovery Fellow

Models and Algorithms for Large-Scale Numerical Optimization

Page Lab

Algorithms for data mining and machine learning, and their applications to biomedical data, especially clinical and high-throughput genetic and other molecular data. Of particular interest are inductive logic programming (ILP) and other multi-relational learning techniques capable of dealing with complex data points (such as molecules or clinical histories) and producing logical rules.

Vetsigian Lab

Dynamics of microbial interactions in natural and synthetic microbial communities. The lab develops protocols for quantifying the community dynamics at the phenotypic and genetic levels, and seek simplified theoretical models that reproduce aspects of the experimentally measured dynamics.

Roy Lab

A computational biology group interested in developing statistical computational methods to understand regulatory networks driving cellular functions. The lab works to identify networks under different environmental, developmental and evolutionary contexts, comparing these networks across contexts, and construct predictive models from these networks.

Loewe Lab

Envisioning new ways to help biologists capture their ideas as models in the larger context of Evolutionary Systems Biology. Our lab aims to improve the quality of these models by quantifying evolution with increasing precision.

Laurence Loewe

Laurence Loewe

Assistant Professor

  • WID Faculty

Developing a stable, user-friendly programming language compiler for reproducible biodata analyses and general modeling in biology while building in-depth models for bioresearch

Adam Christensen

Adam Christensen

Assistant Scientist

  • Research Staff

Building optimization models that help untangle issues of environmental sustainability.

Multi-Omics Hub

The Multi-Omics Hub will focus on the use of big data about the genes, microorganisms, and metabolites to understand biological systems. WID’s expertise makes it an ideal home for the Epigenetics Initiative for the large campus community that studies the epigenome, and as such WID will organize meetings, seminars, mutli-PI …

Connecting the Dots: a New Method to Understand Cell Type Transitions

Wisconsin Institute for Discovery (WID) researchers Rupa Sridharan and Sushmita Roy are combining their expertise in regenerative biology and computational biology to better understand how cells transition from one type to another through gene regulation.

Sushmita Roy

Sushmita Roy

Assistant Professor

  • WID Faculty

Computational methods to model cellular networks

Michael Ferris

Michael Ferris

Stephen C. Kleene Professor, Data Science Hub Leader

  • WID Faculty

Optimization methods and data modeling for large scale problems in science, engineering and economics

Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies

In a paper in Cell Systems, Sushmita Roy and colleagues develop a probabilistic graphical model-based method, multi-species regulatory network learning that uses a phylogenetic framework to infer regulatory networks in multiple species simultaneously.

John Yin

John Yin

Vilas Distinguished Achievement Professor

  • WID Faculty

Forecasting of virus-host growth and infection spread; physical and chemical origins of life