WID’s research portfolio is broad and interconnected, encompassing faculty and students in more than two dozen departments at the University of Wisconsin-Madison. Individual labs form the foundation of WID’s research activities and provide the groundwork for the collaborative community that gives rise to innovation and discovery.
The Ashton Group is working to understand, model, and recapitulate in vitro the instructive signals utilized by human embryos to pattern tissue-specific differentiation of pluripotent stem cells, and apply this knowledge towards the rational design of tissue engineered scaffolds and other regenerative therapeutic strategies.
Theoretical and algorithmic aspects of mixed-integer optimization, with a special emphasis in linear and polynomial functions. Other interests include polyhedral combinatorics and combinatorial optimization.
Investigating the mechanism and biological function of reversible protein modifications involved in modulating signal transduction, chromatin dynamics, and gene activation and addressing the “Histone Code” hypothesis by beginning to understand histone modification signaling code and its mechanisms and regulation.
Algorithmic and interface development for large scale problems in mathematical programming, including links to the GAMS and AMPL modeling languages, and general purpose software such as PATH, NLPEC and FATCOP.
Prof. Gong’s research group focuses on the design, synthesis/fabrication, and characterization of novel materials and devices. Many of their ongoing projects are multidisciplinary, bridging engineering with materials science, chemistry, and life sciences. Some of their efforts include multifunctional drug/agent nanocarriers for the combined delivery of therapeutic and diagnostic agents which can be used to treat and diagnose various types of diseases. Her group also studies multifunctional polymer nanocomposites for various applications including flexible electronics, supercapacitors, and nanogenerators.
Understanding diversity in microbial communities and their role in infectious disease; in particular, the genetic basis for stability of microbial communities, the role of a gut community as a source of opportunistic pathogens, and the soil microbial community as a source of new antibiotics and antibiotic resistance genes.
INTEGRATE, short for “Integrating Robots into the Future of Work,” is a National Science Foundation Research Traineeship (NRT) program developed to provide a unique academic environment to enrich the training of graduate students in STEM disciplines, including computer science, engineering, psychology, and economics, to address fundamental research challenges in realizing the integration of robots into the future of work at the micro, systems, and macro scales.
Developing integrated empirical and theoretical models of biological systems. Using these models we aim to discover and design mechanistic-based engineering solutions to address challenges in the areas of chronic infection and disease, biodefense, and the growing field of bio-manufacturing.
The NRG focuses on signal processing, machine learning, optimization, and statistics. Areas of focus include sparsity and active learning, learning graphs and networks, and interactive machine learning with humans.
Applications in astroinformatics, biology, drug discovery, networks, vision, and recommender systems.
Develop techniques to better the experience of virtual reality through new devices, interfaces, and techniques.
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.
Interests include energy markets, Climate Policy, international trade, technical change and computational economics.
By bringing together stem cell biology, genome engineering, and biomaterials expertise, the Saha lab generates new tools for use with human-induced pluripotent stem cells to ask unique questions about human biology and disease.
Investigating how observers make predictions about objects and entities based on their cognitive and emotional responses to perceptual information; focusing on how people’s associations with colors influence cognitive processing in aesthetic response, judgment and decision making, and interpretation of information visualizations.
My research involves the development of statistical models to answer biological questions.
The main scientific focus of the lab is in defining how the epigenome controls cell identity. We want to know how non-genetic information controls functional specialization of a cell and use this knowledge to direct efficient conversion of desired cell types with the ultimate goal of improving stem cell based therapy.
The Turng lab works with injection molding and innovative plastics manufacturing processes (such as microcellular injection molding / MuCell process), pioneering materials (biobased polymers, nanocomposites, electro-active polymers (EAPs), etc.), and intelligent modeling and process control (computer-aided engineering (CAE), numerical simulation, design and process optimization, intelligent injection molding control, and Internet-based collaboration) to advance the science and manufacturing techniques surrounding tissue engineering scaffolds.
The Virtual Environments Group is a space and a place where scholars explore the connections among environment, technology, human action, experience, and visualization, with expertise in virtual and augmented reality.
Numerical optimization, especially problems involving real (as opposed to integer or discrete) variables. Includes theory, algorithms, implementation and application.
Investigating how living organisms cooperate or compete in diverse and changing environments. Methods and perspectives are drawn from many fields, including ecology, evolution, molecular biology, physics, chemistry, engineering, mathematics, and computer science. The lab uses data-driven mechanistic and statistical models to predict when microbes or other organisms will persist or perish, with a broad goal of promoting human health through effective management of microbe-host interactions.
