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.
Research lies at the intersection of optimization and control, two fields that play a vitally important role in cyber-physical systems (CPS) applications but that tend to have very different goals and tools.
Studying how chromatin dynamics influence gene expression during mammalian development and tumorigenesis. Research is rooted in the idea that chromatin, the physiologically relevant form of eukaryotic genomes, contains an indexing system that represents a fundamental regulatory mechanism that operates outside of the DNA sequence itself.
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.
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.
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.
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.
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.
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.
Working to understand the fundamental mechanisms of chromatin-based gene regulation. The lab studies how various chromatin factors are recruited to chromatin to “read” and ‘translate” epigenetic information into differential gene expression patterns under normal growth and development as well as stress conditions.