Kevin Coombes
Department on Biomedical Informatics
Dr. Coombes’s current research focuses in statistical, mathematical, and computational methods to process, analyze, and understand highly multivariate biological data arising from high throughput technologies. He is particularly interested in (1) methods that incorporate existing biological knowledge early in the analytical process and (2) methods that integrate diverse types of biological data with a view toward predicting clinically relevant patient outcomes. He also specializes in forensic bioinformatics. Prior to joining the university, Dr. Coombes was a part of the MD Anderson Cancer Center within the Department of Bioinformatics and Computational Biology.
Adriana Dawes
Department of Mathematics
In the Dawes Lab, we are interested in understanding how biochemical pathways, mechanical structures, and cell geometry/morphology interact and regulate each other to produce the complex patterns we associate with biological systems. Misregulation of any of these aspects can have disastrous consequences ranging from birth defects to metastatic cancer. As an interdisciplinary lab, we use a wide variety of research techniques from mathematics, statistics, genetics and cell biology, and beyond. We develop biologically based theoretical models that are often both explanatory, identifying underlying mechanisms that give rise to seemingly contradictory behaviors, and predictive, providing novel direction for future experimental work. On the experimental side, we use the nematode worm Caenorhabditis elegans (C. elegans) as our experimental organism, exploiting the high level of conservation involved in these networks. Using an array of tools from genetics and microscopy, we perturb target proteins and quantify the resulting changes. Experimental data are used to motivate, challenge and expand our mathematical models. Tight integration and continuous feedback between experimental and theoretical results is critical to improving our understanding of these important regulatory networks and interactions.
Marty Golubitsky
Department of Mathematics
Dr. Golubitsky works in the fields of nonlinear dynamics and bifurcation theory studying the role of symmetry in the formation of patterns in physical systems and the role of network architecture in the dynamics of coupled systems. His recent research focuses on some mathematical aspects of biological applications: animal gaits, the visual cortex, homeostasis, and coupled systems. He has co-authored four graduate texts, one undergraduate text, and two nontechnical trade books, (Fearful Symmetry: Is God a Geometer with Ian Stewart and Symmetry in Chaos with Michael Field) and over 100 research papers.
Matthew Kahle
Department of Mathematics
Kahle is a professor in the math department at Ohio State University. His research interests include topological and geometric combinatorics, stochastic topology, topological statistical mechanics, and geometric group theory. He is also interested in topological data analysis and applications of algebraic topology in general.
Eben Kenah
Division of Biostatistics
Barbara Keyfitz
Department of Mathematics
My research is in the field of nonlinear partial differential equations. I have studied systems of conservation laws which are nonstrictly hyperbolic or which change type from hyperbolic to elliptic in steady or unsteady flow. Systems with this behavior arise in models for multiphase flow in porous media, and in two-phase compressible and incompressible flow. With Suncica Canic and Eun Heui Kim, I began an analysis of self-similar solutions of systems of conservation laws in two space dimensions. Recently, I have been working with Katarina Jegdic and with Allen Tesdall on extensions of this multidimensional work.
Joseph Tien
Department of Mathematics
Tien’s research interests include modeling infectious disease dynamics, network science, data science, and the use of data-driven methods to promote informed civic engagement.
Chuan Xue
Department of Mathematics
Xue’s research area is mathematical biology. I develop new mathematical models and methods to address fundamental questions in cell and developmental biology. In particular, we are interested in understanding how transport and interaction of intracellular molecules lead to robust whole-cell functioning, how cells communicate with each other and self-organize into multicellular structures, and how different cells coordinate with each other to maintain health and prevent diseases. Xue’s research is data-driven and deeply rooted in real-world applications. I extract interesting applied math problems and solve them using a wide variety of tools including stochastic processes, stochastic differential equations, ordinary and partial differential equations, asymptotic analysis, machine learning and scientific computing.