Best and Calder Appointed Co-Directors of MBI
Christopher Hadad, College of Arts and Sciences Divisional Dean for Research and the Natural and Mathematical Sciences at The Ohio State University, appointed Professor Janet Best, Department of Mathematics, and Professor Catherine (Kate) Calder, Department of Statistics, co-directors of the MBI with a term ending on May 31, 2020.
A Message from the New Co-Directors
We are honored to serve as Co-Directors of the Mathematical Biosciences Institute. As we have begun to settle into our new positions over the last week, we have taken a moment to reflect on the Institute's remarkable achievements since it opened its doors more than fifteen years ago. MBI has run 160 week-long workshops, hosted 12,000 workshop participants and 325 long-term visitors from across the world, and trained over 80 postdoctoral fellows, many of whom have gone on to assume prominent positions in universities, colleges, and industry. Institute activities have fostered innovation in the application of mathematical, statistical, and computational methods to advance research in areas ranging from cancer genetics to neuroscience to ecology and evolution. Simultaneously, MBI activities have supported the development of new areas in the mathematical sciences motivated by important biological questions. Moreover, MBI has leveraged its support from the NSF, Ohio State, and Institute Partners to provide a variety of vertically-integrated training opportunities for undergraduates, graduate students, postdocs, and researchers. The Institute has also fostered innovative diversity programs and established a virtual National Mathematical Biology Colloquium. MBI has shared these successes broadly through our website and video streaming capabilities.
On a more personal note, MBI has played a pivotal role in our academic careers. Janet was part of the second class of MBI postdocs before becoming a faculty member in the Department of Mathematics at Ohio State and ultimately joining the Institute's leadership team as an Associate Director in 2014. As a new faculty member in the Department of Statistics, Kate was heavily involved in MBI's early educational initiatives at the undergraduate and graduate levels. More recently, as an Associate Director, Kate has been responsible for coordinating our NSF-funded Distributed REU Program in the Mathematical Biosciences.
In our new leadership roles at the Institute, we plan to maintain a broad portfolio of activities supporting research and training in the mathematical biosciences. Thinking toward the future, we also intend to thoughtfully update and expand the Institute's mission, with input from the broader MBI community and others.
We would like to extend particular thanks to Professor Avner Friedman and Professor Marty Golubitsky, who served as the first two Directors of the MBI, and to Professor Greg Rempala who has served as the Interim Director for the past year and a half.
We look forward to your continued participation in MBI activities and your involvement in planning the Institute's future.
Our very best for 2018 and beyond,
The Mathematical Biosciences Institute
Catherine (Kate) Calder joined the faculty in the Department of Statistics at The Ohio State University in 2003. She served as an associate director of the Mathematical Biosciences Institute (MBI) from 2015-2017, before assuming the role of MBI co-director in 2018. She is a faculty affiliate of the Institute for Population Research, the Criminal Justice Research Institute, and the Translational Data Analytics Institute at Ohio State. She is currently an associate editor for the Annals of Applied Statistics and Bayesian Analysis and has served the statistics profession through various elected roles in sections of the American Statistical Association (ASA) and in the International Society for Bayesian Analysis. She received the ASA Section on Statistics and the Environment’s 2013 Young Investigator Award and was elected Fellow of the ASA in 2014.
Calder’s research focuses on the development of stochastic models for phenomena that exhibit complex dependencies, particularly when the dependencies are spatial and/or temporal in nature. Her methodological contributions have been in the areas of dimension reduction for spatio-temporal data, the development of covariate-driven nonstationary spatial models, data-augmentation algorithms for Bayesian spatial generalized linear (mixed) models, latent space models for two-mode networks, and model-based comparisons of networks. Much of her research is motivated by applications in the environmental, social, and health sciences. She has received funding for her research from the NIH, NSF, NASA, and other agencies and foundations.
Calder holds a BA in Mathematics from Northwestern University and an MS and PhD in Statistics from Duke University.
Janet Best came to The Ohio State University in 2003 as a Postdoctoral Fellow at the Mathematical Biosciences Institute, and she joined the faculty of the Department of Mathematics in 2006. Since 2014, she has served as an Associate Director of the MBI before assuming the co-Directorship in 2018. She is on the editorial board of Discrete and Continuous Dynamical Systems – Series B, the Board of Directors of the Society for Mathematical Biology, and has also served the mathematics profession and the field of mathematical biology in a variety of roles in the Society for Industrial and Applied Mathematics (SIAM) and its Life Sciences and Dynamical Systems activity groups.
Her research focuses on applications of mathematics to human physiology, especially brain physiology. She has created and analyzed mathematical models of the biophysics of neurons, the behavior of neuronal networks, and the mechanisms of sleep-wake regulation using bifurcation theory and nonlinear dynamics. Another major emphasis of her work is understanding the synthesis and control of neurotransmitters in the brain along with applications to depression and Parkinson’s disease. With collaborators, she has discovered new homeostatic mechanisms in the biochemistry of the brain that are important for protecting the brain against genetic polymorphisms; understanding such homeostatic mechanisms and their control is important for precision medicine. This work has led to new questions in stochastic dynamical systems and bifurcation theory. Her research has been supported by AFOSR, The Alfred P. Sloan Foundation, NIH, and a CAREER award from the NSF.
Best has an AB in Mathematics from Princeton University and a PhD in Mathematics from Cornell University.