Duke’s Provost Sally Kornbluth has launched a Quantitative Initiative to build strength broadly in quantitative science and to establish Duke as an internationally recognized center of excellence in the methods and applications of quantitative science. The initiative seeks to expand quantitative faculty in schools across the Duke campus and to increase collaboration between those departments in Trinity, in Duke’s School of Medicine and in the Pratt School of Engineering. The hiring effort is led by Vice Provost for Research Larry Carin and Dan Kiehart, dean of the Natural Sciences in Trinity College of Arts & Sciences.
Trinity’s goal in this first phase of the Quantitative Initiative was to strategically hire quantitative science faculty whose research portfolios have a strong biomedical focus. Our intention is that these individuals will augment our existing outstanding faculty core in this area and will catalyze greater interaction between the health system and university sides of Duke. The second phase of the QI investment strategy will shift more towards quantitative chemical, biological and physics research problems. In this phase, we will look to more deeply connect Trinity’s natural & physical science departments with those in the Pratt School of Engineering, the Nicholas School for the Environment, and School of Medicine.
Bartesaghi's research is devoted to the development of computational imaging technologies that are the frontier of the cryo-electron microscopy (cryo-EM) field. He develops advanced computational techniques that serve as powerful tools for structure determination of a variety of macromolecular complexes, to efficiently and accurately convert raw images into three-dimensional (3D) representations of assemblies at the highest possible resolution. Importantly, these methods address essential technical aspects that are necessary for the extension of methods in cryo-electron microscopy to study small dynamic protein complexes, including membrane proteins such as G-protein-coupled receptors, transporters and channels that are of fundamental biomedical interest.
Brunel holds a Ph.D. from the Université Pierre et Marie Curie in Paris and came to Duke from the University of Chicago. His computational neuroscience research uses theoretical tools from applied mathematics and statistical physics to understand the dynamics of neural systems, and how they encode and store information. His research efforts have been focused on the single synaptic level, with the development of a new synaptic plasticity model that captures a large body of experimental data; and on the single neuron level, with the mathematical analysis of the stochastic dynamics of a large range of simplified spiking neuron models, and the development of a new spiking neuron model (the EIF model) that captures accurately spiking generation dynamics of real neurons. At the network level, Brunel’s research group has developed tools for analyzing network states with irregular single neuron activity, and investigated the mechanisms of synchronized oscillations in randomly connected networks. He has studied information storage in large networks of neurons, and shown that an information optimization principle can explain many experimentally observed features of synaptic connectivity. His work has been applied to understand phenomena such as persistent activity seen in delayed response experiments in behaving monkeys, as well as oscillations in various systems such as monkey V1 or rodent cerebellum.
Cheng holds a Ph.D. from Princeton University in applied and computational mathematics and comes to Duke from Yale University following a postdoc in the Departement d’Informatique from École Normale Supérieure, France. Cheng’s dissertation focused on random matrices in high-dimensional data analysis and neural networks.
Carlson holds a Ph.D. from Duke University in electrical and computer engineering and conducted a postdoc in the Data Science Institute and Department of Statistics at Columbia University. Carlson’s work in machine learning focuses on neuroscience, where novel devices can collect data orders of magnitude larger than current measurement technologies. He is developing machine learning approaches that can adapt to this complexity to give state-of-the-art predictions. In addition to predictive performance, he is interested in applying interpretable methods to neurological disorders in order to enable design of medical interventions.
Goldberg specializes in populations genetics and will earn her Ph.D. in 2017 from Stanford University and join Duke in 2018. Both a population geneticist and anthropologist by training, she studies the population biology of humans and related species. She develops methods to study population histories and dynamics, integrating techniques from population genetics, ecology, and archeology. Additionally, she is interested in the genetic signatures of sex-specific processes on the autosomes and X chromosome, including mutation, recombination, and admixture. Her dissertation focused on methods for the study of temporary varying populations histories. She has already been published in Nature and the Proceedings of the National Academy of Science
Herring earned a Ph.D. from Harvard University and comes to Duke from the Department of Biostatistics in the Gillings School of Global Public Health at UNC-Chapel Hill. Her research emphasizes longitudinal and multivariate data, hierarchical models, latent variables, Bayesian methods, reproductive epidemiology and environmental health. She won the Mortimer Spiegelman Award for outstanding public health statistician under the age of 40 from the American Public Health Association in 2012. Her published work has created insight into health issues such as multi-ethnic studies of atherosclerosis; modeling birth outcomes such as congenital heart defects and pre-term births; and women’s dietary patterns from pregnancy through postpartum.
Hoff holds a Ph.D. in Statistics with an emphasis in biostatistics from the University of Wisconsin-Madison. He comes to Duke from the University of Washington in Seattle, WA. Hoff deepens Duke’s bench as a leader in Bayesian statistics and has authored a textbook titled “A First Course in Bayesian Statistical Models (Springer New York, 2010). He specializes in building statistical tools to analyze network or “relational” data. These types of data, which document the complex, changing sets of interactions between different individuals within a group, are currently popping up in all areas of research from the social sciences to genomics, Hoff’s tools are designed to extract patterns and meaning from these wide-ranging subjects, which can vary from friendships within social networks and relationships between countries on the international stage, to interactions between different sets of proteins within a cell.
Marvian is completing a postdoc at MIT working on Quantum Computation and Quantum Information theory. He completed his Ph.D. in Physics in October 2012 at the University of Waterloo and Perimeter Institute for theoretical physics. His thesis was titled Symmetry, Asymmetry and Quantum Information. Marvian has published in Nature Communications, Extending Noether’s theorem by quantifying the asymmetry of quantum states. He is particularly interested in the application of representation theory in Quantum Information theory, and applications of Quantum Information theory in Quantum many body systems.
Tarokh holds a Ph.D. in electrical engineering from the University of Waterloo in Ontario, Canada, and comes to Duke from Harvard University. His work focuses on telecommunications, specifically to statistical signal processing and data analysis for wireless communications. In 2014, Science Watch named Tarokh one of the World’s Most Influential Scientific Minds in the field of computer science.
A former Gugenheim Fellow in Applied Mathematics, he is an IEEE Fellow and winner of the IEEE Eric E. Sumner Award for his contributions to communications technology.
Wu is an M.D./Ph.D. who earned his Ph.D. in Mathematics from Princeton University under the guidance of mathematician Ingrid Daubechies, and his M.D. from the National Yang-Ming University in Taiwan. His research interests range from mathematical study to data analysis with a focus on analyzing big/massive datasets by applying proper mathematical tools/theorems. His main field of application is medicine where he works on the following problems: anesthesia/sedation/sleep analysis based on different physiological signals, breathing/heart rate variation analysis and coupling effect, weaning prediction, ECG waveform analysis like fetal ECG analysis and f-wave analysis, seasonality analysis of diseases, etc.