Duke’s Provost Sally Kornbluth launched the Quantitative Initiative in 2017 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 College of Arts & Sciences, Duke’s School of Medicine, Nicholas School of the Environment, and Pratt School of Engineering. The goal of the first phase of the QI was to strategically hire quantitative science faculty whose research portfolios have a strong biomedical focus. The faculty hired under the QI appear below. These faculty have strengthened the units into which they were recruited and are catalyzing greater interaction between the health system and university sides of Duke. The second phase of the QI investment strategy shifted toward quantitative chemical, biological and physics research problems. Through this phase, the natural & physical science departments in Trinity College of Arts & Sciences will be more deeply connected with those in the Pratt School of Engineering, Nicholas School of the Environment, and School of Medicine.
ASSOCIATE PROFESSOR OF COMPUTER SCIENCE & BIOCHEMISTRY
Bartesaghi's research is devoted to the development of computational imaging methods that are at the frontier of the cryo-electron microscopy (cryo-EM) field. He develops advanced image analysis tools to efficiently and accurately convert noisy microscopic images into three-dimensional (3D) structures of proteins at near-atomic resolution. Importantly, these methods address essential technical challenges in the study of small and dynamic assemblies, including CRISPR-Cas surveillance complexes, targets for cancer drugs and membrane proteins such as G-protein-coupled receptors that are of fundamental biomedical interest. Dr. Bartesaghi received the “Norman P. Salzman Memorial Award in Virology” from the Foundation for the National Institutes of Health, for his work on the molecular architecture of native HIV-1 gp120 trimers.NICOLAS BRUNEL
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.
Carlson holds a Ph.D. from Duke University in electrical and computer engineering and previously completed postdoctoral training in the Data Science Institute and Department of Statistics at Columbia University. Carlson’s work in machine learning focuses on building novel methodologies capable of accelerating scientific discovery. One scientific focus is on neural electrophysiology, an area of research where novel devices are collecting data orders of magnitude larger than prior measurement technologies, requiring algorithms and analysis techniques that both scale to the data size and take advantage of the additional data to build stronger representations. By accounting for machine learning analysis in the design of the experiments, these algorithms can be used to construct testable hypotheses that can be validated by follow-up experiments. This approach has previously been used to discover a neural biomarker of stress susceptibility. In addition, Carlson has active collaborations in a wide variety of applications, including analyzing clinical trials in treatments of neuropsychiatric disorders and improving air quality estimates.
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.
Goldberg earned her Ph.D. from Stanford University in 2017. Before joining Duke, she was a Miller Fellow at UC Berkeley. Integrating techniques from population genetics, ecology, and archeology, she develops methods to study population dynamics and evolution. Her work has elucidated sex-specific demography, evolution on short time-scales, and mathematical models of disease transmission and susceptibility. Goldberg was a James F. Crow Early Career Researcher Award Finalist by the Genetics Society of America, and won the Sherwood Washburn award from the American Association for Physical Anthropology. Her work is funded by an NIH MIRA award.
Herring earned her doctorate from Harvard University and came 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 proposed new statistical methods for analysis of correlated data measured on multiple scales and has created insight into health issues such as modeling birth outcomes such as congenital heart defects and pre-term births; health effects of exposure to chemicals in the environment, and understanding women’s dietary patterns from pregnancy through postpartum. Herring was recently awarded the 2019 Janet L. Norwood Award for outstanding achievement by a woman in statistical sciences.
Hoff holds a Ph.D. in Statistics with an emphasis in Biostatistics from the University of Wisconsin-Madison, and is a Fellow of the IMS and ASA. He came to Duke from the University of Washington in Seattle. Hoff deepens Duke’s bench as a leader in Bayesian statistics and has authored a textbook titled “A First Course in Bayesian Statistical Methods" (Springer New York, 2010). He specializes in building statistical tools to analyze multivariate and multi-level datasets, including network or “relational” data. These types of data, which document the complex interactions between different individuals, 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 holds a Ph.D. in Physics from the University of Waterloo and Perimeter Institute for Theoretical Physics. Before coming to Duke he had a postdoc position at MIT. His main research interest is Quantum Computation and Information theory, and its applications in quantum many-body systems. He has worked on various topics in this field, such as Quantum Resource Theories, Quantum Thermodynamics, Quantum Algorithms, Quantum Error Suppression, Entanglement Theory and Symmetry-Protected Topological order.
RHODES FAMILY PROFESSOR OF ELECTRICAL & COMPUTER ENGINEERING
Tarokh holds a Ph.D. in electrical engineering from the University of Waterloo in Ontario, Canada, and came to Duke from Harvard University. His work focuses on statistical signal processing and data analysis, learning methods, modeling, inference, and prediction from data. In 2014, Science Watch named Tarokh one of the World’s Most Influential Scientific Minds in the field of computer science. A former Guggenheim Fellow in Applied Mathematics, he is an IEEE Fellow and winner of the IEEE Eric E. Sumner Award. He is a member of the United States National Academy of Engineering.
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.