Faculty & Staff
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Core and Affiliated Faculty
My research is based in dynamical systems and control theory, with a focus on multi-agent systems and their collective behavior. In this context, I am interested in biologically-inspired mathematical modeling, studying models from both analytical and numerical perspectives, and developing applications informed by the results, such as robotic systems inspired by animal swarms and brain network control systems.
Much of my recent work tackles modeling of collective behavior in animal groups, which has included the motion of bats in flight and collective sensing through shared sonar within robotic swarms.
My work uses a wide range of methods, including: agent-based models which can be studied through numerical simulation; network models whose salient characteristics can be computed analytically; and data-driven analyses with which hypotheses on interaction network structures can be statistically tested.
Dr. Beattie's research interests include model reduction of large scale dynamical systems, computational linear algebra, spectral estimation for linear operators, and problems related to data assimilation and inference in oceanography and atmospheric sciences.
Assistant Professor, University Libraries
Dr. Brown's research involves the application of computational molecular modeling and bioinformatic tools to relate the structure and dynamics of molecular systems to function. Dr. Brown also runs the DataBridge undergraduate research program, which trains and has students apply data science techniques to a variety of data-centric projects the group consults on with collaborators from all over campus and beyond. Projects include working with historical election data, text data mining for antimicrobial resistance and COVID-19 topics, and more. Data visualization, analysis (trend spotting, text data mining, etc), and tool generation are areas where students work to find solutions for research projects.
Dr. Chung's research interests concern various forms of inverse problems. Driven by their application, Dr. Chung develops and analyzes efficient numerical methods for inverse problems. Applications of interest are, but not limited to, systems biology, medical and geophysical imaging, and dynamical systems.
Teaching statement of Dr. de Sturler:
The Rime of the Ancient Professor
(EdS, after Colridge's The Rime of the Ancient Mariner)
O teach me, teach me, learned man!
The student dothe implore.
Tell me, quoth she, of PRD
And ODE much more.
Forthwith this frame of mine was wrenched
With a woeful agony,
Which forced me to begin my class;
And then it left me free.
Since then, each week the selfsame hour,
That agony returns:
And till I have my lecture told,
This heart within me burns.
Three times a week I go to class;
I have strange power of speech;
That moment that her face I see,
I know the student must hear me:
To her my class I teach.
Associate Professor, Statistics
Visiting Collaborator, Biocomplexity Institute
- Interface between design of experiments and machine learning
- Model and analysis of high-dimensional data
- Covariance matrix estimation and its applications
- Statistical methods to Nanotechnology
- Statistical modeling with applications in financial services
Program Leader, Computational Modeling and Data Analytics
Associate Director, Virginia Tech's Smart Infrastructure Laboratory
Dr. Embree's research interests includeinverse eigenvalue problems in vibration, spectral theory for non-self-adjoint operators, and algorithms for large-scale linear algebra.
- Process monitoring and control
- Manufacturing and service process improvement
- Risk management
Dr. Gramacy's research interests include Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, sequential design, and optimization under uncertainty.
Dr. Gugercin's research lies in the area of model reduction, the primary goal of which is to replace large-scale dynamical systems with lower dimensional dynamical systems having as near as possible the same input/output response characteristics as the original.
In contexts where the input/output characteristics are primary, the resulting reduced model can then be used to replace the original model, potentially as a far more efficient component within a larger simulation.
This research area is highly interdisciplinary in character and has a large overlap with other areas of mathematics and scientific computing, such as numerical analysis, systems and control theory, and optimization. It has found immediate application in diverse areas of the physical sciences and engineering including signal propagation and interference in electric circuits; (PDE) constrained optimization; uncertainty quantification and inverse problems. Dr. Gugercin has focussed on both theoretical and computational aspects of model reduction as well as their application in real-world problems.
However, the methods and theory he develops are not specific to a particular application; rather they apply to a wide range of problems.
Specializations: Big Data Econometrics, Statistical Machine Learning, Deep Learning, Time Series Analysis, Forecasting, Financial Economics, Financial Network Analysis, Nonlinear Systems
Assistant Professor, Mathematics
Dr. Hewett’s research interests include:
- Computational inverse problems and data assimilation at extreme compute and data scales
- Deep learning as a physically constrained inverse problem
- Applications in geoscience, atmospheric and space physics, medical imaging, photography, and computer vision
- Software engineering for Computational Science & Engineering
David M. Higdon is a professor in the Social Decision Analytics Laboratory at the Biocomplexity Institute of Virginia Tech. Previously, he spent 10 years as a scientist or group leader of the Statistical Sciences Group at Los Alamos National Laboratory. He is an expert in Bayesian statistical modeling of environmental and physical systems, combining physical observations with computer simulation models for prediction and inference. Dr. Higdon has served on several advisory groups concerned with statistical modeling and uncertainty quantification and co-chaired the NRC Committee on Mathematical Foundations of Validation, Verification, and Uncertainty Quantification. He is a fellow of the American Statistical Association.
Associate Professor, Statistics
Dr. House's research interests include:
- Bayesian statistical modeling with an emphasis in model averaging, kernel regression, and Bayes linear
- Uncertainty analysis of computer models/experiments
- Data mining coupled with data visualization that promotes human-data interaction and education in Statistics
- Applications in proteomics, bioinformatics, cosmology, climatology, and hydrology
Assistant Professor, Statistics
Dr Johnson is a quantitative ecologist working at the intersection of statistics, mathematics, and biology. At a broad level, her research focuses on understanding how differences between individuals in a population result from external heterogeneity and stochasticity, and how this variability influences population level patterns. She address these questions primarily in the context of infectious disease epidemiology, as well as in behavioral and population ecology. Her approach is to use theoretical models to understand how systems behave generally, while simultaneously seeking to confront and validate models with data and make predictions. Thus, a significant portion of her research focuses on methods for statistical — particularly Bayesian — inference and validation for mechanistic mathematical models of biological and ecological systems. Specific application areas have included the transmission of vector-borne diseases and population dynamics of animals.
Associate Professor, Statistics
Dr. Kim's research interests focus on developing semi/nonparametric statistical methods and theory using regression splines or Gaussian process to address issues in several areas (epidemiology, medicine, genomics, proteomics, and system biology) for high and low dimensional analysis. Both Frequentist and Bayesian methods have been developed.
Associate Professor, Statistics
Dr. Leman's core research interests include Bayesian statistics on both a theoretical and inferential level, MCMC mixing theory, Data Augmentation for efficient simulation, large scale stochastic modeling, molecular evolution, and coalescence processes. Additionally, he has a strong interest visualization techniques, which involve Human-Computer-Interaction. More specifically, given visual displays, Dr. Leman is interested in how users can inject feedback, so that resulting displays are a merger between the data, visualization model, and the user's cognitive insights. Such methods prove to be exceedingly useful in exploring relevant information in very high-dimensional spaces.
Collegiate Faculty, Statistics
Dr. Lucero is interested in both computational and statistical methodology applied in a variety of scientific fields. He is extremely passionate about teaching the next generation of computational/data scientists.
His interests involve the following general areas:
- Inverse Problems
- Uncertainty Quantification
- Statistical Learning
- Experimental Design
- Interdisciplinary Applications
Assistant Professor in Mathematics
Dr. Martin's research focuses on computational science related to the built and natural environment, particularly subsurface imaging and source detection. This includes inverse problems, imaging science, signal processing, and augmenting computational science with data science techniques. She often works with large, streaming data sets, and has extensive experience with data recorded by fiber optic distributed acoustic sensing networks.
Dr. Matthews’ research focuses on applications of algebraic geometry and combinatorics to problems in communications and data storage, especially coding theory and cryptography.
Professor, Computer Science
Associate Director, Discovery Analytics Center
Dr. North's research seeks to enable people to interactively visualize and explore big data for discovering new insights, by establishing usable, effective, and scientifically grounded methods for visual interaction. His current research themes focus on creating powerful interactions for computational analytics that respond to human cognitive sense making activity, and exploiting large high-resolution displays to create rich embodied-interactive spaces.
Dr. Piilonen leads an internationally known research program in high-energy particle physics that focuses on charge-parity symmetry breaking in B meson decay. He is a leading member of the Belle collaborations at the KEK National Laboratory in Japan, and his work was cited as the experimental verification of the theoretical predictions honored with the Nobel Prize in Physics in 2008.
Dr. Pleimling's research interests include:
- Out-of-equilibrium dynamical behavior of complex systems: aging phenomena and dynamical scaling
- Critical phenomena in confined geometries
- Microcanonical analysis of small systems
Thomas L. Phillips Professor of Engineering, Computer Science
Director, Discovery Analytics Center
Dr. Ramakrishnan's research interests include mining scientific datasets in domains such as systems biology, neuroscience, sustainability, and intelligence analysis. His work has been featured in the NIH outreach publication Biomedical Computation Review, the National Science Foundation’s Discoveries series, Wall Street Journal, Newsweek, Smithsonian Magazine,Popular Science, Slate magazine, and ACM Technews.
Professor and Department Head, Computer Science
Associate Director, Center for High-End Computing Systems
Dr. Ribbens' primary research interests are in parallel computation, high-end computing and computational science. Current topics of interest include distributed shared memory systems, concurrency bug detection and recovery, algorithms and tools for improving utilization and throughput on parallel systems via dynamically re-sizable (malleable) parallel computations, and algorithms and tools for improving the performance of large-scale computational ensemble computations and sparse linear-algebra kernels. Other topics of recent and possible future interest include numerical linear algebra and mathematical software for PDEs, grid computing (including scheduling, load balancing, code composition frameworks, fault tolerance, and resource-aware issues), and problem solving environments.
Assistant Professor, Statistics
Dr. Sengupta’s research interests are primarily in statistical methodology for network data, bootstrap and related resampling methods, big data, and computational statistics.
He is also interested in statistical applications in wide-ranging problems in science and industry.
Collegiate Assistant Professor Ufferman teaches classes in both Computational Modeling and Data Analytics and Discrete Mathematics. He has worked on developing our undergraduate cryptography sequence and directed related undergraduate research projects. As a member of the orientation and lower-division advising teams, he works to help first-year Math majors with the transition to the university environment.
Dr. Warburton is a Professor of Mathematics and he holds the John K. Costain Faculty Chair in the College of Science at VT. He is the Graduate Program Chair in Mathematics and is also affiliated with the CMDA program, teaching the CMDA 3634 parallel computing for undergraduates course.
Dr. Warburton's research interests include the development of highly parallel numerical algorithms in particular for graphics processing units. He has developed novel high-order discontinuous Galerkin methods and explored their use in high fidelity physical modeling of acoustics, elastodynamics, electromagnetics, fluid dynamics, and plasma physics.
Associate Professor, Mathematics
Dr. Zietsman's current research interests involve the development and analysis of numerical methods for solving optimal control problems where the dynamics are described by partial differential equations; for example, fundamental fluid flows. This includes applications such as the design, optimization and control of energy efficient buildings. Commercial buildings are responsible for 40% of the energy consumption and greenhouse gas emissions worldwide and significantly exceed those of all transportation combined. Reducing energy consumption of commercial buildings can have a tremendous impact on energy cost and greenhouse gas emission.