Kevin’s research is focused on to identifying and interpreting the mechanisms responsible for the complex dynamics we observe in ecological and epidemiological systems using data science and modeling approaches. He is primarily interested in emerging and endemic pathogens, such as SARS-CoV-2, influenza, vampire bat rabies, and a host of childhood infectious diseases such as chickenpox. He uses statistical and mechanistic models to fit, forecast, and occasionally back-cast expected disease dynamics under a host of conditions, such as vaccination or other control mechanisms.
Niko Kaciroti is a Research Scientist at the Departments of Pediatrics and Biostatistics. He received his PhD in Biostatistics from the University of Michigan. Since then he has collaborated in multidisciplinary research at the University of Michigan and with researchers from other universities in the United States and internationally. Dr. Kaciroti is a faculty member at the Center for Computational Medicine and Bioinformatics. His main research interest is in using Bayesian models for analyzing longitudinal data from clinical trials with missing values, as well as using Bayesian methods for nonlinear and dynamic models. Dr. Kaciroti is an elected member of the International Statistical Institute and serves as statistical editor for the American Journal of Preventive Medicine and the International Journal of Behavior Nutrition and Physical Activity.
Andrew uses mathematical and statistical modeling to address public health problems. As a mathematical epidemiologist, he works on a wide range of topics (mostly related to infectious diseases and cancer prevention and survival) using an array of computational and statistical tools, including mechanistic differential equations and multistate stochastic processes. Rigorous consideration of parameter identifiability, parameter estimation, and uncertainty quantification are underlying themes in Andrew’s work.
Machine learning approaches and new data science algorithms are an emerging frontier for the atmospheric sciences. We explore whether newly developed physics-guided machine learning algorithms trained with atmospheric model data or observations can serve as emulators for physical processes in weather and climate models, such as the time-consuming solar radiation code, precipitation mechanisms, or the shallow or deep convection cloud schemes. A second, less aggressive approach is to utilize machine learning approaches for the estimation of uncertain parameters in the subgrid-scale physical parameterizations of atmospheric models. We use idealized weather and climate model configurations to intercompare the pros and cons of various machine learning algorithms, such as linear regression, random forests, boosted forests, artificial neural networks and deep neural networks with and without convolutions. In addition, we are interested in machine learning approaches to understand and foster the predictability of the climate system over subseasonal-to seasonal (weeks-to-months) time scales.
Biodiversity in nature can be puzzlingly high in the light of competition between species, which arguably should eventually result in a single winner. The coexistence mechanisms that allow for this biodiversity shape the dynamics of communities and ecosystems. My research focuses on understanding the mechanisms of competitive coexistence, how competition influences community structure and diversity, and what insights observed patterns of community structure might provide about competitive coexistence.
I am interested in the use and development of data science approaches to draw insights regarding coexistence mechanisms from the structural patterns of ecological communities with respect to species’ functional traits, relative abundance, spatial distribution, and phylogenetic relatedness, through as community dynamics proceed. I am also interested in the use of Maximum Likelihood and Bayesian approaches for fitting demographic models to forest census data sets, demographic models that can then be used to quantitatively assess the role of different competitive coexistence mechanisms.
The current goal of our research is to learn enough about the physiology and ecology of microbes and microbial communities in the gut that we are able to engineer the gut microbiome to improve human health. The first target of our engineering is the production of butyrate – a common fermentation product of some gut microbes that is essential for human health. Butyrate is the preferred energy source for mitochondria in the epithelial cells lining the gut and it also regulates their gene expression.
One of the most effective ways to influence the composition and metabolism of the gut microbiota is through diet. In an interventional study, we have tracked responses in the composition and fermentative metabolism of the gut microtiota in >800 healthy individuals. Emerging patterns suggest several configurations of the microbiome that can result in increased production of butyrate acid. We have isolated the microbes that form an anaerobic food web to convert dietary fiber to butyrate and continue to make discoveries about their physiology and interactions. Based on these results, we have initiated a clinical trial in which we are hoping to prevent the development of Graft versus Host Disease following bone marrow transplants by managing butyrate production by the gut microbiota.
We are also beginning to track hundreds of other metabolites from the gut microbiome that may influence human health. We use metagenomes and metabolomes to identify patterns that link the microbiota with their metabolites and then test those models in human organoids and gnotobiotic mice colonized with synthetic communities of microbes. This blend of wet-lab research in basic microbiology, data science and in ecology is moving us closer to engineering the gut microbiome to improve human health.
My work lies in the learning, control, and design of autonomous systems with an emphasis on connected automated vehicles (CAVs). I have been committed to developing robust autonomous vehicles, augmented reality (AR) technology, and V2X systems at Mcity. The highlights include: (1) a robust self-driving algorithm/software stack enabling high-level CAVs; (2) a data-and-AI-driven sensor-level augmented reality (AR) system for efficient safe CAV tests. These systems have been deployed on the Mcity CAV fleet and Mcity testing track for daily operations. I am interested in using big naturalistic human-driving data to train motion planning and control algorithms of self-driving cars, so the automated cars could behave with better roadmanship and thus higher acceptance. I am also interested in data-driven low-uncertainty learning algorithms for object detection, tracking, and fusion, in order to build the perception system of safety-critical autonomous systems.
Today’s real-world problems are complex and large, often with overwhelmingly large number of unknown variables which render them doomed to the so-called “curse of dimensionality”. For instance, in energy systems, the system operators should solve optimal power flow, unit commitment, and transmission switching problems with tens of thousands of continuous and discrete variables in real time. In control systems, a long standing question is how to efficiently design structured and distributed controllers for large-scale and unknown dynamical systems. Finally, in machine learning, it is important to obtain simple, interpretable, and parsimonious models for high-dimensional and noisy datasets. Our research is motivated by two main goals: (1) to model these problems as tractable optimization problems; and (2) to develop structure-aware and scalable computational methods for these optimization problems that come equipped with certifiable optimality guarantees. We aim to show that exploiting hidden structures in these problems—such as graph-induced or spectral sparsity—is a key game-changer in the pursuit of massively scalable and guaranteed computational methods.
My research lies at the intersection of optimization, data analytics, and control.
Alex Gorodetsky’s research is at the intersection of applied mathematics, data science, and computational science, and is focused on enabling autonomous decision making under uncertainty. He is especially interested in controlling, designing, and analyzing autonomous systems that must act in complex environments where observational data and expensive computational simulations must work together to ensure objectives are achieved. Toward this goal, he pursues research in wide-ranging areas including uncertainty quantification, statistical inference, machine learning, control, and numerical analysis. His methodology is to increase scalability of probabilistic modeling and analysis techniques such as Bayesian inference and uncertainty quantification. His current strategies to achieving scalability revolve around leveraging computational optimal transport, developing tensor network learning algorithms, and creating new multi-fidelity information fusion approaches.
Sample workflow for enabling autonomous decision making under uncertainty for a drone operating in a complex environment. We develop algorithms to compress simulation data by exploiting problem structure. We then embed the compressed representations onto onboard computational resources. Finally, we develop approaches to enable the drone to adapt, learn, and refine knowledge by interacting with, and collecting data from, the environment.
Gabor Orosz is an Associate Professor of Mechanical Engineering and Civil and Environmental Engineering. His theoretical research include dynamical systems, control, and reinforcement learning with particular interests in the roles of nonlinearities and time delays in such systems. In terms of applications he focuses on connected and automated vehicles, traffic flow, and biological networks. His research has been supported by the National Science Foundation and industrial funds. His recent work appeared in journals like IEEE Transactions on Automated Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Intelligent Transportation Systems, and Transportation Research Part C. For the latter journal he has also be serving as an Editor. WIRED magazine reported on his experimental results when his team built a connected automated vehicle and evaluated it in real traffic. He served as the program chair for the 12th IFAC Workshop on Time Delay Systems and served as the general chair for 3rd IAVSD Workshop on Dynamics of Road Vehicles, Connected and Automated Vehicles.