Tamas Gombosi is the Konstantin Gringauz Distinguished University Professor of Space Science and the Gerstacker Professor of Engineering at the University of Michigan.

Over his four-decade-long career at Michigan he participated in a number of space missions (Cassini, Rosetta, Stereo, MMS and others). In the last two decades he has led a highly interdisciplinary team that developed the first solution adaptive (AMR) global magnetohydrodynamic (MHD) simulation code of space plasmas. His most recent research focus is to bring advanced machine learning to space weather modeling.

He is Fellow of the AGU (1996), Member of the International Academy of Astronautics (1997), recipient of AGU’s inaugural Space Weather Prize (2013), Van Allen Lecturer of AGU’s SPA section (2017), recipient of the Kristian Birkeland Medal (2018), and recipient of AGU’s John Adam Fleming Medal (2020).

I study the percolation model, which is the model for long-range connectivity formation in systems that include polymerization, flow in porous media, cell-phone signals, and the spread of diseases. I study this on random graphs and other networks, and on regular lattices in various dimensions, using computer simulation and analysis. We have also worked on developing new algorithms. I am currently applying these methods to studying the COVID-19 pandemic, which also requires comparison with some of the vast amount of data that is available from every part of the world.

My research involves developing novel data collection strategies and image reconstruction techniques for Magnetic Resonance Imaging. In order to accelerate data collection, we take advantage of features of MRI data, including sparsity, spatiotemporal correlations, and adherence to underlying physics; each of these properties can be leveraged to reduce the amount of data required to generate an image and thus speed up imaging time. We also seek to understand what image information is essential for radiologists in order to optimize MRI data collection and personalize the imaging protocol for each patient. We deploy machine learning algorithms and optimization techniques in each of these projects. In some of our work, we can generate the data that we need to train and test our algorithms using numerical simulations. In other portions, we seek to utilize clinical images, prospectively collected MRI data, or MRI protocol information in order to refine our techniques.

We seek to develop technologies like cardiac Magnetic Resonance Fingerprinting (cMRF), which can be used to efficiently collect multiple forms of information to distinguish healthy and diseased tissue using MRI. By using rapid methods like cMRF, quantitative data describing disease processes can be gathered quickly, enabling more and sicker patients can be assessed via MRI. These data, collected from many patients over time, can also be used to further refine MRI technologies for the assessment of specific diseases in a tailored, patient-specific manner.

Synthetic polymers have been used as a molecular platform to develop host-defense antimicrobial peptide (AMP) mimics toward the development of “polymer antibiotics” which are effective in killing drug-resistant bacteria. Our research has been centered on the AMP-mimetic design and chemical optimization strategies as well as the biological and biophysical implications of AMP mimicry by synthetic polymers. The AMP-mimetic polymers showed broad-spectrum activity, rapid bactericidal activity, and low propensity for resistance development in bacteria, which represent the hallmarks of AMPs. The polymers form amphipathic conformations capable of membrane disruption upon binding to bacterial membrane, which recapitulates the folding of alpha-helical AMPs. We propose a new perception that AMP-mimetic polymers are an inherently bioactive platform as whole molecules, which mimic more than the side chain functionalities of AMPs. The chemical and structural diversity of polymers will expand the possibilities for new antimicrobial materials including macromolecules and molecular assemblies with tailored activity. This type of synthetic polymers is cost-effective, suitable for large-scale production, and tunable for diverse applications, providing great potential for the development of versatile platforms that can be used as direct therapeutics or attached on surfaces.

My research interests concern solar magnetism and magnetic flux transport from the convection zone into the corona, and through the heliosphere.In this context, I have contributed to basic theory and modeling efforts with analytical work and large-scale numerical simulations. The topics that I am particularly interested in are: magnetic flux emergence, magnetohydrodynamic instabilities, coronal mass ejection (CME) initiation, propagation, and CME-driven shocks. Flux emergence was the topic of my Ph.D. thesis research, which demonstrated that the expansion of magnetic fields in a gravitationally stratified atmosphere naturally produces Lorentz forces that drive shear flows. These flows lead to the formation of highly energized coronal magnetic fields, which are observed to be at the epicenter of coronal eruptions. This work provides a fundamental explanation for shear flows, which for decades, have been prescribed as ad hoc boundary conditions in numerical models of CMEs, flares and filament eruptions. While at the University of Michigan, I have advanced this fundamental theory of CME initiation by simulating the eruption of both magnetic arcades and flux ropes by such self-induced shear flows. I conceived and implemented the Gibson-Low flux rope as a way of simulating coronal mass ejections and applied this model to study the the propagation of CMEs from the solar corona through the heliosphere. In doing so, many new aspects of CME interaction with the interplanetary medium were discovered. With coauthors, I demonstrated the model’s capability to predict ICME disturbances at Earth, and advised my student, Meng Jin, in developing data-driven tool, EEGGL, which prescribe the model’s parameters and is now installed at CCMC. I am now working with faculty in the University of Michigan Department of Statistics to apply machine learning to classify and predict flare events and to identify the underlying physical processes.

Our work is interdisciplinary in nature and we connect three fields, chemistry, physics and materials science. Our goal is to develop theoretical tools that give access to directly experimentally relevant quantities. We develop and apply codes that describe two types of electronic motion (i) weakly correlated electrons originating from the delocalized “wave-like” s- and p-orbitals responsible for many electron correlation effects in molecules and solids that do not contain transition metal atoms (ii) strongly correlated electrons residing in the d- and f-orbitals that remain localized and behave “particle-like” responsible for many very interesting effects in the molecules containing d- and f-electrons (transition metal nano-particles used in catalysis, nano-devices with Kondo resonances and molecules of biological significance – active centers of metalloproteins). The mutual coupling of these two types of electronic motion is challenging to describe and currently only a few theories can properly account for both types of electronic correlation effects simultaneously.

Available research projects in the group involve (1) working on a new theory that is able to treat weakly and strongly correlated electrons in molecules with multiple transition metal centers with applications to molecular magnets and active centers of enzymes (2) developing a theory for weakly correlated electrons that is able to produce reliable values of band gaps in semiconductors and heterostructures used in solar cells industry (3) applying the QM/QM embedding theories developed in our group to catalysis on transition metal-oxide surfaces and (4) applying the embedding formalism to molecular conductance problems in order to include correlation effects.

Lei Chen’s group focus on applying data science tools to advanced manufacturing. Chen’s research expertise and interests are to integrate the physics-based computational and experimental methods and data-driven approaches, to exploit the fundamental phenomena emerged in advanced manufacturing and to establish the design protocol for optimizing the materials and process parameters of as-fabricated parts for quality control. Current research can be summarized by:

1 One of significant challenges in additive manufacturing (AM) is the presence of heterogeneous sources of uncertainty involved in the complex layer-wise processes under non-equilibrium conditions that lead to variability in the microstructure and properties of as-built components. Consequently, it is extremely challenging to repeat the manufacturing of a high-quality product in mass production, and current practice usually reverts to trial-and-error techniques that are very time-consuming and costly. This research aims to develop an uncertainty quantification framework by bringing together physical modeling, machine-learning (ML), and experiments.

2 Computational microstructure optimization of piezocomposites involves iterative searches to achieve the desired combination of properties demanded by a selected application. Traditional analytical-based optimization methods suffer from the searching efficiency and result optimality due to high dimensionality of microstructure space, complicated electrical and mechanical coupling and non-uniqueness of solutions. Moreover, AM process inherently poses several manufacturing constraints e.g., the minimum feature size and the porosity in the piezoelectric ceramics as well as at the ceramics-polymer interface. It is challenging to include such manufacturing constraints since they are not explicitly available. This research aims to develop a novel data-driven framework for microstructure optimization of AM piezoelectric composites by leveraging extensive physics-based simulation data as well as limited amount of measurement data from AM process.

3 Lithium (Li) and other alkali metals (e.g., sodium and potassium) are very attractive electrode candidates for the next-generation rechargeable batteries that promise several times higher energy density at lower cost. However, Li-dendrite formation severely limits the commercialization of Li-metal batteries, either because dendrite pieces lose electrical contact with the rest of the Li-electrode or because growing dendrites can penetrate the separator and lead to short circuits. This research aims to develop a computational model to accelerate the design of dendrite-free Li-metal batteries.

My research is mainly concerned with theoretical and computational hydrodynamics, with applications in nonlinear ocean wave prediction and dynamics, wave-body interactions, and wave turbulence theory. I have incorporated the data science tools in my research, especially in the following two projects:

1. Quantification of statistics of extreme ship motions in irregular wave fields: In this project, we propose a new computational framework that directly resolves the statistics (and causal factors) of extreme ship responses in a nonlinear wave field. The development leverages a range of physics and learning based approaches, including nonlinear wave simulations (potential flow), ship response simulations (e.g., CFD), dimension-reduction techniques, sequential sampling, Gaussian process regression (Kriging) and multi-fidelity methods. The key features of the new approach include (i) description of the stochastic wave field by a low-dimensional probabilistic parameter space, and (ii) use of minimum number of CFD simulations to provide most information for converged statistics of extreme motions.

2. Real-time wave prediction with data assimilation from radar measurements: In this project, we develop the real-time data assimilation algorithm adapted to the CPU-GPU hardware architecture, to reduce the uncertainties associated with radar measurement errors and environmental factors such as wind and current in the realistic ocean environment. Upon integration with advanced in-situ or remote wave sensing technology, the developed computational framework can provide heretofore unavailable real-time forecast capability for ocean waves.

Prof. Huan’s research broadly revolves around uncertainty quantification, data-driven modeling, and numerical optimization. He focuses on methods to bridge together models and data: e.g., optimal experimental design, Bayesian statistical inference, uncertainty propagation in high-dimensional settings, and algorithms that are robust to model misspecification. He seeks to develop efficient numerical methods that integrate computationally-intensive models with big data, and combine uncertainty quantification with machine learning to enable robust and reliable prediction, design, and decision-making.