People

Kaiser graduated from Iowa State University in 2018 with his B.Sc. in Microbiology and Genetics. In 2024 he went on to complete his Ph.D. in Neuroscience at Virginia Tech. Kaiser is currently a Michigan Institute for Data and AI in Science (MIDAS) postdoctoral fellow and is co-advised by Dr. Omar Ahmed in the Department of Psychology. His work focuses on using Bayesian Inference approaches to understand how networks of neurons work together to represent/predict brain state and behavioral changes in mouse models.

Email Kaiser: [kcarndt] at umich dot edu

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McKenna Davis Breddan completed her Ph.D. in Aerospace Engineering at the University of California, Los Angeles in the fall of 2024, where she simulated and analyzed electrospray plume evolution for spacecraft propulsion as part of the Plasma, Energy, and Space Propulsion Laboratory. Her research now focuses on developing tensor-network based solvers for plasma physics problems in collaboration with the Los Alamos National Laboratory. She also performs uncertainty quantification research for Hall effect thrusters in support of the NASA Joint Advanced Propulsion Institute.

Email McKenna: [mbreddan] at umich dot edu

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Jérémy completed his Ph.D. in Applied Mathematics at the University of Toulouse, France in the fall of 2024, where his research focused on multilevel Monte Carlo application to the estimation of random fields as part of an interdisciplinary project with IRIT and CERFACS laboratory. Currently, his research focuses on the use of multi-fidelity methods in variational inference and filtering problems.

Email Jérémy: [jeremb] at umich dot edu

Caleb completed his Ph.D. in Applied Mathematics at the University of Arizona in the summer of 2024, where his research focused on information theory, optimal experimental design, and uncertainty quantification. His work now centers on developing methods for sequential decision-making under uncertainty, with an emphasis on multi-fidelity and budget-constrained settings. He is particularly interested in advancing the practical use of optimal experimental design for real-world problems, as well as in applications of Bayesian inference and information theory to improve decision-making in complex systems.

Email Caleb: [cdahlke] at umich dot edu

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Zheng received his Ph.D. in Computer Science from the University of California, San Diego, where he developed novel program synthesis frameworks. Currently a Schmidt AI in Science Postdoctoral Fellow, his work focuses on tensor network methods, program optimization, and their applications to AI for science. He is particularly interested in integrating automated reasoning with deep learning methods to reliably and efficiently optimize complex scientific computing programs.

Email Zheng: [zhgguo] at umich dot edu

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Liliang's research interests include Bayesian learning theory and variational inference. Her work has two directions: studying the stability of Bayesian inference to better understand Bayesian sequential learning and model-based decision making, and developing online variational inference methods that support efficient joint state–parameter estimation and decision making in the presence of uncertainty.

Email Liliang: [liliangw] at umich dot edu

Aditya received his B.Tech in Mechanical Engineering from K.J Somaiya College of Engineering in Mumbai, India and his MSE degree from University of Michigan Department of Aerospace Engineering. His research interests include tensor networks, numerical methods, randomized linear algebra, and data-driven learning. He is currently working on developing fast and efficient PDE solvers by leveraging tensor decomposition techniques and randomized algorithms.

Email Aditya: [dadity] at umich dot edu

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In-space propulsion research efforts are focused on electric propulsion systems, which are essential for low Earth orbit spacecraft and future long-term space exploration missions. Josh’s research interests in this field are specifically on improving computational and predictive plasma dynamics modeling and increasing performance of high-power Hall-effect thrusters. Josh received his B.S. in Mechanical Engineering from Rose-Hulman Institute of Technology.

Email Joshua: [eckelsjd] at umich dot edu

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Nicholas received his B.S. in Aerospace Engineering from the University of Southern California in May 2026 with a minor in Mathematics. His current research focuses on generative frameworks for applications in trajectory modeling and multiphysics simulations. His broader research interests also include aerospace design and optimization.

Email Nicholas: [lototsky] at umich dot edu

Sanjan received his BTech degree in Aerospace Engineering from the Indian Institute of Technology, Madras and his MSE Aerospace degree from the University of Michigan. His research interests include CFD, Aerodynamics and its intersection with Uncertainty quantification and Bayesian Inference. Currently, his work focuses on Uncertainty quantification for CFD and multidisciplinary analysis to aid in reducing costs for certification by analysis . He is co-advised by Dr. Martins in the Aerospace department.

Email Sanjan: [sanjancm] at umich dot edu

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Eden recieved a B.S. in Mathematics from Longwood University. Her research interests include data-driven inference, uncertainty quantification, and computational fluid dynamics. She is interested in developing efficient computational methods for optimizing fluid and physical systems operating under uncertainty, with applications toward space systems.

Email Eden: [powersep] at umich dot edu

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Rotating Detonation Engines (RDEs) provide an avenue for improving the fuel efficiency of jet-powered flight. William's research focuses on uncertainty quantification and model error of low- to mid-fidelity mathematical models describing RDEs. William received his B.S. in Mechanical Engineering from North Dakota State University in December, 2025.

Email William: [wroden] at umich dot edu

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Georgios received his B.Sc. in Mechanical Engineering from Columbia University. His research interests lie broadly in computational science and applied mathematics, with a focus on machine learning, Bayesian inference, and multifidelity modeling.

Email Georgios: [thomakos] at umich dot edu

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Junpeng received his B.S. in Aerospace Engineering from Embry-Riddle Aeronautical University and his MSE in Aerospace Engineering from the University of Michigan. His research interests focus on multi-fidelity approaches for uncertainty quantification. His current work centers on extending sampling-based multi-fidelity estimators to account for unknown oracle statistics.

Email Junpeng: [junpengz] at umich dot edu