Alex Gorodetsky

Alex Gorodetsky is an Associate Professor of Aerospace Engineering at the University of Michigan, part of the College of Engineering. His research focuses on autonomous decision-making under uncertainty, leveraging applied mathematics and computational science. Key areas include uncertainty quantification, machine learning, control systems, and tensor decompositions. He leads the Gorodetsky Group, which develops algorithms for high-fidelity simulations and scalable digital twins.

Education: PhD (2016) and SM (2012) from MIT in Aeronautics/Astronautics; BSE (2010) in Aerospace Engineering from the University of Michigan.

Research Interests: Innovating methods for managing uncertainty in complex systems like autonomous aircraft and electric propulsion, with applications in aerospace, bioengineering, and climate modeling. Techniques include Bayesian inference, multi-fidelity surrogate modeling, and compressed data analytics.

Notable Contributions:

• Developed the MFNETS framework for efficient multi-fidelity surrogate networks.
• Recipient of the NSF CAREER Award (2023) and Air Force Young Investigator Award (2018).
• Active in NASA-funded initiatives like the Joint Advanced Propulsion Institute (JANUS).

Recent Work: Focus on GPU-accelerated plasma simulations, automated statistical estimation, and low-rank tensor methods for high-dimensional problems. Over 50 peer-reviewed articles, with recent highlights in Computer Methods in Applied Mechanics and Engineering, SIAM Journal on Scientific Computing, and Journal of Computational Physics.

Teaching & Mentorship: Advises over 20 graduate and undergraduate students, with emphasis on interdisciplinary training in computational methods and AI-driven science.