Sanjan C. Muchandimath

I am a Ph.D. candidate in Aerospace Engineering at the University of Michigan, Ann Arbor, where I am co-advised by Prof. Alex Gorodetsky and Prof. Joaquim Martins. My research lies at the intersection of Bayesian inference and Computational Fluid Dynamics.

Before beginning my doctoral studies, I earned my Master’s degree from the University of Michigan and worked for eight months at Odys Aviation, an eVTOL startup in Long Beach, California. I hold a Bachelor’s degree with Honours from the Indian Institute of Technology (IIT) Madras. Prior to graduate school, I also spent a year as a Project Assistant at the Turbulent Shear Flow Physics and Engineering Laboratory (TSFPEL) at the Indian Institute of Science (IISc) in my hometown of Bangalore during Covid.

My background is rooted in Computational Fluid Dynamics (CFD), structural analysis, and Fluid-Structure Interaction (FSI). However, during my Master’s at the UofM, a course on Inference, estimation and learning sparked a pivot towards the intersection of applied mathematics and engineering physics. This pivot led me to my current Ph.D. work: bridging the gap between traditional physics-based modeling and probabilistic methods.

Specifically, I focus on creating novel frameworks for Uncertainty Quantification (UQ), Bayesian Inference, Surrogate Modeling and (hopefully) Probabilistic Optimization. I am passionate about using rigorous theory to build algorithms that can be validated on a wide class of physical problems — from fluid dynamics to general system design. My ultimate goal is to enable decision-making under uncertainty for computationally expensive systems, regardless of the specific application domain.

In my spare time, I either watch or play soccer, listen to a lot of music and am an avid F1 fan having been a part of Formula Student all my undergrad years with Raftar Formula Racing.