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MAE student Rohit Vuppala received the Roy and Virginia Dorrough Distinguished Graduate Fellowship

Tuesday, November 14, 2023

Rohit Vuppala


Rohit Vuppala is a Ph.D. candidate specializing in Unmanned Aerial Systems at the Department of Mechanical and Aerospace Engineering (MAE), and a two-time recipient of the Roy and Virginia Dorrough Distinguished Graduate Fellowship. His research focuses on developing Data-driven, Machine Learning-based Reduced Order Models (ML-ROMs) to predict wind patterns in urban environments. Using data from Computational Fluid Dynamics (CFD) to simulate gusts, his work is a pivotal component of a National Robotics Initiative (NRI-NSF) at the MAE department, to validate the hypothesis that gust awareness can bolster the safety and resilience of drone operations within cities.


Collaborating with the Unmanned Systems Research Institute (USRI), Systems, Cognition, and Control Laboratory (SCC), and Control, Robotics, and Automation Laboratory (CoRAL) at MAE, Vuppala's ML-ROMs hold promise in revolutionizing drone operations. By providing real-time insights into unpredictable wind patterns, these models have the potential to significantly enhance the safety and reliability of drones navigating complex urban landscapes. Vuppala's contributions extend beyond the laboratory, with his research findings already published in high-impact journals like AIP Advances and presented at prestigious conferences like AIAA, and APS-DFD. He recently completed a summer research internship at the prestigious Los Alamos National Laboratory (LANL) and is currently a visiting graduate student researcher at the Computational Physics and Methods group (CCS-2), LANL. He is advised by Dr. Kursat Kara and has mentored multiple Undergraduate Student Researchers at the KARA Lab.


He is a part of the Tau Beta Pi Engineering Honor Society, a student campus champion for ACCESS at Oklahoma State University and served as the Vice-president of MAE Graduate Student Association. His longtime career goal is to integrate the strengths of Machine Learning and Artificial Intelligence into scientific computing.

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