RESEARCH VISITING SCHOLAR - GEORGIA TECH
Daniel Guggenheim School of Aerospace Engineering
RESEARCH VISITING SCHOLAR - GEORGIA TECH
Daniel Guggenheim School of Aerospace Engineering
During this research internship at Georgia Tech’s NCAE Lab (Nonlinear Computational Aeroelasticity Laboratory) in August 2025 under the supervision of PI. Prof. Marilyn J. Smith, I contributed to ongoing work on the numerical modeling of unsteady aeroelastic phenomena in aerospace applications using Machine Learning Gaussian Regression Process.
Article currently in perr-reviewing : "Accelerating Aerodynamic Load Prediction Gaussian Process - Surrogate Modeling for Proprotor–Wing Unsteady Aeroelasticity"
Relevant courses :
• AE6030 Unsteady Aerodynamics
• AE4331 and AE6333 Rotorcraft Design I
The focus was on computational approaches to fluid-structure interactions, specificaly on the simulation and analysis of aerodynamic behavior under unsteady flow conditions. This experience allowed me to strengthen my skills in computational fluid dynamics (CFD), Machine Learning of Aerodynamics, improve my understanding of aeroelastic modeling strategies, and develop my ability to interpret and analyze complex simulation results. Working in a high-level research environment also helped me gain valuable insight into academic research methodologies and collaborative project workflows.
RESEARCH ASSISTANT - LISPEN LABORATORY
Robotic Disassembling & Remanufacturing
I assisted Dr. Olabi at the LISPEN Robotics Lab (ENSAM) on research applying MIT CSAIL’s ASAP framework to robotic remanufacturing sequence automation. My work focused on exploring computer vision and 3D scanning for automated robotic disassembly, developing machine learning-based semantic recognition to enhance robot autonomy in dismantling tasks, and optimizing planning and automation algorithms for efficient manipulation in constrained environments.
This experience strengthens my technical skills in robotics, control, and AI, while exposing me to research methodologies such as literature review, benchmarking, and algorithmic analysis. It also allows me to deepen my understanding of automation challenges at the intersection of sustainable manufacturing and aerospace systems—fully aligned with my long-term academic and professional ambitions.