Research

I am a PhD candidate in Mechanical Engineering working on the aeroelastic design optimization of wind turbines. My work sits at the intersection of large-scale gradient-based optimization and machine learning, with the goal of designing turbines that are more cost-effective.

Aeroelastic design optimization of wind turbines

One or two paragraphs on the core problem: coupling structural and aerodynamic models, the design variables you optimize over, and why it matters (levelized cost of energy, blade mass, loads, etc.).

Gradient computation

Describe your work on efficient gradient computation — adjoint methods, automatic differentiation, coupled-system derivatives — and why accurate, scalable gradients are the bottleneck for high-dimensional design.

Deep-learning surrogates

Describe your surrogate-modeling work — what quantities you emulate, the architectures you use, and how the surrogates plug into the optimization loop.

Selected publications appear on the Publications page; software and tools are on the Projects page.