Why Transonic Aeroelasticity Still Matters in the ML Era

Over the past year, I’ve been quietly building a few interconnected projects that sit at the intersection of aerospace, data, and women’s wellbeing.

I’m in the final stretch of writing a book on transonic aeroelasticity, drawing on my PhD work on nonlinear effects and my later experiments with machine learning for aerodynamic loads.

I’m also exploring how data and simple digital tools can support mental health for women 50+, building on a “Mental Wellness Planner” app I created during a recent web‑programming course.

In parallel, I’m sharpening my Python and data‑analysis skills and drafting a paper on nonlinear stiffness in transonic aeroelasticity.

The common thread: using rigorous math and code to make complex systems—whether airplanes or our own lives—more stable and resilient.

If you work in aeroelasticity, ML for physics, or women’s mental health and wellness (especially 50+), I’d love to connect or hear what you’re working on.

Originally posted on LinkedIn.