My research goal is to combine machine learning, computational research, and aerospace engineering together. Specifically, I focus on applying machine learning to Multidisciplinary Design Optimization (MDO) for electrified aircraft. Right now, I'm under Professor Gökçin Çınar in Integrated Design of Efficient Aerospace Systems (IDEAS) Lab.
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first year
I'm currently first year. My responsibility is divided into 3 tasks:
Taking courses (to pass Preliminary Examination)
Graduate Student Research Assistant (it's like a job to feed myself)
My own research. Currently on applying Graph Neural Networks to Discrete Optimization.
I'm taking AEROSP 588, 552, and 540. AEROSP 588 is Python coding about optimization algorithms, so it does align well with my research. AEROSP 552 is also coding class in C++. I'm doing computational research, so coding course is helpful. And finally AEROSP 540 is Dynamics. It's neither related nor helpful but I'm just good at Dynamics, so it will be an easy pass for me on the Prelim.
For my GSRA, I'm on NASA AACES project. It's my first large large project, so I'm quite overwhelmed by its scale but I'm getting more used to the project over time.
After these responsibility, I can finally spend time on my own research. I have been reading papers from people in my lab and I find modeling hybrid propulsion system as graph an interesting topic. Therefore, I plan to dig deeper into this and try to apply Graph Neural Networks into this problem. I also try to align my course projects in AEROSP 588 and 552 with this topic so that I can save some time on this haha.
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