I work with Prof. Patrick Pannuto and Raymond Dueñas on CPU–GPU split inference for CNN models on NVIDIA Jetson edge devices. The core question: given a model too large to run entirely on a GPU, at what layer should you cut it — and does the answer change when you optimize for energy rather than throughput?

This project produced my first full research paper, presented at UCSD SRC 2025 and being integrated into a submission to an international conference (name redacted for double-blind review). I'm grateful to Pat and Raymond for taking a chance on me and teaching me how research actually works.

I spent a summer at IITD SeNSE working with Prof. Ravibabu Mulaveesala on signal processing for nondestructive testing. The work involved adapting an open-source pipeline to reconstruct subsurface images using Fourier and correlation transforms, even under noisy measurement conditions.

This was my first mentored research experience — and where I learned what it actually means to do science: reading papers critically, communicating findings clearly, working through problems as a team. Tea time with Ravi sir and the PhD students was also excellent.

I worked on the sensors and circuitry team for a lunar rover prototype, improving Arduino-based control code to synchronize UV and Hall effect sensors. The team repurposed an RC car as a base — which meant adding custom suspension to keep the sensors stable enough to collect consistent data.

Through signal processing improvements and low-level code refinements, we pushed pipeline efficiency up by ~65% and sensor precision by 2.5×. I presented our findings and poster to the NASA California Space Grant Consortium at the end of the summer.