Final Project for Mathematics of High Dimensional Data (Graduate Level)
We present a scalable model to represent multi-subject, multi-dataset fMRI data in the same learned latent space. Firstly, we cluster similar subjects based on the tensor decomposition of the input data. Then, we use regularized gradient descent on the data from similar subjects to learn the shared latent space. Our best model consistently achieves accuracies over 85%. Moreover, our chosen regularization ensure scalability and low computational complexity.
Final PaperFinal Project for Advanced Algorithm Design (Graduate Level)
We explore time-evolving graphs, where each node's 'opinion' is updated at every time-step to match the majority opinion held by its neighbors. After several time-steps, a winning opinion is declared by performing a majority vote on the population. We provide theory which shows that certain graphs eventually hold a majority incorrect opinion, despite each node being initially biased towards the correct opinion. Furthermore, we empirically investigate the effects of various seeding and opinion-recovery methods.
Final PaperFinal Project for Wireless and High-Speed Integrated Circuits and Systems (Graduate Level)
We design a low-noise low-power transimpedence amplifier with a high bandwidth of 7 GHz and gain of over 4.3 kΩ. The design was analytically analyzed using the generalized time-constant method and simulated using Cadence.
Final PaperFinal Project for Robotics and Autonomous Systems
We present a design for a portable mechanical ventilator offering both volume and pressure controlled support, accessible through a clean user-interface. We construct sensors and valves from common hardware and 3D-printed parts, significantly reducing the cost-of-build without sacrificing safety. Our final design costs under $300, which is about one-tenth the cost of off-the-shelf solutions.
Final Paper Pitch DeckIBM Qiskit Camp and Hackathon
We implemented the Richardson extrapolation technique to reduce gate-errors in noisy intermediate-scale quantum computers, as proposed by Kandala, et. al. 2019 (Nature 567, 491–495).
Presentation Code