Berkeley, California, United States
Spring 2021 Project: ShallowSleep
A paper released a few years ago from MIT proposed an alternative solution to sleep tracking: determining sleep stages with a ML model using radio signals. We implemented this model, and achieved a fairly high accuracy. I experimented with implementing a ResNet model to be our encoder. I also helped create a website to display the data using React.
Fall 2021 Project: Scribe
We constructed a model which has the ability to generate images of a user’s handwriting using very few samples of the handwriting by combining the ScrabbleGANN and Reptile architectures. We added an extra discriminator to ScrabbleGANN called a “Recognizer” which pushes the GANN to generate text in the same style as a specific user.
Spring 2022 Project: Launchpad x DroppTV
I worked with a startup, DroppTV, to create a model to effectively classify a shoe’s brand and model from video data. I experimented with self-supervised contrastive learning, which aims to create an embedding space where similar samples are close together. I also tuned training hyper parameters to increase model performance and generalizability on two separate datasets.