Based on successful work in building cross-domain translation systems using Variational Autoencoders to translate between MNIST handwritten digits and spoken utterances of those digits, I attempted to build a model that could translate between quantized MIDI musical data and choreography (represented as quantized sequences of pose data).

One of the biggest risks to the future development of artifical intelligence is the consolidation of AI research within just a handful of institutions and corporations due to the ever-rising computational and data demands of our increasingly complex statistic models. That's why, in my attempt to build a competitive model for the SQuAD 2.0 question-answering task, I focused on lightweight alternatives to the popular transformer-based approach dominating much of the current NLP landscape.

After building much of my AI experience in projects around audio and/or reinforcement learning, I was excited for the opportunity to branch out in my recent work. This paper represents one such exploration in a new direction, and probes the applicability of transfer learning, in combination with textual descriptors of real estate, to provide signals for visual price estimation.

Graph neural networks (GNNs) were a fascinating new technology to dive deep on in my first quarter at Stanford. After discovering the application of reinforcement learning to the node preduction in the form of Policy-GNN, I attempted to further augment the model performance by replacing the GNN component with a state-of-the-art, attention-based, GAT network.

My first research project enabled me to hit the ground running in applying deep learning to audio problems. Using a physical-modelling approach to sound synthesis for digital models, we attempted to build a system that could solve the inverse problem - identifying the source geometry from a sound of the object being struck.

For my senior honors thesis at UNC Chapel Hill, I sought to bring together my experience with reinforcement learning from my time at Berkeley's AI and Robotics research group with my love for music. I was interested in exploring inverse RL approaches due to their high sample efficiency - enabling the development of an AI model trained on the sensibilities of an individual musician.