Projects

In a class project, we examined the impact of GitHub Copilot on Test-First Development, a software development paradigm where tests are written before implementation. We conducted a between-subjects (no Copilot vs Copilot) pilot study, consisting of coding tasks and pre- and post-task surveys. Participants iterated between writing a comprehensive API and test suite for a problem, with or without Copilot. We found that while Copilot enhanced coding speed, it resulted in superficial problem comprehension and decreased scope of the test suites.

I introduced a unified, hierarchical meta-representation of sequence data structure called the structural temporal graph (STG), a k-partite directed acyclic graph, and applied it to symbolic music. Then, I used simulated annealing to develop a measure of structural distance between two music pieces rooted in graph isomorphism. Finally, I combined the formal guarantees of SMT solvers with nested simulated annealing over structural distances to frame and solve the dually NP-hard combinatorial optimization problem of music structure summarization.

In order to perform genomic data analyses, bioinformaticians are frequently forced to do cubmersome pre-processing of their data. To addres these challenges, in a class project, we developed pgen-rs, a tool enabling end-users to write their data wrangling requirements in natural language enhanced by LLM suggestions. pgen-rs converts natural language to a DSL that is executed by out Rust-based high-performance genomic data processor enabled by the PLINK file format.

In a class project, we developed ProCon, a tool implementing continuous, rule-based enumeration for just-in-time bottom-up search in SyGuS problems. Programs are enumerated in order of continuous, nonrounded weights as determined by a probabilistic weighting function. ProCon thus leverages the full power of the probabilistic model since it does not round the probabilities to discrete weights as size-based enumeration does.

Under the mentorship of Dr. Ben Wiedermann at Harvey Mudd College, I created MusAssist, a DSL for music notation bridging the abstraction gap between music theory and composition. MusAssist gives end-users the novel ability to describe musical templates, such as cadences, at precisely the abstraction level as the music theoretic structures they represent, which models what a composer would organically conceive when composing by hand. I designed MusAssist’s syntax using a participatory design approach, and then wrote a Haskell-based compiler that translates MusAssist to MusicXML, a lower-level language accepted by most music notation software, for further manual editing.

Under the mentorship of Dr. Mark Huber at Claremont McKenna College, I created a novel system of Markov chains using inverse transform sampling to rapidly generate musical sketches, giving end-users the ability to create custom-length music in the style of a desired piece.

In an independent study during my undergrad, I assisted Dr. Christopher Raphael at Indiana University Bloomington on a project investigating audio algorithms for music score alignment. We developed a novel algorithm to synchronously assemble remotely recorded audio tracks without click tracks in order to address the need for remote media collaboration induced by the ongoing COVID pandemic.

In a class research project during my first year of undergrad, I created an original Python-based model to sonify genetic material by translating DNA to MIDI piano chords. By mapping nucleotides and codons to musical chords, my work introduced a novel and straightforward means of conceptualizing both the structure of a gene and the processes of biological splicing and translation that is accessible to users of all scientific backgrounds.