Stephanie Murray

Hi, I’m Stephanie Murray, a researcher and PhD student in Computer Science at the University of Hawai‘i at Mānoa. My work focuses on machine learning, data analysis, and algorithm development, with research interests in large-scale biological datasets and phylogenetic methods.

I have experience developing analytical pipelines for complex datasets, including work in biomedical signal processing during my master’s thesis. I’m interested in creating computational approaches that make challenging, high-dimensional data more accessible and interpretable.

I enjoy working on meaningful problems, learning from new ideas, and exploring creative solutions in computer science that can have a positive impact.

Updated: October 2025

SC25 Poster Accepted

My master’s thesis poster (Advacing EEG Signal Analysis wuth Quantum Machine Learning) has been accepted to Supercomputing 2025 (SC25) in November. I’ll be presenting results comparing variational quantum circuits to a tuned Random Forest baseline.

Preview: See the small thumbnail. I’ll share the full poster PDF after the conference.

• Computational Biology: Developing algorithms and analytical methods for large-scale biological datasets, with a focus on phylogenetics and environmental DNA (eDNA).
• Machine Learning: Designing and evaluating models for classification, prediction, and pattern discovery in complex, high-dimensional data.
• High-Performance Computing (HPC): Leveraging parallel computing and GPU acceleration to improve efficiency in data-intensive workflows.
• Data Analysis: Exploring techniques to address small, sparse, or imbalanced datasets through feature engineering and statistical modeling.
• Biomedical & Life Sciences Applications: Applying computational methods to datasets from neuroscience, genomics, and environmental monitoring.

SHARD‑DB: Sequence Hashing for Amplicon Reference Databases - Ongoing

SHARD‑DB is a fast, reproducible workflow for building locus‑specific metabarcoding reference databases (e.g., COI, 12S, ITS). It replaces slow alignment‑based expansion with hash‑sketch retrieval (k‑mer hashing & locality‑sensitive hashing), then applies biological filters (primer anchoring, expected amplicon length) and taxonomy annotation.

Current work benchmarks SHARD‑DB against CRUX on coverage, unique taxa, BLAST recall, and compute cost, targeting scalable reference DB builds.

UW Master’s Thesis (Completed June 25): "Exploring Quantum Machine Learning-Enhanced Models for EEG Data Classification"

This 103-page thesis investigates the application of Variational Quantum Circuits (VQCs) for classifying EEG signals. The work integrates quantum computing methods with classical preprocessing to improve classification performance for movement-related brain activity, using both simulation and real quantum hardware tests.

Across 40 experimental runs, the VQC achieved higher precision and recall than a tuned Random Forest baseline, demonstrating that compact quantum models can match or exceed classical pipelines for certain biomedical signal processing tasks.

Methods Used: Variational Quantum Circuits, classical preprocessing (band-pass filtering, PCA, CSP), and gradient-free optimization (COBYLA) for training quantum models.

Full thesis available via ProQuest Abstract. Publication expected January 2026.

SightShift: VR Assistive Technology

Worked in a team in conjunction with MXTReality to create a VR application simulating LHON (Leber Hereditary Optic Neuropathy) disorder using Unity and Oculus SDK. Focused on accessibility and assistive technology, promoting empathy and understanding of visual impairments through immersive experiences.

Methods Used: Unity game engine, VR development (Oculus SDK), and accessibility design principles.

• SC25 — Attendee & Research Poster presenter (accepted), Quantum ML for EEG classification.
• SC24 — Attendee & student volunteer; focus areas: HPC & quantum computing.

Email: contact at stephaniemurray dot org

LinkedIn: linkedin.com/in/s-a-murray