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: April 2026

November 2025 — SC25 poster presentation

I presented my master’s thesis poster, Advancing EEG Signal Analysis with Quantum Machine Learning, at Supercomputing 2025 (SC25) in St. Louis. The poster summarized results from my written thesis comparing variational quantum circuits to a tuned Random Forest baseline.

Full poster image (PNG)

December 2025 - Michael Bruno Award for Excellence in Microbiome Science

Received the Michael Bruno Award for Excellence in Microbiome Science through the C-MĀIKI #MahiMicrobe2025 program for a proposed Rapid ‘Ōhi‘a Death microbiome project focused on comparing microbial communities from surviving and infected trees using amplicon sequencing, computational biology, and machine learning, in collaboration with Dr. Lenore Pipes.

• 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: Scalable Hash-Based Approximate Retrieval for High-Throughput Reference Database Construction - Ongoing in 2026

Under the supervision of Dr. Lenore Pipes at UH Manoa I have designed and developed SHARD-DB. SHARD-DB is an alignment-free reference database construction project for large nucleotide collections. The work is a combination of bioinformatics and high-performance computing, with a focus on making database generation more scalable than traditional BLAST-heavy approaches. Instead of relying on exhaustive expansion and downstream filtering, SHARD-DB uses a retrieval-first strategy to identify promising regions efficiently before refining candidate loci for database construction. The broader goal is to make reference database building faster and more practical for high-throughput metabarcoding workflows. Full methodological details are still in preparation, but early results are promising. My initial results suggest that SHARD-DB significantly reduces runtime relative to existing reference database construction pipelines while maintaining practical utility for high-throughput workflows. Currently in final testing.

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 now!.

SightShift: VR Assistive Technology (Completed)

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 — Poster presenter; 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