I am currently…

• Health Data Scientist at Guidehouse
• MSc Candidate in Bioinformatics
• Finance Director at Black In Neuro

My name is Victoria McCray, and I’m a bioinformatics researcher who thrives at intersections between disciplines, between methods, between the patterns we can measure and the meanings we can extract from noise. My work spans computational neuroscience, public health data science, and open science education. I’m currently a Master of Science Candidate in Bioinformatics at Northeastern University. I earned my Bachelor of Science in Psychology (Honors) with a minor in Data Science from Northeastern University. I joined the Black In Neuro team in 2021 and now serve as Director of Finance in my second term.

Research Journey

Currently, I develop statistical pipelines and machine learning methods for large-scale biomedical data. This means analyzing neuroimaging studies of psychiatric disorders one day, processing millions of health records for federal surveillance systems the next, and supporting open and transparent data science capacity initiatives on another day. The problems change, but the underlying question stays the same: How do we find signal in noise?

My path into computational research started with an unexpected realization: linguistics is really just math, but with your ears. As a psychology student at Northeastern, I was interested by how the mind parses streams of phonological information into conceptual meaning along with several unifying constraints across global languages; how noise becomes structure, how chaos becomes understanding. This led me down a series of questions about information processing: Why do humans need novelty and learning? How do we abstract complex information into flexible symbolic representations? What happens when streams of information (from the internet, from our environment, from other people) interact with how peopled are designed for organization?

For me, the most interesting problems live at the boundaries between fields. Understanding cognition may require neuroscience, but also biology, statistics, computer science, linguistics, philosophy, and increasingly, ethics.

What I Do Now

As a health data scientist at Guidehouse, I architect data quality frameworks and statistical pipelines for national public health surveillance systems. I work with epidemiologists, neurologists, and data engineers to transform messy, large-scale health data into reliable insights that inform policy decisions and community care. This includes developing prevalence estimation methods for neurological diseases at the national level, and building systems that track overdose patterns across jurisdictions.

Concurrently, I’m completing my MSc in Bioinformatics at Northeastern to grow my expertise in computational biology and statistical genomics. I’m particularly interested in how multi-omic approaches and network analysis can generate insight into disease mechanisms by finding meaningful structure in high-dimensional, noisy data.

Building Research Capacity

Beyond my technical work, I’m committed to democratizing access to STEM and computational research tools. In February 2025, I led a two-week neuroimaging data science program at Lilongwe University for Agriculture and Natural Resources in Malawi, teaching Python, neuroimaging analysis, and research ethics.

Research Interests

• Computational Neuroscience: Brain connectivity analysis, reward processing, information integration, neuropsychiatric disorders
• Biomedical Data Science: Large-scale health surveillance, epidemiological modeling, signal detection in noisy clinical data
• Machine Learning for Biology: Statistical genomics, multi-omic integration, pattern recognition in complex biological systems
• Human-Computer Interaction & Neuroethics: How technology shapes cognition, bias in AI/ML, ethical data collection in vulnerable populations
• Research Methodology & Equity: Open science in resource-limited settings, community-engaged research, reproducibility

View my CV here.