Neuroscientist · Aging and Neurological Diseases

Victor Chimaobim
Onuh

Junior Specialist at the Baraban Lab, UCSF. I am interested in the relationship between the immune system and neurological disease, which I study using immunofluorescence, imaging, and computational tools.

Alzheimer's Disease ML for Neuroscience Neurodegeneration Epilepsy
Victor Chimaobim Onuh

About Me

I am a neuroscientist based in San Francisco. My research interest sits at the intersection of immune biology and brain aging. I am motivated by the question of how immune processes drive neural vulnerability in Alzheimer's disease, and how we might detect or slow that decline before it becomes irreversible.

Currently at the Baraban Lab (UCSF Department of Neurosurgery), I study immune system behavior in mouse epilepsy models following MGE transplantation. I previously worked in the Seeley Lab (UCSF Memory and Aging Center), generating neuron tracings to train ML models for automated segmentation of FTD/ALS postmortem tissue, and in the Larkum Lab at Humboldt University in Berlin, conducting advanced image analysis on cortical layer 5 pyramidal neurons.

I hold a B.S. in Natural Sciences from Minerva University and am a first-author in the Journal of Alzheimer's Disease. I serve as an Associate Editor and Peer Reviewer at the same journal.

Python scikit-learn SHAP R MATLAB Streamlit Immunofluorescence FreeSurfer
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Current Position
Junior Specialist · Baraban Lab, UCSF
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Education
B.S. Natural Sciences · Minerva University, 2025
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Certifications
  • Data Science (6-month program) · Tinzwave Academy
  • AI Fluency: Framework & Foundations · Anthropic
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Research Interests
Aging biology · Neuroinflammation · Neurological diseases
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Publication
First author · Journal of Alzheimer's Disease · 2025
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Global Experience
Studied in 7 countries

Projects

Using computational approaches to study diseases.

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Python XGBoost scikit-learn

Heart Disease Risk Classifier

About 55 people die of sudden cardiac arrest in the U.S. each day, often without knowing they were at risk. I built this classifier to explore how routine clinical features can surface cardiovascular risk before an event. An educational project, not a clinical tool.

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Python Decision Tree scikit-learn

Lung Cancer Detection

Lung cancer is the leading cause of cancer death in the U.S., largely because most cases are caught late, when survival drops sharply. I built this interpretable decision-tree classifier to explore how clinical features might flag risk earlier, visualized as a transparent tree. A learning project for educational purposes.

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Python Logistic Regression NLP

Email Spam Detector

A foundational NLP exercise: a logistic-regression classifier with Bag-of-Words vectorization reaching 98.9% accuracy on 5,728 emails, saved as a deployable pipeline. It grounds text-processing and pipeline skills that transfer to biomedical-text and clinical-notes analysis.

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Coming soon

More Projects

Additional projects in computational neuroscience and biomedical ML are in progress. Follow on GitHub for updates.

Publications

2025
Journal Article · First Author

Advancing Alzheimer's Disease Treatment: A literature review on senolytic intervention

Onuh, V.C.

Journal of Alzheimer's Disease (2025)

A systematic review examining senolytic therapies, agents that clear senescent cells, as a potential intervention in Alzheimer's disease, synthesizing evidence across animal models and clinical research. It grew out of the question at the center of my work: whether targeting the aging immune processes that drive neuroinflammation, rather than downstream plaques alone, could slow neurodegeneration.

Academic Service

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Associate Editor
Journal of Alzheimer's Disease
January 2026 – Present

Managing manuscript submissions, coordinating peer review, providing editorial recommendations, and participating in the annual Alzheimer Award selection process.

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Peer Reviewer
Journal of Alzheimer's Disease
May 2025 – Present

Reviewing submitted manuscripts in Alzheimer's disease research — evaluating methodology, statistical rigor, and clarity; providing constructive feedback in accordance with journal standards.

Get in Touch