Sarah Gusman Cybersecurity track
Skills
- Machine Learning Basics
- ETL Workflows
- R
- Pandas
- NumPy
- Scikit-learn
- Seaborn
- Microsoft Excel
- Microsoft PowerPoint
- Microsoft Word
- Microsoft Access
- Figma
- Adobe Creative Cloud
- Canva
- Genetic Algorithms
- Predictive Analytics
- Data Visualization
Featured project
Shadow KEDI — Open-Source Shadow IT & Insider Threat Detection Platform
Shadow KEDI is a TTPR capstone project that combines statistical anomaly detection with a supervised machine learning classifier in a hybrid behavioral detection system for Shadow IT and insider threats. In an ML and data engineering role, Sarah engineered a synthetic dataset simulating six Shadow IT attack scenarios (220 users, 10,000 events) in Python, used it alongside the CERT r4.2 dataset, and built a heuristic anomaly detection model with Pandas, NumPy and Scikit-learn that reached an F1 score of 0.9653. She also applied a Genetic Algorithm for feature selection and hyperparameter tuning, reducing 38 candidate signals to the 18 most predictive.
Certifications
- Cornell University Certificate in User Experience and User Interface Design
Resume
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Beyond the resume
Clubs and activities
- Teacher's Assistant at Project Reach, facilitating implicit bias workshops for youth
Meet Sarah
Tell us who you are and what the role is. Darion Cole from TTPR will follow up by email and introduce you to Sarah.