00 / Entrance
Data / AI / Systems
I design data and AI systems around clarity, structure, and execution.
MS Information Systems candidate focused on applied AI, analytics, financial modeling, cybersecurity, and business strategy.
Site Thesis
Applied machine learning, systems thinking, and business analysis presented as structured evidence instead of generic portfolio filler.
01
Featured Projects
Projects are presented as case files: constrained, legible, and built around proof rather than decorative marketing copy.
Plate 01
2026-04
Built a classification pipeline to predict whether an Airbnb listing would receive a perfect rating using structured and text-based features.
System Type
Machine Learning
Delivery State
Completed
Tools
Proof Point
~0.815 validation ROC-AUC across structured and text features
Plate 02
2026-03
AI-driven real estate analytics system designed to predict home sale price and sale timing using Montgomery County property data.
System Type
Applied Analytics
Delivery State
Completed
Tools
Proof Point
Dual workflow for sale price and sale timing analysis
Plate 03
2026-02
Conceptual detection system for identifying suspicious insurance activity and cybersecurity-related fraud patterns.
System Type
Risk Analytics
Delivery State
Completed
Tools
Proof Point
Fraud monitoring framework spanning claims risk and cyber signals
Plate 04
2025-12
Built a framework for running and evaluating chess bots in repeatable tournament settings.
System Type
Software Engineering
Delivery State
Completed
Tools
Proof Point
Repeatable evaluation framework for tournament-grade bot comparison
02
Experience
A sharper presentation layer for roles where research, communication, and technical systems came together.
01
2025
Remote
EcoStart
Supported customer discovery and supplier matching for companies pursuing sustainability goals, with a focus on research, outreach, and early-stage platform validation.
02
2024
University Park, PA
Penn State
Assisted Professor Mark D. Gough with structured information collection and automation scripts for research-oriented data gathering.
03
System Archive
The coursework layer highlights the undergraduate core behind the projects: programming, algorithms, databases, systems, statistics, and business context without exposing transcript-only details.
Penn State B.S. Computer Science
Undergraduate coursework built a technical foundation across programming, algorithms, databases, operating systems, theory of computation, programming languages, numerical computing, probability, statistics, computer organization, and business fundamentals.
Public-facing archive only. Grades, GPA, student ID, repeated-course history, and audit status are intentionally excluded.
Computer Science Foundations
Programming, algorithms, systems, databases, theory, and software construction.
Data, AI & Analytics
Data mining, statistics, probability, numerical methods, and analytical modeling.
Systems & Architecture
Operating systems, computer organization, digital design, and low-level systems thinking.
Mathematics & Quantitative Foundations
Calculus, linear algebra, probability, statistics, and vector analysis.
Business, Finance & Management
Business fundamentals connecting technical systems to markets, operations, finance, and organizations.
Archive Scope
Computer Science Foundations, Data/AI/Analytics, Systems & Architecture, Math/Statistics, and Business/Finance.
Institution
Penn State University
B.S. Computer Science / University Park
04
Contact
The direction is harder-edged, but the goal stays practical: make the work legible, memorable, and recruiter-friendly.
The next pass will carry this system across the rest of the portfolio and replace placeholder project files with real case studies backed by your GitHub archive and coursework.