I’m a data & systems guy obsessed with clean pipelines, forensic accuracy, and making “impossible” workflows actually…usable.
- 🎓 Senior @ University of Pittsburgh (CBA)
- Business Analytics & Accounting (Honors)
- Minor in Italian
- 🧮 Teaching Assistant — Business Analytics II (Python, pandas, ML)
- 🧪 Forensic & Litigation Consulting / Data & Analytics experience
- 🛠️ Builder of audit, ETL, scraping, and document-intelligence tools that feel like real products, not class projects.
I like solving problems where:
- The data is messy.
- The stakes are high (audit, compliance, legal, risk).
- The UX actually matters for non-technical users.
AI-assisted audit & forensic analytics framework focused on:
- Journal entry anomaly detection, Benford’s Law, vendor & payment risk
- Evidence-linked workpapers (JSON schemas, hashed attachments, lineage)
- Alignment with PCAOB, ISA, AICPA, COSO concepts
- Data quality + governance via dbt, Great Expectations, and clear documentation
Tech: Python, SQL, dbt, Great Expectations, cloud warehouses, LLM integration
Goal: Give audit teams an actually usable AI-first toolkit that is explainable, repeatable, and regulator-friendly.
A blueprint-style, production-minded document parsing toolchain.
- Built around robust PDF parsing + OCR (e.g. Docling / Tesseract style pipelines)
- Structured extraction for contracts, invoices, workpapers, and audit evidence
- Designed for packaging as a real desktop/CLI app (PyInstaller / Electron-style flow)
- Emphasis on: transparency, logs, reproducibility, clean APIs
Focus: Turn ugly PDFs into trustworthy, analysis-ready datasets.
An advanced, modular scraping framework with:
- Smart CSS/HTML pattern detection + container recognition
- Playwright-based automation & interactive GUI concepts
- Re-usable configs for legal, compliant data collection
- Designed for investigators, analysts, and power users
Keywords: Playwright, Python, modular architecture, “Codex-friendly” prompts
A system that treats playlists like context, not just lists.
- Embeddings + similarity (e.g. Faiss-style)
- Metadata-aware suggestions based on mood / theme
- API-driven design aimed at scalable deployment
Languages & Data
- Python (pandas, NumPy, PyTorch basics, FastAPI)
- SQL (analytical queries, modeling, optimization)
- R / basic stats & forecasting concepts
- Markdown, LaTeX when needed
Data & Infra
- Databricks, Snowflake, SQL Server
- dbt, Great Expectations
- ETL design, audit-ready data modeling
Tools & Dev
- VS Code / JetBrains
- Git & GitHub (obviously)
- Docker basics, packaging, automation scripting
Domains
- Audit & forensic analytics
- Financial/reporting integrity
- Operations & supply chain analytics
- Document intelligence & LLM-powered workflows
- TA for Business Analytics II — helping students get comfortable with Python, pandas, and applied ML.
- Leadership roles in Delta Sigma Pi & Sigma Chi (operations, treasury, tech, and comms).
- Regularly build:
- Internal dashboards
- Notion & documentation systems
- Study guides, formula sheets, and technical how-tos
I care a lot about:
- Making complex systems understandable to non-technical users.
- Writing docs that someone else’s future intern can follow.
- Building open, well-documented tooling others can extend.
If you’re working on:
- audit / risk / forensic analytics,
- AI for accounting / compliance,
- or high-signal data tooling for real workflows,
feel free to reach out or open an issue — I like collaborating on serious problems.
“Strong systems, clear evidence, honest data.”