Currently an Extern @PfizerI study Information Technology at Florida State University and focus on hands-on projects that prepare me for entry-level IT work.
A small portfolio of coursework and personal projects demonstrating systems configuration, basic networking, and troubleshooting approaches I built while at Florida State University.
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Pfizer · ✅ Verified by Extern · ⏱️ In progress
Prototype AI-powered document intelligence with Pfizer—using OCR, LLMs, and RAG to automate real enterprise PDF workflows and build a standout portfolio project.
I study Information Technology at Florida State University and focus on hands-on projects that prepare me for entry-level IT work.
I am Emma Wooley, a junior at Florida State University pursuing a Bachelor's in Management Information Systems and Finanace. I am building practical skills in IT fundamentals and preparing for my first professional role through coursework and hands-on projects.
Externships
Pfizer Advanced: AI-Powered Document Insights & Data Extraction Externship
In progress
Experience
Food and Beverage Assistant
Sweet Lou’s Concession Stand · August 2023 — June 2024
Pfizer Advanced: AI Powered Document Insights & Data Extraction, Externship · Sept. 2026 — Current
Education
Florida State University
B.A MIS & Finance · Class of 2028
Skills
✅ Verified by Extern · ⏱️ In progress
Prototype AI-powered document intelligence with Pfizer—using OCR, LLMs, and RAG to automate real enterprise PDF workflows and build a standout portfolio project.
The externship prototyped AI-powered document intelligence for enterprise PDF workflows using OCR, LLMs, and retrieval-augmented generation. Work included researching LLM fundamentals and producing explanatory material about model architectures, training signals, and common capabilities and limitations. The deliverable explained how LLMs process and generate text for document tasks.
I produced a written submission that explained LLM concepts, detailed tokenization and probability-based text generation, described transformer self-attention, and summarized pretraining, fine-tuning, and evaluation practices including accuracy and bias checks.
I explained what a Large Language Model is, described tokenization and the transformer self-attention mechanism, and outlined pre-training, fine-tuning with human feedback, and evaluation steps for accuracy and safety.
