Four teams. One pattern: find the manual bottleneck, automate it correctly.
Built 8 Python agentic systems (Selenium, pandas, openpyxl, Outlook API) executing I-9 and immigration workflows across Workday, Equifax, and Sprintax.
Cut processing time from 1–3 hours to under 5 minutes per task, saving ~28 hrs/week and up to ~40 hrs/week during peak periods.
Built an Outlook-integrated email agent (Python + Microsoft Graph API + rule-based classification) that reads I-9/immigration inbox emails, classifies queries, pulls employee data, and replies without manual intervention.
Delivered ~$40K in annual savings and wrote documentation that lets non-technical staff run and troubleshoot the systems themselves.
OCT 2025 – PRESENTBuilt ingestion and validation pipelines in Python and R across field and sensor data sources, cutting manual cleaning time by ~30 hours per week.
Developed statistical agents that automatically flag missing values and anomalous sensor readings, catching 325+ data quality issues before they reached downstream models.
Set up computer vision preprocessing with torchvision to standardize and augment ecological image data, taking preprocessing from ~3 hours per batch to fully automated across 25,000+ images.
JAN 2026 – MAY 2026Designed and ran evaluation suites covering 200+ test scenarios to measure AI agent accuracy, response quality, and edge-case behavior.
Built automated LLM testing workflows with batched, parallelized execution, cutting manual evaluation time by ~60%.
Constructed benchmark datasets that surfaced failure modes feeding directly into model improvement cycles, improving total test coverage by 35%.
Presented evaluation findings and coverage gaps to the agent team in weekly syncs.
JUL 2025 – AUG 2025Built multi-granularity datasets (daily, weekly, monthly) from 5 years of raw time series data, engineering features to surface trend, seasonality, and autocorrelation.
Trained and benchmarked four forecasting models — ARIMA, Prophet, XGBoost, LSTM — with XGBoost outperforming the field by ~18% on weekly granularity, later adopted into the team's production pipeline.
Shipped a resampling frequency analysis that cut the team's trial-and-error preprocessing time by ~5 hours per experiment.
MAY 2025 – AUG 2025Things I built because I wanted to know if they'd actually work.
- Built a six-object ontology with four link types from six synthetic datasets, then architected six agentic workflows grounded directly in it.
- Chose SQL-over-ontology retrieval instead of embeddings, since exact case_ID matches mattered more than semantic recall.
- Root-caused a grounding failure to an unscoped case_ID, fixed it, and hit 93% grounding accuracy on a 15-case held-out test set.
- Cut response latency 4.2s → 2.1s by trimming the input payload to deal-relevant fields only.
- Built a RAG chatbot with OpenAI + LangChain, handling ingestion, chunking, and FAISS vector embeddings from scratch.
- Cut irrelevant retrieval by ~20% through iterative prompt engineering.
- Brought average response latency from ~4s to under 1.5s by optimizing chunk size and retrieval parameters.
- Deployed on Streamlit to 100+ users during a live testing period with no major failures.
- Built a torchvision fallback for damaged or missing tags, matching found-item photos to registered records via image embeddings and vector search.
- Built a React + Firebase lost-and-found platform mapping NFC/QR tags to dynamic, Firestore-backed recovery pages.
- Built an admin dashboard with search, filters, pagination, record stats, and CSV export.
The stack, end to end.
Foundations.
Coursework: Data Structures & Algorithms, Computer Systems, Probability, Applied Statistics, Database Systems