Vraj Patel

Interactive mental-health NLP stack: intent classification and domain-specific responses using PyTorch, Hugging Face Transformers, and TensorFlow/Keras. Fine-tuned LLaMA 3.2 1B with LoRA adapters (including efficient QLoRA via Unsloth) for clinical text generation, alongside BERT-style classifiers for supervised mental-health labeling. Reached 0.79 Macro-F1 on aggregated clinical data while cutting training VRAM by 50%+; built an ETL pipeline over 53,000+ text samples for fine-tuning.

SeeMyRace is a full-stack, Dockerized Flask platform for race photography—Google Drive ingest, bib and selfie search, and Google OAuth. I led bib-number detection research (including SWT), shipped much of the main site UI, flagging and moderation flows, and backend connectors into the ML inference pipeline. For OCR, I evaluated Tesseract and PaddleOCR; PaddleOCR looked strongest for our task, but at the time its docs skewed Chinese-heavy and were hard to operationalize for a lean MVP.

I architected DeepFace-based semantic facial recognition targeting sub-50ms inference and an asynchronous ingestion pipeline that kept heavy model work off the request path, with UI staying responsive (under 100ms) during bulk uploads. Separately, the product’s face matching also relies on PostgreSQL with pgvector (cosine similarity over embeddings)—that vector search path wasn’t my direct implementation focus. The repo has since gained contributors and more CV plumbing (e.g. YOLOv8, InsightFace), while goals stay aligned.

A Princeton University Studio Lab research initiative to create the largest unified dataset for Indian political linguistic analysis. Engineered a resumable Selenium crawler to index the Parliament Digital Library (handling dynamic pagination and session state), processed 40+ GB of election speech audio (Modi & Gandhi, 2014/2019 campaigns) through an AWS Transcribe/Translate pipeline, and digitized 40+ years (1981–2024) of Lok Sabha and Rajya Sabha debates. Designed a PyMuPDF and FuzzyWuzzy text extraction engine to structure thousands of raw PDF statements, mapping OCR output to standardized Ministry entities at scale.