Christopher Williams

Designed and deployed an LLM-powered document Q&A system using RAG architecture with FAISS, LangChain, and OpenAI APIs, improving internal knowledge retrieval efficiency by 70%. Fine-tuned LLaMA 2 and Mistral-7B models using QLoRA and PEFT techniques on domain-specific data, reducing hallucinations by 35% and improving factual consistency in enterprise chatbots.

Built and deployed an LLM-based summarization system for EHRs using T5, DistilBART, and BioGPT, reducing clinician review time by 65%. Constructed a multi-modal fusion model using Transformers + CNNs to analyze both genomic sequences and patient imaging data, aiding in personalized treatment planning.

Led an initiative to modernize internal ML infra using Kubeflow and Kubernetes, enabling reproducible experiments and scalable training. Developed time-series forecasting models with Prophet, ARIMA, and LSTM, forecasting macroeconomic risks and improving hedging strategies.

Built an NLP-powered smart search system for ecommerce using Elasticsearch, BM25, and sentence embeddings, boosting search-to-purchase conversions by 12%. Engineered a reinforcement learning-based pricing optimization engine, increasing profit margins during flash sales by 10%.

Focused on advanced topics in machine learning and artificial intelligence. Completed a minor in Computer Science, enhancing foundational knowledge in algorithms and data structures.

Studied the principles of data science, including statistical analysis and data modeling. Completed a minor in Mathematics, strengthening analytical and problem-solving skills.