Conner Lambden

Built a vertically integrated biology AI with RAG access to full scientific papers and data, enabling question answering, automated bioinformatics analyses, graph generation, and visual knowledge graphs.

Built a stock/crypto/options prediction platform processing >10M data points/week with AI news summaries, sentiment and alternative data, and cloud systems that train and test millions of models per day to support options strategies.

Built and deployed RAG pipelines to surface actionable insights from large financial and business datasets and integrated LLM-based meta-reasoning to improve analytics accuracy.

Developed web-based analysis tools for biologists and implemented novel single-cell analysis methods (pseudotime, differential abundance, neighborhood embedding) to track and integrate immunological assays.

Developed and analyzed graphical models and algorithms to study T cell development and activation, analyzed bulk and single-cell RNA and PERTURB-seq data, and created a semisupervised gene–gene interaction search algorithm.

Completed undergraduate studies focusing on Molecular & Cell Biology (Immunology) and Computer Science with interdisciplinary coursework bridging computational and experimental biology.