Biomedicine and Research Computing Consultancy specializing in oncology, drug discovery, AI/ML pipeline integration, and custom bioinformatics architecture.
Deploying advanced machine learning workflows (large-language models, vision transformers, representation learning) to automate and optimize biological data analysis. Bridging the gap between computational biology and wet-lab experimental design.
Building reproducible, scalable architecture for cloud and HPC deployment with mature software. Expertise in architecting high-throughput biological data pipelines to eliminate manual bottlenecks and increase accessibility for wet-lab scientists.
Evaluating the molecular mechanisms of disease from first principles. Specialized focus on cell biology, medicinal chemistry, and therapeutic development.
Programmed and open-sourced a Python package engineered to automate the analysis of high-throughput microscopy images. Reduces manual analysis time by 95% and saves >1000 person-hours annually.
Developed a scalable Snakemake pipeline designed for rapid end-to-end processing and reproducible analysis of next-gen sequencing (NGS) CRISPR screen datasets on high-performance computing (HPC) clusters.
Engineered and deployed an interactive Stable Diffusion (SDXL) pipeline on Yale's McCleary A100 GPU clusters for the 2025 AI at Yale Symposium. By mapping biological evolutionary principles directly to a generative model's latent space, this project demonstrates advanced PyTorch deployment, complex dependency management, and seamless visualization in HPC environments.
Engineered a self-hosted web infrastructure from scratch using a Raspberry Pi and Nginx. Demonstrates complete end-to-end control over Linux system administration, DNS routing, secure networking, and continuous deployment outside of managed cloud environments.
Provided scientific advisory, biological mechanism review, and manuscript auditing for major scientific publications and a #1 NYT bestselling author, ensuring accuracy and fluency in oncology and cellular biology.
Jael Labs is operated by James Elia, a software developer and PhD candidate in Pathology and Molecular Medicine at Yale University.
Operating at the intersection of computer science and translational medicine, James focuses on building deep mechanistic intuition from first principles. His previous research elucidated the mechanism of action for novel cancer therapeutics, leading directly to a $1.3B biopharma acquisition.
Today, his consulting leverages that rigorous scientific foundation to translate complex biological problems into clean computational methods and precise language.
Outside the lab, James has run ultra-marathons, competed nationally for UMass Powerlifting, and won medals in BJJ and judo. He believes there is beauty in the method and brings this dedication to every endeavor.
"When you're a carpenter making a beautiful chest of drawers, you're not going to use a piece of plywood on the back, even though it faces the wall and nobody will see it. You'll know it's there, so you're going to use a beautiful piece of wood on the back. For you to sleep well at night, the aesthetic, the quality, has to be carried all the way through."
— Steve Jobs