Cordero Core
Cordero Core is a highly skilled senior software engineer with over 14 years of experience successfully delivering innovative software solutions to healthcare, e-commerce, aerospace, and security industries. He has achieved a patent for his groundbreaking work in computational microscopy and digital pathology that has revolutionized imaging and analysis techniques for medical professionals. Cordero maintains active involvement in his field through mentoring startups and entrepreneurs as an advisory board member for the Journal of Small Business and Enterprise Development. He is currently focused on creating software solutions that enable scientific research, data management, and collaboration through his role as a Senior Software Engineer at the eScience Institute.
Sessions
Generative AI systems built upon large language models (LLMs) have shown great promise as tools that enable people to access information through natural conversation. Scientists can benefit from the breakthroughs these systems enable to create advanced tools that will help accelerate their research outcomes. This tutorial will cover: (1) the basics of language models, (2) setting up the environment for using open source LLMs without the use of expensive compute resources needed for training or fine-tuning, (3) learning a technique like Retrieval-Augmented Generation (RAG) to optimize output of LLM, and (4) build a “production-ready” app to demonstrate how researchers could turn disparate knowledge bases into special purpose AI-powered tools. The right audience for our tutorial is scientists and research engineers who want to use LLMs for their work.
We present Caustics, a tool to accelerate the analysis of gravitational lensing systems for the next generation of astronomical data. Caustics will enable precision measurements of dark matter properties, the expansion rate of the Universe, lensed black holes, the first stars, and more. In this talk I will discuss the benefits and challenges of how we used PyTorch (a differentiable and GPU accelerated scientific python package) to allow for fast development without sacrificing numerical performance. I will detail our development process as well as how we encourage users of all skill levels to engage with our documentation/tools.