UCSF Institute for Computational Health Sciences, NVIDIA Deep Learning Institute, & Hadley Lab
The emerging field of precision medicine demands a new approach to research that harnesses the power of data science. This requires biomedical researchers and practicing clinicians with skills in quantitative and computer sciences as well as modern computing and data infrastructure to support relevant research activities and implement findings in clinical practice. The UCSF Institute for Computational Health Sciences (ICHS) is working to build a foundation of faculty and knowledge assets in computational health sciences and bring together a community of thinkers interested in this emerging field. Our initiatives are focused on enhancing education and infrastructure as well as building community with the goal of advancing computational health sciences in research, practice, and education—in support of precision medicine for all.
The NVIDIA Deep Learning Institute (DLI) offers hands-on training for developers, data scientists, and researchers looking to solve the world’s most challenging problems with deep learning.
Through self-paced online labs and instructor-led workshops, DLI provides training on the latest techniques for designing, training, and deploying neural networks across a variety of application domains. Students will explore widely used open-source frameworks as well as NVIDIA’s latest GPU-accelerated deep learning platforms.
The Hadley Laboratory at UCSF translates big data into precision medicine and digital health. Our work generates, annotates, and ultimately reasons over large multi-modal data stores to better characterize disease. We develop state-of-the-art data driven models of clinical intelligence that drive clinical applications to more precisely screen, diagnose, and manage disease. We integrate multiple large data-stores to identify novel biomarkers and potential therapeutics for disease. The end point of our work is rapid proofs of concept clinical trials in humans that translate into better patient outcomes and reduced morbidity and mortality across the disease spectrum.