Accelerating Cancer Research with Deep Learning

In the US, the Department of Energy (DOE) entered into a partnership with the National Cancer Institute (NCI) of the National Institutes of Health (NIH) to accelerate cancer research. This "Cancer Moonshot" aims to tackle three main objectives: to better understand the mechanisms of cancer, to encourage the use of large amounts of diverse medical data for predictive models, and to enable precision medicine by providing guidance for treatment to individual patients. Leveraging the compute expertise of DOE in high performance computing (HPC) and new methods for deep learning in artificial intelligence, this HPC+AI approach aims to create a single scalable deep neural network code called CANDLE (CANcer Distributed Learning Environment) that will be used to address all three challenges. This talk aims to give an overview of the project and highlight how GPU accelerated systems in the DOE ecosystem, Summit and Sierra, have contributed to the project

Date        :    10 September 2018
Time        :    5:30pm to 7:30pm
Venue    :    BASH, Level 3, Block 79 Ayer Rajah Crescent, Singapore 139955

Programme: 
5:30pm – 6:00pm: Registration
6:00pm – 7:00pm: Accelerating Cancer Research with Deep Learning
7:00pm – 7:30pm: Q&A and Networking Session

Speaker: Fernanda Foertter, Healthcare Developer’s Relation (DevRel), NVIDIA

Speaker’s Profile:

Fernanda Foertter has recently joined NVIDIA but has been a long time GPU/CUDA/OpenACC advocate in the HPC community. Prior to NVIDIA she was a member of the Advanced Data Workflow Group assigned to the Biomedical Science and Engineering Center at the National Center for Computational Sciences (NCCS) located at Oak Ridge National Laboratory (ORNL), working on scalable deep learning algorithms on next generation high performance computing systems with application to cancer research. Foertter was also in charge of HPC training and is the original creator of the OLCF GPU Hackathons. Her interests include disrupting healthcare research and advocating for developer productivity for HPC and AI.