SC17: AI and Machine Learning are Central to Computational Attack on Cancer

Enlisting computational technologies in the war on cancer isn't new but it has taken on an increasingly decisive role. At SC17, Eric Stahlberg, director of the HPC Initiative at Frederick National Laboratory for Cancer Research in the Data Science and Information Technology Program, and two colleagues will lead the third Computational Approaches for Cancer workshop being held the Friday, Nov. 17, at SC17. It is hard to overstate the importance of computation in today's pursuit of precision medicine. Given the diversity and size of datasets it's also not surprising that the "new kids" on the HPC cancer fighting block – AI and deep learning/machine learning – are also becoming the big kids on the block promising to significantly accelerate efforts understand and integrate biomedical data to develop and inform new treatments. In this Q&A, Stahlberg discusses the goals of the workshop, the growing importance of AI/deep learning in biomedical research, how programs such as the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) are progressing, the need for algorithm assurance and portability, as well as ongoing needs where HPC technology has perhaps fallen short.