Scientific Computing Meets Machine Learning and Life Sciences

点击翻译按钮获取中文摘要

The workshop "Scientific Computing meets Machine Learning and Life Sciences" will be held on the campus of Texas Tech University in Lubbock, TX, from October 7 through October 9, 2019. This workshop will bring together leading experts and early career researchers from mathematics, statistics, computer science, machine learning, data sciences, and life sciences to report on cutting-edge and state-of-the-art computational algorithms in scientific computing and to identify computational and statistical challenges and open problems in machine learning and the life sciences. In addition, the workshop will provide a forum for an international and diverse group of researchers to foster communication, to facilitate new collaborative interactions, and to initiate joint research projects that will address the open and emerging issues and the computational and statistical challenges posed in machine learning and the life sciences. The three-day workshop will consist of presentations, posters, and group discussions that will stimulate an intensive exchange of ideas and foster fruitful interactions. This award supports the attendance of both researchers and graduate students, with priority given to graduate students, postdoctoral scholars, early career investigators, members of under-represented groups, and researchers who do not have other federal support. Scientific computing is an increasingly important tool in many areas of science and engineering, such as biomedical imaging, genomics, proteomics, phylogeny, computer vision, and precision medicine, allowing biological data and systems to be explored that are not amenable to theoretical or experimental investigations. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. The advent of the big data era pushed machine learning to the forefront and has spurred broad interests in machine learning in recent years. The field of life sciences has advanced through a synergistic interplay between deep understanding of biology and mathematical techniques, especially from computational mathematics, probability, and statistics. Still, biologists are overwhelmed by the amount of data being generated and the new methods required for data-management. Quantitative theories are needed to help interpret and to contextualize observations. A variety of new challenges in scientific computing for machine learning have emerged in recent years that are related to the life sciences, such as developing predictive models for disorder detection, drug repurposing, toxicity prediction, electronic health record analysis, language translation, etc. These issues and many other open problems will be discussed among the diverse group of scientists participating in the workshop. More information is available at http://www.math.ttu.edu/scmlls2019/.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.