Student Travel Support: ACM International Workshop on Big Data in Life Sciences, Seattle, WA, October 2, 2016

ACM生命科学大数据国际研讨会（BigLS）是一个系列研讨会，重点关注广义生命科学中的计算和数据挑战。该研讨会于2013年启动，与ACM生物信息学，计算生物学和健康信息学会议一起举办-计算机械协会生物信息学，计算生物学和生物医学信息学特别兴趣小组（SIGBio）的旗舰会议。该研讨会汇集了领先的研究人员和从业人员，他们致力于研究与生物学和医学相关的各种大数据问题，并让他们参与讨论当前的大数据问题，计算工具和分析的状态，该项目将支持来自美国学术机构的10名学生和博士后研究人员参加BigLS 2016，将于2016年10月2日在华盛顿州西雅图举行。获奖者将通过广泛宣传的竞争过程中选择，包括提交旅行补助金申请，并由计划委员会审查。大数据问题和挑战在现代计算生物学、生物信息学和生物医学信息学中已经成为现实。研讨会将围绕生命科学大数据研究的五个关键研究主题进行同行评审论文和特邀演讲：1）分子生物学大数据分析的可扩展算法和技术; 2）大数据生物学的统计和综合方法; 3）大数据生物学的新兴机器学习和人工智能技术; 4）大数据生物学的高性能计算方法和软件; 5）管理生物医学信息学大数据的软件和硬件基础。

The ACM International Workshop on Big Data in Life Sciences (BigLS) is a workshop series focusing on computational and data challenges in broadly defined life sciences. The workshop was initiated in 2013, and it is hosted together with the ACM Conference on Bioinformatics, Computational Biology and Health Informatics - the flagship conference of the Association for Computing Machinery Special Interest Group on Bioinformatics, Computational Biology, and Biomedical Informatics (SIGBio). The workshop brings together leading researchers and practitioners working on a diverse range of big data problems relating to biology and medicine, and engages them in a discussion about current big data questions, the state of computational tools and analytics, the challenges and the future trends within life sciences.This project will support up to ten students and postdoctoral researchers from US academic institutions to attend BigLS 2016 to be held on October 2, 2016 in Seattle, WA. Awardees will be selected via a widely advertised competitive process involving the submission of a travel grant application, and review by the program committee. Preference will be given to women and underrepresented minority groups, first-generation college students, and undergraduate researchers.Big data problems and challenges have become a reality in modern day computational biology, bioinformatics and biomedical informatics. The workshop will feature peer-reviewed papers and invited talks on five key research themes that underline big data research in life sciences: 1) scalable algorithms and techniques for big data analytics in molecular biology; 2) statistical and integrative approaches to big data biology; 3) emerging machine learning and AI techniques for big data biology; 4) high performance computing methods and software for big data biology; 5) software and hardware foundations for managing big data in biomedical informatics.