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Conference: Advances in Statistical and Computational Methods for Analysis of Biomedical, Genetic, and Omics Data

Conference: Advances in Statistical and Computational Methods for Analysis of Biomedical, Genetic, and Omics Data
会议:生物医学、遗传和组学数据分析的统计和计算方法的进展
批准号:
2232547
负责人:
Swati Biswas
金额:
$3.17万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2023
资助国家:
美国
项目状态:
已结题
起止时间:
2023-01-01 至 2024-12-31

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英文摘要
A three-day international conference titled "Advances in Statistical and Computational Methods for Analysis of Biomedical, Genetic, and Omics Data" will be held at the University of Texas at Dallas from March 17-19, 2023. The conference will bring together diverse groups of students, junior researchers, and leading researchers from academia, industry, and government in a collaborative setting to discuss cutting-edge statistical and computational methods for health-related research. The scientific program will include plenary and invited talks, and a poster session. In addition to its core mission of contributing towards the advancement of science, this conference will provide opportunities for students and young researchers to engage in robust interaction and networking with senior researchers. Thus, the conference will also contribute toward building a diverse human capital skilled in statistics and data science for health-related research.The theme of the conference is timely, especially in today's era of big data. Massive amounts of a variety of biological and medical data are now available and continue to be collected. Information gleaned from them can power breakthroughs leading to advances in not only personalized medicine but also innovative drug designs and evidence-based medical decision support systems that can potentially revolutionize healthcare. However, a key challenge is the development of new statistical and computational methods that will allow researchers to dive deep into such data and derive actionable information from them. The conference will contribute toward fostering the development of newer and better tools for filtering useful information from large, complex, and noisy datasets, helping pave the way for future advances leading to improved healthcare. More details about the conference can be found at https://sites.google.com/view/abgod2023/home.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.
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