III: Small: Novel Geometric Algorithms for Learning from Big Biomedical Data

大生物医学数据已成为生物医学发现的关键资源。然而，由于它们的分布式性质以及敏感和噪声数据的存在，它们没有被充分利用。例如，许多生物医学数据生成并存储在医院、药房和生物医学实验室等不同地点，在收集、整合和利用这些数据时可能会出现许多挑战。因此，迫切需要更合适、更有效的技术对它们进行整合和分析。该项目旨在为大生物医学数据中常见的几个基本数据分析问题设计一套新颖的算法工具。该项目可能会提高收集和整合大生物医学数据的能力，有效利用这些数据，并处理不可靠和敏感的生物医学数据。 具体来说，该项目将集中在四个数据分析问题：分布式真相发现，分布式分类，分布式聚类和差异化私人学习。这些问题中的每一个都已经被认为是数据分析和信息集成中的关键工具。该项目解决了许多具有挑战性的问题（如通信成本，数据可靠性，鲁棒性，计算成本和隐私保护），旨在为这些问题中的每一个实现高效和质量保证的解决方案。这四个研究目标将根据其有效性，效率和特定生物医学数据集的实用性进行评估。此外，这项研究将提供教育和研究的机会，研究生和本科生，包括学生从代表性不足的groups.This奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

Big biomedical data has become a key resource for discovery in biomedicine. However, due to their distributed nature and the existence of sensitive and noisy data, they are not utilized to their full potential. For example, lots of biomedical data are generated and stored at different sites such as hospitals, pharmacies, and biomedical labs, numerous challenges could arise when gathering, integrating, and utilizing them. Thus, more suitable and effective techniques are urgently needed to integrate and analyze them. This project aims to design a set of novel algorithmic tools for several fundamental data analytics problems commonly encountered in big biomedical data. This project could potentially increase the ability of gathering and integrating big biomedical data, effectively exploiting such data, and handling unreliable and sensitive biomedical data. Specifically, the project will focus on four data analysis problems: distributed truth discovery, distributed classification, distributed clustering, and differentially private learning. Each of these problems has already been recognized as critical tools in data analysis and information integration. The project addresses a number of challenging issues (such as communication cost, data reliability, robustness, computational cost, and privacy-preserving), and aim to achieve highly efficient and quality guaranteed solutions for each of these problems. These four research aims will be evaluated based on their effectiveness, efficiency, and the practicality on specific biomedical datasets. Furthermore, this research will provide educational and research opportunities to both graduate and undergraduate students including students from underrepresented groups.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.