CREST HBCU-RISE: Advancing Theoretical Artificial Intelligence Infrastructure for Modern Data Science Challenges

CREST HBCU-RISE：推进理论人工智能基础设施应对现代数据科学挑战

• 批准号: 2409093
• 负责人: Ali Sekmen
• 金额: $ 120万
• 依托单位: Tennessee State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-09-15 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2409093&HistoricalAwards=false
• 关键词: CREST; HBCU; RISE; Advancing; Theoretical

With support from the Centers of Research Excellence in Science and Technology HBCU Research Infrastructure for Science and Engineering (CREST HBCU-RISE), this project aims to improve the theoretical Artificial Intelligence (AI) infrastructure at Tennessee State University (TSU) for addressing important data science challenges. While AI applications have proliferated, there remains a gap in the fundamental mathematical theories required to construct reliable and secure AI systems suitable for safety-critical applications. This project seeks to address this. The expected outcomes include the advancement of scientific knowledge in AI theory and its practical applications in cybersecurity, bioinformatics, and agriculture. Additionally, this project aims to enhance interdisciplinary collaboration, promote broader participation in STEM fields, and strengthen research competitiveness at TSU.The overarching goal is to advance research capabilities in emerging AI areas and develop a comprehensive approach to educate and train Ph.D. students in collaboration with three TSU colleges: Engineering, Life and Physical Sciences, and Agriculture. The research will develop mathematical theory and practical algorithms for accurate and robust machine learning that can be applied for advancing research in privacy-preserving AI, protein structure modeling with enhanced cryo-electron microscopy imaging, and optimal feature selection for precision agriculture. High-dimensional manifold geometries for neural networks training, network linearization for homomorphic encryption in private AI, systematic integration of subspace segmentation and machine learning for cryo-electron microscopy, and quality assessment of multi-scale sensing data for crop parameters and yield estimation will be investigated. This project implements a coherent curriculum across three colleges for AI education, which includes developing educational materials, organizing professional development activities for students, providing Ph.D. student mentoring, and procuring research equipment to support this research. The CREST HBCU-RISE program supports the expansion of institutional research capacity as well as the successful training of doctoral students in STEM at HBCUs.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.

在科学技术卓越研究中心 HBCU 科学与工程研究基础设施 (CREST HBCU-RISE) 的支持下，该项目旨在改善田纳西州立大学 (TSU) 的理论人工智能 (AI) 基础设施，以处理重要数据科学挑战。尽管人工智能应用激增，但构建适合安全关键应用的可靠且安全的人工智能系统所需的基本数学理论仍然存在差距。该项目旨在解决这个问题。预期成果包括人工智能理论科学知识的进步及其在网络安全、生物信息学和农业中的实际应用。此外，该项目旨在加强跨学科合作，促进 STEM 领域更广泛的参与，并增强 TSU 的研究竞争力。总体目标是提高新兴人工智能领域的研究能力，并开发一种全面的方法来教育和培训博士。学生与托州立大学三个学院合作：工程学院、生命与物理科学学院以及农业学院。该研究将开发数学理论和实用算法，以实现准确而强大的机器学习，可用于推进隐私保护人工智能、增强冷冻电子显微镜成像的蛋白质结构建模以及精准农业的最佳特征选择等方面的研究。用于神经网络训练的高维流形几何、用于私人人工智能中同态加密的网络线性化、用于冷冻电子显微镜的子空间分割和机器学习的系统集成，以及用于作物参数和产量估计的多尺度传感数据的质量评估调查了。该项目在三所学院实施了一致的人工智能教育课程，包括开发教材、为学生组织专业发展活动、提供博士学位。学生指导，并采购研究设备来支持这项研究。 CREST HBCU-RISE 计划支持机构研究能力的扩展以及 HBCU 中 STEM 博士生的成功培训。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响进行评估，被认为值得支持审查标准。