HDR TRIPODS: UT Austin Institute on the Foundations of Data Science

这个项目在德克萨斯大学奥斯汀分校的数据科学基础上建立了一个新的研究所。该研究所将由德克萨斯大学奥斯汀分校电气工程、计算机科学、数学和统计部门的8名pi以及该研究所新项目的博士后和研究生合作建立。它将通过研究分析和设计的基本方法，形成机器学习和数据科学理论研究的中心枢纽。这对于设计新颖复杂的机器学习和人工智能理论和算法是必要的，这些理论和算法可以处理接收数据的加速规模和计算机更快的计算速度。算法和系统将从数据和环境中解释和预测行为，目标是以有原则的方式实现更好的设计方法。这项研究还将为自动驾驶汽车和个性化医疗等领域的应用开辟道路。研究和教育将整合在一起，以创建新的跨部门博士后和研究生研究项目，在德克萨斯大学奥斯汀分校建立统一的数据科学学位和组合项目，举办专门的系列研讨会和研讨会，并与行业和领域专家合作。它将通过资助的倡议，大大扩大警察正在进行的扩大代表性不足群体参与这一重要领域的努力。研究重点是机器学习和优化的基础数学理论，包括神经网络，鲁棒性和图。该研究围绕三个主题进行组织：(a)开发深度学习的算法理论，使用新的可证明的训练方法，进行超参数优化和开发置信度度量，(b)使机器学习对数据中的对抗性和偶然错误都具有鲁棒性，以及(c)设计使用图算法进行统计推断的新方法，包括图统计的快速估计，以及它们在生物和视觉应用中的应用。该项目是美国国家科学基金会“利用数据革命（HDR）大创意”活动的一部分。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project establishes a new institute on the Foundations of Data Science at the University of Texas at Austin. The Institute will be a collaboration between eight PIs in the electrical engineering, computer science, mathematics and statistics departments at UT Austin, as well as postdocs and graduate students from the new programs this Institute establishes. It will form a central hub for theoretical research into machine learning and data science by looking at foundational approaches to analysis and design. This is necessary to devise novel complex and sophisticated machine-learning and artificial-intelligence theory and algorithms that can handle the accelerating scale of received data and the faster computational speeds of computers. The algorithms and systems will interpret and predict behavior from data and the environment with the goal towards better design methods performed in a principled way. The research will also open avenues for applications in fields such as autonomous vehicles and personalized medicine. The research and education will be integrated to create new inter-departmental postdoctoral and graduate research programs, establish a unified degree and portfolio program in data science at UT Austin, run dedicated seminar series and hold workshops, and partner with industry as well as domain experts in the sciences. It will significantly expand, via funded initiatives, the PIs' ongoing efforts to expand participation of under-represented groups in this important field.Research focuses on fundamental mathematical theory of machine learning and optimization, including neural networks, robustness, and graphs. The research is organized around three themes: (a) developing an algorithmic theory for deep learning, with new and provable methods for training, doing hyper parameter optimization and developing confidence measures, (b) making machine learning robust to both adversarial and incidental errors in data, and (c) devising new methods for statistical inference using graph algorithms, including fast estimation of graph statistics, and their use in biological and vision applications.This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.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.