A Laboratory for Scientific Visualization and Experimental Machine Learning

这个基础设施奖是为了表彰可视化和机器学习实验室的发展。该实验室由功能强大的图形工作站、并行处理器、大型文件服务器、光纤高速数据网络以及维护实验室的支持人员组成。该设备支持的研究包括：统计数据分析、模式识别和机器学习；样本量数据的可视化；模拟的交互引导；高速切换；并行系统；以及电子图书馆的应用。科学信息利用的一个主要瓶颈是目前收集的大量数据。科学可视化这个术语涵盖了将多维数字数据转换为可理解的图形图像的一系列新技术。然后，科学家和工程师可以操纵和探索这些图像，他们坐在自己的工作站上，创建模型并从中得出推论。机器学习包括模式识别、信号处理、统计决策和控制理论以及人工智能等领域的各种技术，这些技术允许机器本身参与创建模型和从数据中得出推理的过程。预计这一奖项将在机器学习和科学可视化之间产生新的协同作用，并将与计算机科学系以外的科学家进行实质性合作。

This infrastructure award is for the development of a visualization and machine learning laboratory. The laboratory consists of powerful graphics workstations, a parallel processor, a large file server, a fiber optics high speed data network, and support personnel to maintain the laboratory. The research supported by this equipment includes: statistical data analysis, pattern recognition, and machine learning; visualization of sample volume data; interactive steering of simulations; high speed switching;concurrent systems; and applications of electronic libraries. A major bottleneck to the utilization of scientific information is the massive amount of data now collected. Scientific visualization is the term that covers an emerging collection of new techniques for transforming multidimensional numeric data into understandable graphic images. These images can then be manipulated and explored by scientists and engineers who, sitting at their workstations, create models and draw inferences from them. Machine learning includes a wide variety of techniques from the fields of pattern recognition, signal processing, statistical decision and control theory, and artificial intelligence that allow the machine itself to participate in the process of creating models and drawing inferences from data. It is anticipated that a new synergy between machine learning and scientific visualization will arise from this award and that substantial collaborations with scientists outside of the computer science department will occur.