REU Site: CAAR: Combinatorics and Algorithms Applied to Real Problems

该项目将招募本科生来到位于学院公园的马里兰州大学，在那里他们将从事研究项目。这些项目是关于理论与实践相结合。例如，有几个是关于机器学习的，它使用概率和统计来训练机器寻找数据中的相关性，识别图像或玩游戏。其他项目是密码学，数学用于构建安全系统。将特别努力从非研究型学校和代表性不足的群体中招收学生。这个项目将给许多学生一个做研究的机会，这是他们本来没有的。这个项目将让学生从两个方面了解研究生院是什么样的：（1）他们的研究项目是博士论文的缩小版，（2）将与学生和当前的研究生进行互动。学生将获得各种项目。我们在下面列出了一些示例项目：（1）密码学：一些下一代密码系统是基于解决格上某些问题的难度（而不是因式分解）。这个项目将探索解决这些格问题的已知算法的实现，作为测试新系统安全性的一种方式。(2)安全性：侧信道攻击是一种通过观察系统使用了多少时间或功率（或其他可见信号）来攻击系统的方法。在过去，这种攻击被用来寻找密钥。在这个项目中，我们开发和实施了找出用户数据信息的攻击，以及开发和实施防止这些攻击的方法。(3)分配：一家公司应该如何分配其有限的面试资源，从大量的求职者中选择最佳的新员工？解决这个问题似乎需要有趣的算法和机器学习技术。在这个项目中，学生将实现解决问题的程序，并在真实的数据上运行它们。(4)机器学习/图像识别：机器学习中的一个常见问题是训练系统识别图像，比如狗。如何测试这些系统？机器学习中的另一个问题是为图像识别器生成硬案例。在这个项目中，我们将编写程序，产生虚假的图像，欺骗图像识别器，我们将使用这些假货，以改善原来的图像识别器。这个奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的智力价值和更广泛的影响审查标准。

The project will recruit undergraduates to come to The University of Maryland at College Park where they will work on research projects. These projects are about combining theory and practice. For example, several are on machine learning which uses probability and statistics to train machines to find correlations in data, identify images, or play games. Other projects are in cryptography, where Mathematics is used to build secure systems. There will be a special effort to recruit students in the program from non-research schools and underrepresented groups. This program will give many of the students a chance to do research, which they otherwise would not have.This program will give students an idea of what graduate school is like in two ways: (1) their research projects are scaled down versions of PhD theses, and (2) there will be interaction with the students and current grad students.The students will be offered a variety of projects. We list some sample projects below:(1) Cryptography: Some next generation cryptosystems are based on the hardness of solving certain problems on lattices (rather than factoring). This project will explore implementations of known algorithms for solving these lattice problems as a way to test the security of the new systems.(2) Security: Side channel attacks are a way to attack a system by observing how much time or power (or other visible signs) the system uses. In the past such attacks have been used to find keys. In this project we develop and implement attacks that find out information about users' data, as well as develop and implement ways to prevent these attacks.(3) Allocation: How should a firm allocate its limited interviewing resources to select the optimal cohort of new employees from a large set of job applicants? Solving this problem seems to require interesting algorithms and machine learning techniques. In this project the students will implement programs for the problem and run them on real data.(4) Machine Learning/Image Recognition: A common problem in Machine Learning is to train a system to recognize an image, say of a dog. How to test such systems? Another problem in machine learning is to generate hard cases for an image-recognizer. In this project we will write programs that generate fake images that fool an image-recognizer, and we will use these fakes to improve the original image-recognizer.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.