Check back soon for more information on the computer science seminar series. Unless otherwise noted, the seminars now meet on Mondays at 3pm in Stanley Thomas 302. If you would like to receive notices about upcoming seminars, you can subscribe to the announcement listserv.
Helmut Alt Freie Universität Berlin
Abstract: We consider the problem of placing geometric objects in two dimensions so that they occupy an area as small as possible. "Packing" means that the objects may overlap, "stacking" that they may not. Packing has important applications, e.g., in the clothing and steel industries. Reasonably efficient algorithms can be found if the number of objects to be placed is constant. If not, even the most simple variants of the problem are NP-hard, so it is important to find efficient approximation algorithms. In the talk, we will give an introduction into the problem, present its numerous variants, demonstrate its computational complexity, and present some approximation algorithms.
Chao Chen Rutgers University
Abstract: Biomedical imaging informatics has been rapidly developed in the past few decades. While many models used prior information of the shape of the target object/data, recently, many new applications arose in which the shape-prior is not available. These 'shapeless' data include the neuron structures from Electron Microscopy (EM) images and the fine trabeculae muscle structures within the human heart. When working with the macroscopic level, we face challenges in capturing the shape of data of extremely high dimension.
In this talk, I will discuss how to explore new 'shapes' of these data. To start with, we show that the topology of the data, e.g., handles and connected components, could be very useful priors. Furthermore, we show that the topographical landscape, namely, the mountains and valleys, of the data can help us in data acquisition and analysis.
The new shapes of data that we present also provide new opportunities for interactive exploration. In EM images, human interaction is inevitable due to the extremely high expectation of the accuracy of the extracted structure. In high dimensional data analysis, showing the shape of the data enables domain expert to explore the data and invent new hypothesis to verify.
Results in this talk have been published in NIPS, IPMI, MICCAI and CVPR.
Jeannette Wing Microsoft Research
Abstract: My vision for the 21st Century: Computational thinking will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science, but benefit people in all fields.
A.W. Roscoe University of Oxford
Abstract: In this talk I will report on a successful extension to the CSP refinement checker FDR3 that permits it to run on clusters of machines. FDR3 is able to scale linearly up to clusters of 64 16-core machines (i.e. 1024 cores), achieving an overall speed-up of over 1000 relative to the sequential case. This speed-up was observed both on dedicated supercomputer facilities, but more notably, also on a commodity cloud computing provider. This enhancement has enabled the verification of a system of 1012 states, which we believe to be the largest refinement check ever attempted by several orders of magnitude.
About the Speaker:
School of Science and Engineering, 201 Lindy Boggs Center, New Orleans, LA 70118 504-865-5764 email@example.com