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.
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.
A.W. Roscoe University of Oxford
Dmitriy Morozov Lawrence Berkeley National Laboratory
Abstract: In the last decade, persistent homology emerged as a particularly active topic within the young field of computational topology. Homology is a topological invariant that counts the number of cycles in the space: components, loops, voids, and their higher-dimensional analogs. Persistence keeps track of the evolution of such cycles and quantifies their longevity. By encoding physical phenomena as real-valued functions, one can use persistence to identify their significant features.
This talk is an introduction to the subject, discussing the settings in which persistence is effective as well as the methods it employs. As examples, we consider problems of scalar field simplification and non-linear dimensionality reduction. The talk will sketch the budding field of topological data analysis and its future directions.
Lincoln Wallen Chief Technology Officer, Dreamworks Animation SKG
This colloquium, offered as the 2014 James Mead Lecture (see more info re: Tulane alumnus James Mead ) will be held on Wednesday, November 19th, in Room 105 of the Boggs Center for Energy and Biotechnology. The hour-long lecture will begin at 4:30 p.m. and will be followed by a 30-minute Q&A session. Please note the special weekday, time, and venue for this event.
Abstract: Media has long been a primary driver of personal computing technology including the chipsets for CPU, graphics and radio interfaces. Full computer generated (CG) movies represent these trends at enterprise scale and drive innovation in storage, servers, and cloud software. Over the last 20 years, DreamWorks Animation has been a leader in producing award-winning CG animated movies such as Shrek, Madagascar and How to Train Your Dragon 2. Lincoln Wallen,CTO, will give insight into the studio's next generation production platform named Apollo, which unites the worlds of micro-computing (scalable multicore) and macro-computing (cloud), pointing the way toward a new approach to product design.
Nancy M. Amato TEXAS a & m UNIVERSITY
Abstract: Motion planning arises in many application domains such as computer animation (digital actors), mixed reality systems and intelligent CAD (virtual prototyping and training), and even computational biology and chemistry (protein folding and drug design). Surprisingly, one type of sampling-based planner, the probabilistic roadmap method (PRM), has proven effective on problems from all these domains.
In this talk, we describe the PRM framework and give an overview of some PRM variants developed in our group. We describe in more detail our work related to virtual prototyping, crowd simulation, and protein folding. For virtual prototyping, we show that in some cases a hybrid system incorporating both an automatic planner and haptic user input leads to superior results. For crowd simulation, we describe PRM-based techniques for pursuit evasion, evacuation planning and architectural design. Finally, we describe our application of PRMs to simulate molecular motions, such as protein and RNA folding. More information regarding our work, including movies, can be found at http://parasol.tamu.edu/~amato/.
About the Speaker:
School of Science and Engineering, 201 Lindy Boggs Center, New Orleans, LA 70118 504-865-5764 email@example.com