All Seminars
| Title: Algebraic Preconditioning of Symmetric Indefinite Systems |
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| Seminar: Numerical Analysis and Scientific Computing |
| Speaker: Miroslav Tuma of Academy of Sciences of the Czech Republic |
| Contact: Michele Benzi, benzi@mathcs.emory.edu |
| Date: 2015-03-10 at 4:00PM |
| Venue: MSC W301 |
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| Abstract: Sparse symmetric indefinite linear systems of equations arise in many practical applications. An iterative method is frequently the method of choice to solve such systems but a system transformation called preconditioning is often required for the solver to be effective. In the talk we will deal with development of incomplete factorization algorithms that can be used to compute high quality preconditioners. We will consider both general indefinite systems and saddle-point problems. Our approach is based on the recently adopted limited memory approach (based on the work of Tismenetsky, 1991) that generalizes recent work on incomplete Cholesky factorization preconditioners. A number of new ideas are proposed with the goal of improving the stability, robustness and efficiency of the resulting preconditioner. For general indefinite systems, these include the monitoring of stability as the factorization proceeds and the use of pivot modifications when potential instability is observed. Numerical experiments involving test problems arising from a range of real-world applications are used to demonstrate the effectiveness of our approach and comparisons are made with a state-of-the-art sparse direct solver. The talk will be based on joint work with Jennifer Scott, Rutherford Appleton Laboratory. |
| Title: Zero-cycles and rational points on rationally connected varieties, after Harpaz and Wittenberg |
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| Seminar: Algebra |
| Speaker: Jean-Louis Colliot-Thelene of Universite Paris-Sud |
| Contact: David Zureick-Brown, dzb@mathcs.emory.edu |
| Date: 2015-03-03 at 4:00PM |
| Venue: W304 |
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| Abstract: Harpaz and Wittenberg have recently proved a general result on the local-global principle for zero-cycles on rationally connected varieties. There is also a conditional variant for rational points. I shall explain some of the ideas in their paper. Reference : http://arxiv.org/abs/1409.0993 |
| Title: Extracting medically interpretable concepts from complex health data |
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| Colloquium: N/A |
| Speaker: Joyce Ho of University of Texas at Austin |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2015-03-03 at 4:00PM |
| Venue: MSC W303 |
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| Abstract: Electronic health records (EHRs) are an increasingly important source of patient information. Efficient analysis of EHRs can help address many healthcare problems by improving clinical decisions, facilitating knowledge discoveries, and enabling the development of cost-effective treatment and management programs. However, EHRs pose many formidable challenges for traditional analytics. The data are collected across diverse populations, consist of heterogeneous and noisy information, and have varying time resolutions. Moreover, healthcare professionals are unaccustomed to interpreting high-dimensional EHRs; they prefer concise medical concepts. Thus, a major question is how to transform EHR into meaningful concepts with modest levels of expert guidance.\\ \\ In this talk, I will discuss two approaches to extract concise, meaningful concepts from certain types of health datasets. First, I will describe a dynamic time series model that tracks a patient's cardiac arrest risk based on physiological measurements (i.e., heart rate, blood pressure, etc.) in an intensive care unit. Our algorithm is inspired by financial econometric and yields interpretability and predictability of a cardiac arrest event. Next, I will present a sparse, nonnegative tensor factorization model to obtain multiple medical concepts with minimal human supervision. Tensor factorization utilizes information in the multiway structure to derive concise latent factors even with limited observations. We applied tensor analysis to real EHRs from the Geisinger Health System to automatically identify relevant medical concepts. Both our models are powerful and data-driven approaches to extract medically interpretable concepts from complex health data. |
| Title: Understanding Information: From Bits to Brains |
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| Colloquium: N/A |
| Speaker: Avani P. Wildani nee Gadani of The Salk Institute |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2015-03-02 at 4:00PM |
| Venue: MSC W303 |
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| Abstract: Information is the currency of the modern era, and there are surprising similarities in data processing and representation between computer systems and neuroscience. In the first half of this talk, I will discuss how to dynamically identify related blocks or files in trace data and use the resulting data groups to make information storage more efficient and robust. From there, I will discuss how the classical systems metrics of reliability, performance, and availability apply to biologically plausible neural networks, including recent work exploring the balance between classification accuracy and robustness. Finally, I will show how computational models from vision can be applied to understand information flow in the visual cortex, and how algebraic topology is a promising method to classify neurons by network function and categorize visual stimuli. |
| Title: Novel Geometric Algorithms for Machine Learning Problems |
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| Colloquium: N/A |
| Speaker: Hu Ding of State University of New York at Buffalo |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2015-02-27 at 3:00PM |
| Venue: MSC W303 |
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| Abstract: Machine learning is a discipline that concerns the construction and study of algorithms for learning from data, and plays a critical role in many other fields, such as computer vision, speech recognition, social network, bioinformatics, etc. As the data scale increases dramatically in the big-data era, a number of new challenges arise, which require new ideas from other areas. In this talk, I will show that such challenges in a number of fundamental machine learning problems can be resolved by exploiting their geometric properties. Particularly, I will present three geometric-algorithm-based results for various machine learning problems: (1) a unified framework for a class of constrained clustering problems in high dimensional space; (2) a combinatorial algorithm for support vector machine (SVM) with outliers; and (3) algorithms for extracting chromosome association patterns from a population of cells. The first two results are for fundamental problems in machine learning, and the last one is for studying the organization and dynamics of the cell nucleus, an important problem in cell biology. Some geometric-algorithm-based future work in machine learning will also be discussed. |
| Title: The Foxby-morphism and derived equivalences |
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| Seminar: Algebra |
| Speaker: Satya Mandal of University of Kansas |
| Contact: David Zureick-Brown, dzb@mathcs.emory.edu |
| Date: 2015-02-26 at 4:00PM |
| Venue: W306 |
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| Abstract: Suppose $X$ is a quasi-projective scheme over a noetherian (Cohen-Macaulay) affine scheme $Spec(A)$, with $dim X=d$. In $K$-theory and related areas (Witt theory, Grothendieck-Witt theory), bounded chain complexes $G_{\bullet}$ of Coherent sheaves or locally free sheaves play an important role. One considers the category $Ch^b(Coh(X))$ (resp. $Ch^b(V(X))$) of bounded chain complexes of coherent sheaves (resp. of locally free sheaves). One also considers, the corresponding derived categories $D^b(Coh(X)$, $D^b(V(X))$, which is obtained by inverting the quasi-isomorphisms in the chain complex categories. \vspace{4pt} Given a chain complex map $L_{\bullet}\to G_{\bullet}$, between two complexes $L_{\bullet}$, $G_{\bullet}$, with extra information on homologies, one complex can be viewed as \emph{an approximation to} the other. Given one such complex $G_{\bullet}$, constructing such a complex $L_{\bullet}$, with desired properties, and constructing a map $L_{\bullet}\to G_{\bullet}$ would be challenging. In the affine case $X=Spec(A)$, such a map was constructed by Hans-Bjorn Foxby (unpublished), several other versions of the same was given by others. In this lecture we implement the construction of Foxby to quasi-affine case and give applications. Intuitively, one can look at this implementation as a "graded" version of Foxby's construction. |
| Title: Scalable Big Graph Data Processing |
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| Colloquium: N/A |
| Speaker: Kisung Lee of Georgia Institute of Technology |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2015-02-26 at 4:00PM |
| Venue: MSC W303 |
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| Abstract: The application of graph data analytics is virtually unlimited because graph data are everywhere, from the friendship graphs of social networks to networks of the human brain. Even though graph data analytics is essential for gaining insight into big graphs, large-scale graph processing is complex because of its graph-specific challenges, including complicated correlations among data entities, highly skewed distribution, various graph operations, and the sheer enormity of graph data. This presentation will focus specifically on two new distributed systems for the scalable processing of big graph data. I will first present a graph system that efficiently supports graph pattern query processing (subgraph matching) by scalable graph partitioning and efficient distributed query processing. I will then describe a distributed system for iterative graph computations that can reduce memory requirements for running iterative graph algorithms while ensuring competitive performance. I will conclude the presentation by introducing a set of challenges for developing a general purpose graph analytics system that can support both efficient graph query processing and fast iterative graph computations under one unified system architecture.\\ \\ Bio: Kisung Lee is a Ph.D. candidate in the School of Computer Science at Georgia Tech. His research interests lie in the intersection of big data systems and distributed computing systems. Kisung has also worked on research problems in spatial data management and social network analytics. He has been a recipient of the best paper awards of IEEE Cloud 2012 and MobiQuitous 2014. Kisung received his B.S. and M.S. degrees in computer science from KAIST and has served as a reviewer for several conferences and journals. |
| Title: High Performance Spatial and Spatio-temporal Data Management |
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| Colloquium: N/A |
| Speaker: Suprio Ray of University of Toronto |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2015-02-24 at 4:15PM |
| Venue: MSC W303 |
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| Abstract: The rapid growth of spatial data volume and technological trends in storage capacity and processing power are fuelling many emerging spatial and spatio-temporal applications from a wide range of domains. Spatial join is widely used by many of the emerging spatial data analysis applications. However, spatial join processing on even a moderate sized dataset is very time consuming. At the same time, there is a rapid expansion in available processing cores, through multicore machines and Cloud computing. The confluence of these trends points to a need for effective parallelization of spatial query processing. Unfortunately, traditional parallel spatial databases are ill-equipped to deal with the performance heterogeneity that is common in the Cloud. In this talk I present two systems that I developed to parallelize spatial join queries in the Cloud and in a large main memory multicore machine.\\ \\ With the proliferation of GPS-enabled mobile devices and sensors, Location Based Services (LBS) have become the most prominent among the spatio-temporal applications. These applications are characterized by high rate of location updates and many concurrent short running range queries. With a large number of devices, the rate of location update can easily surpass 1 million or more updates per second. Traditional relational databases may not be well-suited for this. As the era of "Internet of things" approaches, this issue is expected to get accentuated. In my talk I present a parallel in-memory spatio-temporal indexing technique to support the demands of LBS workloads. Our system achieves significantly better performance than existing approaches to indexing spatio-temporal data. I also present a parallel in-memory spatio-temporal topological join approach. Finally, I outline some of my ideas for future research. |
| Title: Taming the Big Data Elephant with Query Explanations |
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| Colloquium: N/A |
| Speaker: Sudeepa Roy of University of Washington |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2015-02-23 at 4:00PM |
| Venue: MSC W303 |
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| Abstract: In recent years, the availability of big data has resulted in a growing number of users from a variety of backgrounds interested in identifying and interpreting the general trends and anomalies of large datasets. This presents an imminent requirement of sophisticated data analysis tools that can provide qualitative information based on query answers on such datasets. In this talk, I will describe my current research on developing a principled framework for explaining query answers in terms of intervention (explanations are changes in the database that change the observed query answers). I will present our solutions to core challenges in this task such as obtaining concise descriptions of explanations, handling inherent dependencies of database tuples, and achieving real-time efficiency in large explanation spaces. Then, I will briefly talk about my research in the areas of probabilistic databases, provenance, information extraction, and crowd sourcing. The unifying theme of this research is to address defining characteristics of modern datasets: uncertainty, unreliability, lack of structure, and the effects of human participation. I will conclude with my long-term vision of incorporating techniques to handle these challenges in the generic data explanation framework. |
| Title: Looking for Structure in Real-World Networks |
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| Seminar: Computer Science |
| Speaker: Blair Sullivan of Department of Computer Science North Carolina State University |
| Contact: Michele Benzi, benzi@mathcs.emory.edu |
| Date: 2015-02-17 at 4:00PM |
| Venue: W306 |
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| Abstract: Graphs offer a natural representation of relationships within data -- for example, edges can be defined based on any user-defined measure of similarity (e.g. word frequencies, geographic proximity of observation, gene expression levels, or overlap in sample populations) or interaction (e.g. social friendship, communication, chemical bonds/protein bindings, or migration). As such, network analysis is playing an increasingly important role in understanding the data collected in a wide variety of social, scientific, and engineering settings. Unfortunately, efficient graph algorithms with guaranteed performance and solution quality are impossible in general networks (according to computational complexity).\\ \\ One tantalizing approach to increasing scalability without sacrificing accuracy is to employ a suite of powerful (parameterized) algorithms developed by the theoretical computer science community which exploit specific forms of sparse graph structure to drastically reduce running time. The applicability of these algorithms, however, is unclear, since the (extensive) research effort in network science to characterize the structure of real-world graphs has been primarily focused on either coarse, global properties (e.g., diameter) or very localized measurements (e.g., clustering coefficient) -- metrics which are insufficient for ensuring efficient algorithms.\\ \\ We discuss recent work on bridging the gap between network analysis and structural graph algorithms, answering questions like: Do real-world networks exhibit structural properties that enable efficient algorithms? Is it observable empirically? Can sparse structure be proven for popular random graph models? How does such a framework help? Are the efficient algorithms associated with this structure relevant for common tasks such as evaluating communities, clustering and motifs? Can we reduce the (often super-exponential) dependence of these approaches on their structural parameters? Joint work with E. Demaine, M. Farrell, T. Goodrich, N. Lemons, F. Reidl, P. Rossmanith, F. Sanchez Villaamil and S. Sikdar. |