Seminars archive

It is a classical problem in geometry to find the densest sphere packing in n-dimensional Euclidean space. Analogous problems of packing among lattices, or on compact spaces such as the sphere or Hamming space, are widely studied in number theory, discrete geometry, coding theory and combinatorics. I will talk about some recent work which puts these problems in the framework of potential energy minimization. This leads to experimental and theoretical techniques to approach these optimization problems (and their inverse problems), and leads to some surprising new results in high dimensions.

I'll spend a fair amount of time discussing the mathematical models behind the CT (computed tomography) and PET (positron emission tomography) imaging modalities, with the goal of bringing the audience along for as much of 50 minutes as possible. At some point, however, I'll dive into my own research focusing on the PET imaging problem, which includes iterative methods, as well as the choice of the regularization function and parameter.\\ \\ Anyone curious about how these standard medical imaging techniques work is encouraged to attend.

This research takes place in the larger context of the study of one-on-one tutoring, a form of instruction that has been shown to be very effective. We conducted a study of human tutoring in the domain of Computer Science data structures, to understand which features and strategies of human tutoring are important for learning. We developed an Intelligent Tutoring System, iList, that helps students learn linked lists. One of the main advancements in iList is the presence of a Procedural Knowledge Model automatically extracted from student data. This model allows iList to provide effective reactive and proactive procedural feedback while a student is solving a problem. We tested five different versions of iList, differing in the level of feedback they can provide, in multiple classrooms, with a total of more than 200 students. The evaluation study showed that iList is effective in helping students learn; students liked working with the system; and the feedback generated by the most sophisticated versions of the system is helpful in keeping the students on the right path. \\ Davide Fossati (http://www.fossati.us) received his Ph.D. in Computer Science at the University of Illinois at Chicago in summer 2009. He also holds an M.Sc. degree in Computer Engineering from the Politecnico di Milano, Italy (2004), and an M.Sc. in Computer Science from the University of Illinois at Chicago (2003). His research focuses on applications of Artificial Intelligence in education, such as Intelligent Tutoring Systems, and computational models and tools to support formative assessment in Computer Science education.

A pseudo-reductive group is a smooth connected affine algebraic group over a field k which does not contain any nontrivial smooth connected normal unipotent subgroups defined over k. Such groups arise naturally as the quotient of any smooth connected affine algebraic k-group by the maximal smooth connected normal unipotent subgroup defined over k. For study of general affine algebraic groups it is important to know the structure and classification of pseudo-reductive groups. In a joint work with Brian Conrad and Ofer Gabber we have determined the structure and classification of these groups. In my talk I will explain the classification, and also mention group theoretic and arithmetic applications.

We outline the general development of a theory of symmetric homology of algebras, an analog of cyclic homology where the cyclic groups are replaced by symmetric groups. This theory is developed using the framework of crossed simplicial groups and the homological algebra of module-valued functors. The symmetric homology of group algebras is related to stable homotopy theory. Two spectral sequences for computing symmetric homology are constructed. The $E_1$ term of one of these relates to a new class of geometric complexes for Coxeter groups. Many open questions remain.

Advances of sensor and RFID technology provide significant new power for humans to sense, understand and manage the world. RFID provides fast data collection with precise identification of objects with unique IDs without line of sight, thus it can be used for identifying, locating, tracking and monitoring physical objects. Despite these benefits, RFID poses many challenges for data processing and management. In this talk, I will discuss our work on temporal and location based RFID data management, and rule-based complex RFID event processing. \\ Fusheng Wang is a Senior Research Scientist at Emory University's Center for Comprehensive Informatics. Dr. Wang received his Ph.D. in Computer Science from University of California, Los Angeles in 2004. Before joining Emory, he was a research scientist and project lead at Siemens Corporate Research. His research areas include heterogeneous scientific data management and integration, high performance biomedical image management systems, temporal data management, RFID data management, XML databases, and collaborative information systems.

Parallel computing has been used for scientific computing applications since the 1960s, when the first supercomputers were developed. However, only recently have these programming paradigms become useful for software running on desktop and notebook computers. In this dissertation we demonstrate the advantage of exploiting modern computer architectures in scientific computing with multithreaded programming in Java for applications in image processing. A significant contribution of this work is an open source, multithreaded high performance scientific computing Java library called Parallel Colt. In addition, on top of Parallel Colt, we have implemented six ImageJ plugins for deconvolution, super resolution, fast Fourier transforms and image cropping. Hence, we are able to provide software to solve important problems in real image processing applications, and which can effectively make use of multi-core CPUs available on affordable desktop and notebook computers.

With the ubiquity of information networks and their broad applications, there have been numerous studies on the construction, and mining of information networks in multiple disciplines, including social network analysis, World-Wide Web, database systems, data mining, machine learning, and networked communication and information systems. However, large graphs may often be disk resident, and such graphs cannot be efficiently processed. In this talk, we examine the issues of on-line analytic processing, summarization and indexing of large graphs. Specifically, the problem of connectivity in the context of massive graphs is considered. In many large communication networks, social networks and other graphs, it is desirable to determine the minimum-cut between any pair of nodes. We will discuss how a connectivity index can be developed for a massive-disk resident graph. A sampling based approach is deployed to create compressed representations of the underlying graph. Trade-off between processing efficiency and accuracy will be shown. Bio: Philip S. Yu is a Professor in the Department of Computer Science at the University of Illinois at Chicago and also holds the Wexler Chair in Information Technology. Before joining UIC, he spent most of his career at IBM Thomas J. Watson Research Center and was manager of the Software Tools and Techniques group. Dr. Yu is a Fellow of the ACM and the IEEE. He served as the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001-2004). He is an associate editor of ACM Transactions on Knowledge Discovery from Data and also ACM Transactions of the Internet Technology. He serves on the steering committee of IEEE Int. Conference on Data Mining. He was a member of the IEEE Data Engineering steering committee. Dr. Yu received a Research Contributions Award from IEEE Intl. Conference on Data Mining in 2003. His research interests include data mining, privacy, data stream, and database systems. He has published more than 560 papers in refereed journals and conferences. He holds or has applied for more than 300 US patents. Dr. Yu was an IBM Master Inventor when at IBM.

A Rogers-Ramanujan type identity is a series=product identity which relates certain partitions into parts satisfying difference to partitions into parts satisfying congruence conditions. Rogers-Ramanujan type identities arise in a variety of settings ranging from Lie algebras to statistical mechanics. A supreme example of such a partition identity is the deep 1967 theorem of Gollnitz. We shall discuss a new approach to Gollnitz's theorem in which this partition result and its generalizations will emerge out of a remarkable $q$-hypergeometric identity in three free parameters. This leads to crucial connections with several fundamental results on partitions and $q$-series, a new combinatorial understanding of Jacobi's triple product identity for theta functions, and to some partition congruences modulo powers of 2. The talk will be accessible to non-experts.