Seminars archive

With the trend of cloud computing, data and computing are moved away from desktop and are instead provided as a service from the cloud. Data-as-a-service enables access to a wealth of data across distributed and heterogeneous data sources in the cloud. It remains a challenge, however, to ensure the privacy, interoperability, and scalability for such services.\\ \\ We designed and developed DObjects, a general-purpose P2P-based query and data operations infrastructure that can be deployed in the cloud and provides access to heterogeneous data sources. The system builds on top of a distributed mediator-wrapper architecture where individual system nodes serve as mediators and/or wrappers and interact with each other in a P2P fashion what guarantees good scalability. As an analogy, the system nodes can be considered as droplets, small elements that provide similar functionality in the cloud. Just as thousands or millions of droplets form a single drop in nature, in cloud computing, groups of droplets that provide similar functionality can form a micro-cloud. Micro-clouds are an integral part of the whole cloud computing system and can provide specific services to users.\\ \\ The dissertation also discusses the novel dynamic query execution engine within the data query infrastructure that dynamically adapts to network and node (or droplet) conditions. The query processing is capable of fully benefiting from all the distributed resources to minimize the query response time and maximize system throughput. In addition to leveraging the traditional distributed query optimization techniques, the (sub)queries are deployed and executed on droplets in a dynamic and iterative manner in order to guarantee the best reaction to network and resource dynamics.\\ \\ Finally, the dissertation presents an extension to the basic DObjects model that enables access to private data that is distributed and needs anonymization. The extension enables droplets to form virtual groups in order to addresses two important privacy issues for the sensitive data: privacy of data subjects and confidentiality of data providers. The dissertation discusses decentralized protocols that enable data sharing for horizontally partitioned databases given these constraints. These protocols can be run by the groups of droplets. Concretely, given a query spanning multiple databases, the query results do not contain individually identifiable information. In addition, institutions do not reveal their databases to each other apart from the query results.

TBA

I will introduce some basic notions from "large scale geometry" and carry out a brief survey of some of the applications to group theory and graph theory. I will mostly be concerned with infinite groups and graphs.

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.

Exactly 150 years ago, Riemann published a short paper (in German) called "On the Number of Primes Less Than a Given Magnitude." In it, he speculated that the zeros of the zeta function all lie on a certain line called the critical strip. This conjecture, now called the Riemann Hypothesis, is the greatest unsolved problem in all of mathematics. In this talk we'll introduce the Hypothesis and try to explain why mathematicians find this arcane-sounding problem so compelling.

Exploring and analyzing data become more challenging as the data grow larger and become more complex. If a picture truly can be worth a thousand words, then clever visualizations of data should hold promise in helping people with sense-making tasks. I firmly believe that visual representations of data can help people to better explore, analyze, and understand it, thus transforming the data into information. In this talk, I will explain how information visualization and visual analytics help people make sense of data and I will illustrate this idea through a number of examples. I also will describe my current research into visualization for investigative analysis. This project explores how visual analytics can help investigators examine a large document collection in order to discover embedded stories and narratives scattered across the documents in the collection. // John Stasko is Professor and Associate Chair of the School of Interactive Computing at Georgia Tech. He received his B.S. degree in Mathematics from Bucknell University, and SC.M. and Ph.D. in Computer Science from Brown. His primary research area is human-computer interaction, with a focus on information visualization and visual analytics.

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.

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.

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.