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.
Francesca Rossi University of Padova, Italy
Abstract: Sentiment analysis assigns a positive, negative or neutral polarity to an item or entity, extracting and aggregating individual opinions from their textual expressions by means of natural language processing tools. It then aggregates the individuals' opinions into a collective sentiment about the item under consideration.
Current sentiment analysis techniques are satisfactory in case there is a single entity under consideration, but can lead to inaccurate or wrong results when dealing with a set of possibly correlated items. This can be useful, for example, when a company wants to know the collective preference order over a set of products. Or also when we want to predict the outcome of an election over a collection of candidates.
We argue that, in order to deal with this more general setting, we should exploit AI techniques such as those used in preference reasoning, multi-agent systems, and computational social choice. Preference modelling and reasoning tools provide the useful ingredients to model individual's preferences in the most faithful way, while computational social choice techniques give methods to aggregate such preferences which satisfy certain desired properties.
Other AI techniques can be very useful as well, such as machine learning or recommender system tools to cope with incompleteness in the information provided by each individual.
We describe a social choice aggregation rule which combines individuals' sentiment and preference information. We show that this rule satisfies a number of properties which have a natural interpretation in the sentiment analysis domain, and we evaluate its behavior when faced with highly incomplete domains.
Neal R. Wagner UNIVERSITY OF TEXAS AT SAN ANTONIO (RETIRED)
Abstract: First I will introduce public-key cryptosystems (PKCs). A famous example is the early Merkle-Hellman proposal to use the NP-complete problem Subset Sum (SS) as the basis for a PKC. To encrypt, one forms the sum of a subset of given numbers, but decryption requires solving SS and finding the numbers that made up the sum. For a PKC one creates an instance of SS with a "trapdoor" -- a secret design that allows an instance to be decrypted efficiently. This was the first serious proposal for a PKC. Eventually it was broken in its full generality.
Then I will talk briefly about the cryptosystem of Rivest, Shamir, and Adleman (RSA), which is based on the problem of factoring a product of primes (FACTOR). FACTOR is in NP but conjectured not to be NP-complete. Nevertheless, it is intractable right now (until we get quantum computers). I was the first person to propose using an undecidable problem as the basis for a PKC. The problem I used is the undecidable word problem for finitely presented groups (WPG). First I'll discuss the general Word Problem (WP), fairly easily shown to be undecidable. Then I will introduce extra structure to make WP into a group. This last consists of finitely many symbols called generators (each with an inverse), a collection of strings of these generators, and a collection of relators, each of which is a string of generators (and inverses) which is equal in the group to the empty string (the identity element).
WPG asks if a given string of generators and inverses can be transformed to the empty string. The talk will give a number of definitions needed to precisely define these groups. They are very complex entities.
Then I show how to encrypt a single bit with this PKC. This is a randomized PKC, with infinitely many cyphertexts corresponding to each plaintext bit. In general, decryption would be undecidable, but I will show a way to add extra secret relators to this group to make a new group in which decryption is efficient.
The talk requires no prior special knowledge.
About the Speaker: Neal Wagner retired as Associate Professor from the University of Texas at San Antonio. He received a Ph.D. in mathematics from the University of Illinois at Urbana-Champaign in 1970, specializing in topology. He also taught at the University of Texas at El Paso, the University of Houston, and Drexel University. In 1973 he was seduced by the dark side and started looking at computer science. During 1974-76 he worked as a programmer and programming supervisor on real-time simulation of the Space Shuttle at Johnson Space Center. Later, he specialized in cryptography and database security.
Leonid Ryzhyk Carnegie Mellon University
Abstract: Modern operating systems consist of millions of lines of low-level code, which is hard to understand, maintain, and validate. In this talk I will advocate the rigorous approach to OS design as a way to overcome the problem. I argue that the time has come to re-think the OS design based on a solid mathematical foundation. This long-sought vision is finally becoming feasible due to recent advances in programming languages, model checking, and interactive theorem proving. I will outline a high-level methodology for rigorous OS design, consisting of three components: (1) a theoretical foundation for formal reasoning about OS behaviour at a level of abstraction much higher than C code, (2) a software ecosystem, consisting of domain-specific languages and tools that provide a programmer-friendly interface to the theory, (3) OS implementation, built and verified on top of this ecosystem.
As a concrete instantiation of this methodology, I will present the Termite project, which has developed the first tool for automatic synthesis of correct-by-construction device drivers. In Termite, the driver behavior is formalized in terms of the discrete control theory and is automatically synthesized using controller synthesis techniques. I will show how the connection to the control theory enables efficient formal reasoning about complex low-level software. The talk will conclude with a brief demo of Termite.
Thomas Banchoff Emeritus Professor at Brown University and Visiting Professor at Sewanee, The University of the South
Abstract:On a smooth or polyhedral surface a strip neighborhood of a closed curve is either and orientable cylinder or a non-orientable Moebius band. How can we distinguish which form it has by observing singular fold chains for projections to planes or self-intersection curves of immersions (and non-immersions) into three-space or a new phenomenon, inflection faces? This talk gives an introduction to the geometry of characteristic classes and it features computer graphics images and animations that will make it accessible to novice and expert alike.
About the Speaker: Thomas Banchoff received his PhD under Prof. Shiing-Shen Chern at UC Berkeley in 1964 and he has been teaching ever since, mainly at Brown University. He has received a number of local and national teaching awards and he was president of the MAA in 1999-2000. He is the author of four books and eighty articles on geometry and topology and computer graphics. He gave an invited address at the International Congress of Mathematicians in Helsinki in 1978 on "Computer Animation and the Geometry of Surfaces in Three- and Four-Space" and he was named a fellow of the American Mathematical Society in 2012.
Brygg Ullmer Louisiana State University
Abstract: Scientific and information visualization have long offered powerful approaches for helping people graphically represent, explore, and understand our universe. Our group investigates tangible visualization. Here, tangible interfaces (which support interaction through systems of computationally-mediated physical artifacts) are used to interactively represent complex systems. We approach this research from two perspectives: studying and engaging specific application domains; and developing underlying architectures supporting realizations of tangible visualization.
In this talk, we will consider several applications engaging interactive computational science, technology, engineering, arts, and mathematics (ICy STEAM), with some emphasis on tangible visualization in the context of computational genomics. We will also discuss opportunities and trajectories relating to computational systems research; and briefly consider several intersections between high-performance computing, machine learning, and future computational systems.
About the Speaker: Brygg Ullmer is the Effie C. and Donald M. Hardy associate professor at LSU, jointly in the School of Electrical Engineering and Computer Science (EECS) and the Center for Computation and Technology (CCT). He leads CCT's Cultural Computing focus area (research division), with 16 faculty spanning six departments, and co-leads the Tangible Visualization group. He also serves as director for the NIH-supported Louisiana Biomedical Research Network (LBRN) Bioinformatics, Biostatistics, and Computational Biology (BBC) Core. Ullmer completed his Ph.D. at the MIT Media Laboratory (Tangible Media group) in 2002, where his research focused on tangible user interfaces. He held a postdoctoral position in the visualization department of the Zuse Institute Berlin, internships at Interval Research (Palo Alto) and Sony CSL (Tokyo), and has been a visiting and remote lecturer at Hong Kong Polytechnic's School of Design. His research interests include tangible interfaces, computational genomics (and more broadly, interactive computational STEAM), visualization, and rapid physical and electronic prototyping. He also has a strong interest in computationally-mediated art, craft, and design, rooted in the traditions and material expressions of specific regions and cultures.
For more information, please visit: https://cc.cct.lsu.edu/groups/tangviz/.
Nina Narodytska Carnegie Mellon University
Abstract:Two-player games are a useful formalism for the synthesis of reactive systems. The traditional approach to solving such games iteratively computes the set of winning states for one of the players. This requires keeping track of all discovered winning states and can lead to space explosion even when using efficient symbolic representations. I will present a new game solving method that works by exploring a subset of all possible concrete runs of the game and proving that these runs can be generalized into a winning strategy on behalf of one of the players. We use counterexample-guided backtracking search, to identify a subset of runs that are sufficient to consider to solve the game. Our search procedure performs learning from both players' failures, gradually shrinking winning regions for both players. In addition, it allows extracting compact winning/spoiling strategies using interpolation procedures. We evaluate our algorithm on several families of benchmarks derived from real-world device driver synthesis problems. Our approach outperforms state-of-the-art BDD solvers on some families of benchmarks. We demonstrated that our strategy extraction procedure is efficient and introduces low overhead on top of the main solver.
About the Speaker: Nina Narodytska is a postdoctoral researcher in the Carnegie Mellon University School of Computer Science. She received her PhD from the University of New South Wales in 2011. Nina works on developing efficient search algorithms for decision and optimization problems. She was named one of "AI's 10 to Watch" young researches in the field of AI. She also received an Outstanding Paper Award at AAAI 2011 and an outstanding program committee member award at the Australasian Joint Conference on Artificial Intelligence 2012. Her EvaMaxSAT Solver was a winner of the Ninth Evaluation of Max-SAT Solvers in 2014.
Shantenu Jha Rutgers University
Abstract:To support science and engineering applications that are the basis of many societal and intellectual challenges in the 21st Century, there is a need for comprehensive, balanced and flexible distributed cyberinfrastructure. The process of designing and deploying such large scale distributed cyberinfrastructure however, presents a critical and challenging research agenda along at least two different dimension: conceptual and implementation challenges.
The first challenge is the ability to architect and federate large scale distributed resources so as to have collective performance that can be designed and predicted as well as the ability to plan and reason about executing distributed workloads. The second challenge -- is to produce tools that provide a step change in the sophistication and scale of problems that can be investigated using DCI, while being extensible, easy to deploy and use, as well as being compatible with a variety of other established tools.
In the first part of the talk we will discuss how we are laying the foundations for the design and architecture of the next-generation of distributed cyberinfrastructure. In the second part of the talk, we will introduce RADICAL-Cybertools -- a standards-based, abstraction-driven approach to High-Performance Distributed Computing. RADICAL Cybertools builds upon important theoretical advances, production software development best practices and carefully analyzed usage and programming models. We will discuss several science and engineering applications that are currently using RADICAL Cybertools to utilize DCIs in a scalable and extensible fashion. We will conclude with a discussion of the connection between the two challenges.
About the Speaker: Shantenu is an Associate Professor of Computer Engineering at Rutgers University. His research interests lie at the triple point of Cyberinfrastructure R&D, Applied Computing and Computational Science. His research is currently supported by DOE and multiple NSF awards, including CAREER, SI2 (elements, integration and conceptualization), CDI and EarthCube. Shantenu leads the RADICAL-Cybertools (http://radical-cybertools.github.com) project which are a suite of standards-driven and abstractions-based tools used to support large-scale science and engineering applications. He is co-leading a "Conceptualization of an Institute for Biomolecular Simulations" project. He is also designing and developing "MIDAS: Middleware for Data-intensive Analytics and Science" as part of a HPBDS (http://arxiv.org/abs/1403.1528) -- a 2014 NSF DIBBS project. Away from work, Jha tries middle-distance running and biking, tends to indulge in random musings as an economics-junky, and tries to use his copious amounts of free time with a conscience.
Justin Domke National ICT Australia
Abstract: Probabilistic graphical models are a natural modeling framework for a range of areas, including computer vision, computational biology, and natural language processing. However, applications of graphical models are typically complicated by the fact that inference (querying a given model) is computationally intractable in general. This talk will describe two broad strategies for coping with this situation. The first is based on restricting consideration to a “tractable” set of parameters. This can be used either for inference (approximately querying an intractable model) or for learning (enforcing tractability in the process). The second strategy is based on learning with a fixed, approximate inference method. The goal of learning is then to make this approximate procedure perform well, rather than to make the model accurate under exact inference.
Johannes Wachs Corruption Research Center Budapest
Abstract: Network science is a rapidly developing field. In this talk, the speaker will present a few ways in which observed networks deviate structurally from randomness. Among more canonical examples, he will discuss the public procurement network in Hungary and how it relates to corruption.
About the Speaker: Johannes Wachs graduated from Tulane in 2009 with a BS in mathematics and from Central European University in 2012 with an MS. He worked for a time in the strategy and modeling group at Morgan Stanley. Now he works for the Corruption Research Center Budapest and will begin a PhD in network science at Central European University this fall.
Brian SummaUNIVERSITY OF UTAH
Abstract: Scientists, artists, and engineers are producing data at increasingly massive scales. However, visualization/graphics techniques for processing and interaction rarely scale as needed, leaving these users with a challenging scientific or creative process. In this talk, I will discuss how my ongoing work in designing and deploying scalable algorithms and techniques enables users to interactively explore, analyze, and process large data. In this way, my work solves a crucial need for users to understand and manipulate these big data sources. In particular, I will discuss how my work has provided scalable construction of large images as a mosaic of smaller images (panoramas in digital photography). These images can range from megapixels to hundreds of gigapixels in size. My work has brought this offline, slow, and tedious pipeline to a fully interactive and streaming user experience. I will detail a fast, light, and highly scalable approach for the computation of boundaries/seams in a mosaic. This technique has almost perfect linear scaling and provides the first, direct interaction for the editing of seams. In addition, I will describe a streaming, progressive solver for a Poisson system with application in mosaic color correction. This progressive approach allows interactive color correction on-the-fly without the need of a full solution. This technique has also been shown to scale for in-core, out-of-core, and distributed environments. I will conclude my presentation with a discussion of future research directions and emerging needs in large data processing for visualization and graphics.
About the Speaker: Brian Summa is a post doctoral researcher at the Scientific Computing and Imaging Institute at the University of Utah where he studies large data processing, analysis, and interaction. He has particular interests in visualization and computer graphics applications and has published in the top venues in both areas. Brian's work, in collaboration with his interdisciplinary partners, is helping advance several research disciplines such as geology, physics, and neurobiology. His work has supported the creation of large data processing software that scales from mobile devices to supercomputers. Applications from his research have been deployed at several University of Utah departments, Department of Energy national laboratories, a national health science laboratory, and even private corporations including a large oil and gas partner. Brian received his Ph.D. in computer science from the University of Utah where his dissertation focused on large image processing. He received his bachelor's and master's degrees from the University of Pennsylvania.
School of Science and Engineering, 201 Lindy Boggs Center, New Orleans, LA 70118 504-865-5764 email@example.com