Tulane Computer Science Seminars

 

Below is the list of talks in the computer science seminar series. Unless otherwise noted, the seminars meet on Fridays at 3pm in Stanley-Thomas 302. If you would like to receive notices about upcoming seminars, you can subscribe to the announcement listserve by following this link.


Here is a list of the talks:


  1.    April 12, 2013: Bruce Donald, Duke University

    Computer Science Department Distinguished Lecture

    Title: Computational Protein Design, With Applications to Cystic Fibrosis and HIV  

     Abstract: Some of the most challenging and influential opportunities for Physical Geometric Algorithms (PGA) arise in developing and applying information technology to understand the molecular machinery of the cell. Our recent work shows that PGA techniques may be fruitfully applied to the challenges of structural molecular biology and rational drug design. Concomitantly, a wealth of interesting computational problems arise in proposed methods for discovering new pharmaceuticals.


In this talk, I will discuss recent results on computational protein design. Computational protein design is a transformative field with exciting prospects for advancing both basic science and translational medical research. My laboratory has developed protein design algorithms and used them to design new drugs for leukemia; redesign an enzyme to diversify current antibiotics; to design protein-protein interactions; design probes to isolate broadly-neutralizing HIV antibodies; and predict MRSA resistance mutations to new antibiotics. At the heart of this research lies OSPREY, a software package implementing provable design algorithms of considerable intrinsic interest. I will introduce these algorithms for protein design. Then, I will discuss two applications: (1) designing drugs for cystic fibrosis, and (2) designing antibodies against HIV-1. Computational, and experimental (in vitro, and ex vivo) results will be presented.


About the Speaker: Bruce Donald is the James B. Duke Professor of Computer Science at Duke University, and Professor of Biochemistry in the Duke University Medical Center. He was a professor in the Computer Science Department at Cornell University from 1987-1998. Dr. Donald received a B.A. from Yale University, and a Ph.D. from MIT. He has been a National Science Foundation Presidential Young Investigator, and was awarded a Guggenheim Fellowship for his work on algorithms for structural proteomics. Donald is a Fellow of the ACM and the IEEE. His most recent book, Algorithms in Structural Molecular Biology, was published by The MIT Press (Cambridge, 2011).


  1.   April 5, 2013: Ryan Lilien , Cadre Research Labs, 

                    University of Toronto

    Title: Guns, Drugs and Computation*  

     Abstract: In this talk I will highlight two current yet very different projects taking place in my labs. First, I'll discuss the development of a 3D imaging and analysis system for firearm forensics. The topography of micro-toolmarks imprinted on the head of a fired carridge case (shell) serve as a 'fingerprint'; cases with highly similar surface topographies are likey to have been fired through the same firearm. Based off the retrographic sensor of Adelson and Johnson (MIT), we are developing a system for imaging the three-dimensional surfaces of fired cartridge cases and comparing the measured topographies using feature-based image analysis.


In the second half of the talk I'll highlight a novel modeling approach for predicting the emergence of drug resistance. The problem of modeling drug resistance, like many computational biology problems, manifests as a combinatorial search and optimization problem. The ability to predict drug resistance allows the prioritization of drug candidates and increased clinical vigilance. Our work represents some first, promising, steps towards this goal.


*No expertise in firearms or computational biology required


Work is supported by the NIST, the NIJ, the Natural Sciencees and Engineering Research Council of Canada, and the Gates Foundation. Collaboration with Maria Safi (UofT), Marcus Brubaker (Cadre), Todd Weller (Oakland PD), Kimo Johnson (GelSight).


  1.    February 22, 2013: Hanan Samet, University of Maryland

    Computer Science Department Distinguished Lecture

    Title: Place-based Information Systems: Textual Location Identification and

              Visualization  

     Abstract:The popularity of web-based mapping services such as Google Earth/Maps and Microsoft Virtual Earth (Bing), has led to an increasing awareness of the importance of location data and its incorporation into both web-based search applications and the databases that support them, In the past, attention to location data had been primarily limited to geographic information systems (GIS), where locations correspond to spatial objects and are usually specified geometrically.  However, in the web-based applications, the location data often corresponds to place names and is usually specified textually.


An advantage of such a specification is that the same specification can be used regardless of whether the place name is to be interpreted as a point or a region.  Thus the place name acts as a polymorphic data type in the parlance of programming languages.  However, its drawback is that it is ambiguous.  In particular, a given specification may have several interpretations, not all of which are names of places.  For example, ``Jordan'' may refer to both a person as well as a place.  Moreover, there is additional ambiguity when the specification has a place name interpretation.  For example, ``Jordan'' can refer to a river or a country while there are a number of cities named ``London''.


In this talk we examine the extension of GIS concepts to textually specified location data and review search engines that we have developed to retrieve documents where the similarity criterion is not based solely on exact match of elements of the query string but instead also based on spatial proximity.  Thus we want to take advantage of spatial synonyms so that, for example, a query seeking a rock concert in Tel Aviv would be satisfied by a result finding a rock concert in Herzliyah of Petach Tikva.  This idea has been applied by us to develop the STEWARD (Spatio-Textual Extraction on the Web Aiding Retrieval of Documents) system for finding documents on website of the Department of Housing and Urban Development.  This system relies on the presence of a document tagger that automatically identifies spatial references in text, pdf, word, and other unstructured documents.  The thesaurus for the document tagger is a collection of publicly available data sets forming a gazetteer containing the names of places in the world.  Search results are ranked according to the extent to which they satisfy the query, which is determined in part by the prevalent spatial entities that are present in the document.  The same ideas have also been adapted by us to collections of news articles as well as Twitter tweets resulting in the NewsStand and TwitterStand systems, respectively, which will be demonstrated along with the STEWARD system in conjunction with a discussion of some of the underlying issues that arose and the techniques used in their implementation.  Future work involves applying these ideas to spreadsheet data.


  1. January 4, 2013: Jeff Phillips, University of Utah

    Title: Coresets for uncertain data

    Abstract: I will discuss computational geometry problems on uncertain data.  That is, instead of a point set where each point has a known position, we consider point sets where each point has a probability distribution describing its location.  We often consider the case where each uncertain point has a finite number of possible locations, motivated by scenarios where each object has been observed multiple times with slightly different results. 


Next I will argue that when considering uncertain data, any queries (e.g. mean) should not be answered with a single value, but rather a distribution of values, describing what the true answer might be under the probability distributions governing the uncertain location of the data points.  This suggests a certain type of approximate representation of the cumulative density function. 


Finally, as an efficient way to compute these queries, we discuss methods to construct small coresets for large uncertain data sets which permit efficient answers of queries, up to the approximate guarantees suggested above.  The first approach is a Monte Carlo sampling approach that selects possible locations from each uncertain point, and we apply it to shape fitting (a slightly old result) and nearest neighbor queries (a new result).  The second approach selects a subset of uncertain points including their full probability distributions, and we apply this to various sorts of range counting queries (also a new result).


  1. May 7, 2012: Brent Venable, University of Padova, Italy

    Title: Compact preference models in single- and multi-agent settings

    Abstract: As preferences are fundamental for the analysis of human choice behavior, they are becoming of increasing importance for computational fields such as artificial intelligence (AI). Their embedding in intelligent systems calls for both expressive and, at the same time, compact representation models as well as for efficient reasoning machinery.In this talk we will start by giving a brief overview of soft constraints and CP-nets: two of the most successful AI compact preference frameworks currently used to represent the preferences of a single agent.  We will discuss how, for example, uncertainty about preferences can be efficiently dealt with within such frameworks. We will also show how such models can be embedded in multi-agent settings, such as decision making via voting, by equipping them with appropriate reasoning tools and adapting voting protocols to compact preference structures. Finally, we will conclude  by highlighting some promising and exciting future research directions in this field.


  1. April 30, 2012: Carola Wenk, UTSA

    Title: Geometric Shape Matching with Applications

    Abstract: Geometric shapes are at the core of a wide range of application areas. In this talk we will discuss how approaches from computational geometry can be used to solve shape matching problems arising in a variety of applications including biomedical areas and intelligent transportation systems. In particular, we will discuss point pattern matching algorithms for the comparison of 2D electrophoresis gels, as well as algorithms to compare and process trajectories for improved navigation systems and for live cell imaging.


  1. April 27, 2012: Carl Baribault, Tulane Cancer Center

    Title: Higher Order Hidden Markov Modeling (HOHMM) for Eukaryotic Gene Prediction

    Abstract: Attempts to find novel genes using pure homology-based methods are expected to remain insufficient largely due to the roughly half of all genes in eukaryotic (intron-bearing) genomes being specific to the given organism, such as in the nematode C. Elegans, a model eukaryotic genome.  Also, the simple, 1st-order Hidden Markov Model is insufficient to leverage the typically extended range of information (as measurable via Shannon entropy) in the vicinity of the type-specific, coding-noncoding boundaries of a eukaryotic genome.  My talk will cover some of the intricacies involved in the development of this (meta-state) HOHMM for the prediction of eukaryotic, protein-coding genes, some issues for the standards of comparison among various prediction tools, and some comments on future extension of the HOHMM to include support for transcription-promoting motifs and perhaps non-coding genes as well.


  1. March 9, 2012: Ken Ford, Florida Institute for Human and Machine Cognition

    Title: Human Centered Computing

    Abstract: The emerging concept of human-centered computing represents a significant shift in thinking about intelligent machines and, indeed, about information technology in general. My talk will provide a survey of selected research activities at the Institute for Human & Machine Cognition (IHMC) developed under this framework. Human Centered Computing research requires a broader interdisciplinary range than is typically found in one organization, and IHMC staff includes computer scientists, cognitive psychologists, neuroscientists, physicians, philosophers, engineers and social scientists of various stripes, as well as some people who resist all attempts to classify them.


  1. March 2, 2012: Aron Culotta, Southeastern Louisiana University

    Title: Health Informatics and Disaster Planning using Social Media Analysis

    Abstract: The proliferation of social media (Twitter, Facebook, blogs, etc.) has created an unprecedented, continuous stream of messages containing the thoughts of millions of people. The nascent field of Social Media Analysis (SMA) combines natural language processing, data mining, machine learning, and statistics to explore what we can infer from the behavior of social media users. Recent research suggests that such analysis can provide insights into public health, finance, politics, social unrest, and natural disasters. In this sense, SMA can be understood as an alternative to slower and more costly data collection methods, such as surveys and opinion polls.


In this talk I will first give an overview of SMA methodology, then present results from three recent applications: (1) estimating national influenza rates, (2) estimating alcohol consumption volume, (3) assessing personal risk perception prior to an impeding natural disaster. These results suggest that relatively simple methods can extract socially valuable insights from this rich source of data. I will conclude with a discussion of open problems and discuss how more sophisticated machine learning algorithms (graphical models, semi-supervised learning) may expand the capabilities of this emerging field of study.


  1. February 24, 2012: Sam Landry, Tulane University School of Medicine

    Title: Antigen Structure Shapes T-cell-mediated Immunity

    Abstract: The human immune system repels infectious pathogens by attacking certain immunodominant molecular structures in the pathogens. However, pathogens like the human immunodeficiency virus (HIV) may have used molecular structures to misdirect the helper T cells. This research would reveal the rules governing how the immune system chooses targets for helper-T-cell immunity and how it decides whether to use antibodies or cytotoxic T cells in the fight against infection.


Sponsored by the 2012 D. W. Mitchell Lecture Series and the Provost's Faculty Seminars in Interdisciplinary Research.


  1. December 1, 2011: Eric Deeds, University of Kansas

    Title: The Dynamics of Assembly in Biological Networks

    Abstract: Large multicomponent protein complexes, such as the ribosome and proteasome, are crucial for cellular function.  Our work focuses on building computational models of the assembly of these structures.  Rings represent an important class of structural motifs; they can display remarkable thermodynamic stability that causes the overall assembly reaction to approach completion.  Our model of ring assembly indicates that the dynamics of this process can display complex behaviors.  We have found that rings can optimize assembly according to a wide range of criteria by exhibiting at least one protein interaction that is significantly weaker than the others in the ring.  Analysis of the experimentally available structures of heteromeric 3-membered rings indicates that most have evolved such a weak bond, as we would predict.  We have also examined the process of complex formation in the context of large protein-protein interaction and signaling networks.  These networks are combinatorially complex, in the sense that they can generate astronomical numbers of possible molecular species.  We employed a recently developed rule- and agent-based modeling technique to simulate the dynamics of two large networks.  Our results indicate that the combinatorial complexity of this network engenders "drift" in the space of molecular possibilities.   To produce large complexes that assemble reliably into well-defined, stable structures, cells have had to evolve mechanisms that constrain and eliminate this drift.


  1. September 6, 2011: David Balduzzi, Max Planck Institute, Tübingen,

    Germany

    Title: Categorization is Cognition: On the Structure of Distributed Measurements

    Abstract: This talk presents an information-geometric approach to analyzing how neurons "see the world". Neurons are modeled as probabilistic input/output devices that categorize ("measure") inputs according to the outputs they assign to them. I consider two main questions. First: How sharply do neurons categorize their inputs? This is quantified via effective information. Second: How are categorizations by neuronal assemblies composed out of sub-categorizations? The indecomposability of categorizations is quantified via integrated information, which has an interesting geometric interpretation.

    Finally, time permitting, I will sketch two very different applications of these ideas. First, it turns out that effective information relates to measures of capacity (arising in statistical learning theory) that bound the expected future performance of classifiers. Second, experimental results using transcranial magnetic stimulation suggest that integrated information is higher during wakefulness than during sleep or under anesthesia. Combining these results with a simple thought experiment, I will argue that integrated information is necessary for cognitive function.