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Colloquia Archives: 2017-2018

Below is the list of talks in the computer science seminar series for the 2017-2018 academic year. If you would like to receive notices about upcoming seminars, you can subscribe to the announcement listserv

Top ⇑Fall 2017 Colloquia

Sept 11

The Pebbling Comonad in Finite Model Theory

Samson Abramsky University of oxford and Tulane University

Abstract: Pebble games are a powerful tool in the study of finite model theory, constraint satisfaction and database theory. Monads and comonads are basic notions of category theory which are widely used in semantics of computation and in modern functional programming. We show that existential k-pebble games have a natural comonadic formulation. Winning strategies for Duplicator in the k-pebble game for structures A and B are equivalent to morphisms from A to B in the coKleisli category for this comonad. This leads on to comonadic characterisations of a number of central concepts in Finite Model Theory. These results lay the basis for some new and promising connections between two areas within logic in computer science which have largely been disjoint: (1) finite and algorithmic model theory, and (2) semantics and categorical structures of computation.

About the Speaker: Samson Abramsky is the Christopher Strachey Professor of Computing at the University of Oxford. He also is a Visiting Research Professor at Tulane, as part of the MURI project on Semantics and Tools for Higher Order Functional Quantum Programming Languages. Samson has worked in a wide range of areas in the semantics and logic of computation, including concurrency, domain theory (especially domain theory in logical form), lambda calculus, semantics of programming languages, and abstract interpretation and program analysis. He played a leading role in the development of game semantics and its applications to the semantics of programming languages, in interaction categories, and in geometry of interaction, and connections with traced monoidal categories and realizability. He and five colleagues just received the 2017 Church Award for their work in game semantics and its application to programming language semantics. He also has been a leader in establishing categorical models of quantum mechanics, and their application to quantum computing and quantum information. Samson is a Fellow of the Royal Society.
Sept 25

Modeling Spatial Auditory Attention Using Soft Constraints

Jaelle Scheuerman Tulane university

Interdisciplinary Project Presentation

Abstract: In this talk, the speaker will present an interdisciplinary effort to develop a computational model of spatial auditory attention. Spatial attention has been the focus of research in cognitive models, though much of the work has focused on studying visual attention. This model uses soft constraints and the well-established framework of constraint satisfaction problems to model how auditory attention is allocated over space. It includes three main components: a goal map, saliency map and priority map. The goal map represents attention that is allocated by choice (top-down attention). The saliency map models the distribution of salient (bottom-up) attention. Finally, a priority map combines these two maps to model the total distribution of attentional bias. The model's constraint-based approach is very flexible in terms of embedding and testing different hypotheses. The model was shown to be successful in modeling behavioral data of experiments where there is a single attended location. The framework was then extended to encompass scenarios where there may be multiple attended locations. Using the parameters learned by fitting behavioral data from a single attended location task, the model made predictions about a task where sounds are presented at multiple locations with equal probability. These predictions well fit the experimental data and provide a first example of how the computational model can be used as a predictor.

About the Speaker: Jaelle Scheuerman is a 3rd year PhD student in the Department of Computer Science. Her research interests include artificial intelligence, cognitive models and cognitive architectures. She is particularly interested in applications of preferences and constraints to models of attention and decision making.
Oct 23

Building Scalable Machine Learning Solutions for Data Curation

Ihab Ilyas University of Waterloo

Abstract: Machine learning tools promise to help solve data curation problems. While the principles are well understood, the engineering details in configuring and deploying ML techniques are the biggest hurdle. In this talk I discuss why leveraging data semantics and domain-specific knowledge is key in delivering the optimizations necessary for truly scalable ML curation solutions. The talk focuses on two main problems: (1) entity consolidation, which is arguably the most difficult data curation challenge because it is notoriously complex and hard to scale; and (2) using probabilistic inference to suggest data repair for identified errors and anomalies using our new system called HoloCLean. Both problems have been challenging researchers and practitioners for decades due to the fundamentally combinatorial explosion in the space of solutions and the lack of ground truth. There’s a large body of work on this problem by both academia and industry. Techniques have included human curation, rules-based systems, and automatic discovery of clusters using predefined thresholds on record similarity Unfortunately, none of these techniques alone has been able to provide sufficient accuracy and scalability. The talk aims at providing deeper insight into the entity consolidation and data repair problems and discusses how machine learning, human expertise, and problem semantics collectively can deliver a scalable, high-accuracy solution.

About the Speaker: Ihab Ilyas is a professor in the Cheriton School of Computer Science at the University of Waterloo, where his main research focuses on the areas of big data and database systems, with special interest in data quality and integration, managing uncertain data, rank-aware query processing, and information extraction. Ihab is also a co-founder of Tamr, a startup focusing on large-scale data integration and cleaning. He is a recipient of the Ontario Early Researcher Award (2009), a Cheriton Faculty Fellowship (2013), an NSERC Discovery Accelerator Award (2014), and a Google Faculty Award (2014), and he is an ACM Distinguished Scientist. Ihab is an elected member of the VLDB Endowment board of trustees, elected SIGMOD vice chair, and an associate editor of the ACM Transactions of Database Systems (TODS). He holds a PhD in computer science from Purdue University, West Lafayette.
Nov 13

Clustering Correctly

Justin Eldridge Ohio State University

Abstract: Clustering is an important machine learning task whose goal is to identify the natural groups (or "clusters") in data. Given a data set, what is the correct clustering? There is no single answer to this seemingly simple question. In a statistical setting, however, where it is assumed that the data are sampled from an underlying probability distribution, it is natural to define the clusters of the distribution itself. We then say that a clustering method is "correct" if its output converges in some sense to the ideal clustering of the distribution as the size of the data grows. In this talk, I discuss the correctness of clustering methods in two settings: first, when the data are points sampled from a probability density, and second, when the data are graphs generated from a graphon -- a powerful non-parametric random graph model of much recent interest. In each case, I identify the natural hierarchical cluster structure of the distribution, formalize a strong notion of convergence to the tree of the ideal clustering, and construct efficient clustering methods which converge -- and are therefore "correct".

About the Speaker: Justin Eldridge is a Ph.D. student in the Department of Computer Science and Engineering at The Ohio State University, advised by Mikhail Belkin and Yusu Wang. His research interests focus on the foundations of learning structure from unlabeled data. Justin's work with Drs. Belkin and Wang on the statistical consistency of clustering received the best student paper award at COLT 2015 and a full oral presentation at NIPS 2016. Earlier this year, Justin was a graduate visitor at the Simons Institute for the Theory of Computation at Berkeley, and he is currently a Presidential Fellow at Ohio State.
Dec 1

User-Centric Data Management for Fun, Profit, and the Common Good

Alexandros Labrinidis University of Pittsburgh

Abstract: Big data is transforming all aspects of the human experience, be it everyday life, scientific exploration and discovery, medicine, business, law, journalism, and decision-making at all levels of government. The majority of big data management research emphasizes the systems point-of-view, which focusses on efficiency and scale. This talk will showcase multiple ways where we consider the user point-of-view for big data management, and demonstrate its benefits. We will show how user preferences can positively influence resource allocation decisions, especially in overload situations. This is true both for traditional (static) data and for streaming data processing systems. We will conclude with new research directions, that are being developed as part of new urban informatics research projects.

About the Speaker: Dr. Alexandros Labrinidis is a Professor of Computer Science at the University of Pittsburgh. He joined the Department of Computer Science in 2002, right after receiving his PhD in Computer Science from the University of Maryland, College Park. He is the co-director of the Advanced Data Management Technologies Laboratory and has an adjunct professor appointment with Carnegie Mellon University. Dr. Labrinidis' research focuses on user-centric (big) data management for scalable network-centric applications, including web-databases, data stream management systems, urban informatics, sensor networks, internet of things, and scientific data management. He has published over 100 papers at peer-reviewed journals, conferences, and workshops and was the recipient of an NSF CAREER award in 2008. Dr. Labrinidis served as the Secretary/Treasurer for ACM SIGMOD and as the Editor of SIGMOD Record. He is currently the founding Editor of the Systems/Applications Track of the Parallel and Distributed Databases Journal and an Associate Editor for the VLDB Journal. He has also served on numerous program committees of international conferences/workshops.

Personal home page: http://labrinidis.cs.pitt.edu
Dec 4

Topic

Speaker Institution

Abstract: TBA

303 Stanley Thomas Hall, New Orleans, LA 70118 504-865-5785 compsci@tulane.edu