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Colloquia

Spring 2018 Colloquia

Check back soon for more information on the computer science seminar series. Unless otherwise noted, the seminars 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.

Jan 12

Pleaching Pencil-&-Paper Picture Puzzles

Maarten Löffler Utrecht University

This event will be held on Friday, 1/12/2018, from 2:00 - 3:00 p.m. in Stanley Thomas, Room 302. Please note the special weekday and time for this event.

Abstract: Pencil-and-paper puzzles (e.g., Sudoku) are a popular pastime for both children and adults. The main appeal lies in the logical solving process, but in some genres the puzzler is additionally rewarded when the solved puzzle reveals a picture (e.g., Nonograms). We introduce free-form variants of classic puzzle genres containing non-rectilinear or even curved elements. We study the underlying geometry: what constraints are there on the shapes and location of puzzle elements? How can we measure aspects of puzzles like solvability, difficulty, originality, fun, etc.? Finally, we use these geometric properties to develop automatic generators of puzzles: you draw a picture, and the system gives you a puzzle that solves to that picture.

About the Speaker: Maarten Löffler is an assistant professor at Utrecht University, working in computational geometry and graph drawing. He obtained his PhD in 2009 on geometric uncertainty, and afterwards spent two years at the University of California in Irvine. He has a passion for pencil-and-paper puzzles, participating in the World Championship in 2003 and 2006.
Feb 6

Theory and Data for Better Decisions

Nicholas Mattei IBM Research - AI, T.J. Watson Research Center

This event will be held on Tuesday, 2/6/2018, from 3:30 - 4:30 p.m. in Stanley Thomas, Room 302. Please note the special weekday and time for this event.

Abstract: The internet and modern technology enables us to communicate and interact at lightning speed and across vast distances. The communities and markets created by this technology must make collective decisions, allocate scarce resources, and understand each other quickly, efficiently, and often in the presence of noisy communication channels, ever changing environments, and/or adversarial data. Many theoretical results in these areas are grounded on worst case assumptions about agent behavior or the availability of resources. Transitioning theoretical results into practice requires data driven analysis and experiment as well as novel theory with assumptions based on real world data. I'll discuss recent work that focus on creating novel algorithms for resource allocation with applications ranging from reviewer matching to deceased organ allocation. These projects require novel algorithms and leverage data to perform detailed experiments as well as creating open source tools.

About the Speaker: Nicholas Mattei is a Research Staff Member with IBM Research AI - Reasoning Group at the IBM T.J. Watson Research Laboratory. His research is in artificial intelligence (AI) and its applications; largely motivated by problems that require a blend of techniques to develop systems and algorithms that support decision making for autonomous agents and/or humans. Most of his projects leverage theory, data, and experiment to create novel algorithms, mechanisms, and systems that enable and support individual and group decision making. He is the founder and maintainer of PrefLib: A Library for Preferences; the associated PrefLib:Tools available on Github; and is the founder/co-chair for the Exploring Beyond the Worst Case in Computational Social Choice (2014 - 2017) held at AAMAS. Nicholas was formerly a senior researcher working with Prof. Toby Walsh in the AI & Algorithmic Decision Theory Group at Data61 (formerly known as the Optimisation Group at NICTA), a lecturer in the School of Computer Science and Engineering (CSE), and member of the Algorithms Group at the University of New South Wales. He previously worked as a programmer and embedded electronics designer for nano-satellites at NASA Ames Research Center. He received his Ph.D from the University of Kentucky under the supervision of Prof. Judy Goldsmith in 2012.
Feb 15

Machine Learning-Enhanced Visualization

Matthew Berger University of Arizona

This event will be held on Thursday, 2/15/2018, from 3:30 - 4:30 p.m. in Stanley Thomas, Room 302. Please note the special weekday and time for this event.

Abstract: Visualization is indispensable for exploratory data analysis, enabling people to interact with and make sense of data. Interaction is key for effective exploration, and is dependent on two main factors: how data is represented, and how data is visually encoded. For instance, text data may be represented as a 2D spatialization and visually encoded through graphical marks, color, and size. Typically, these factors do not anticipate how a user will interact with the data, however, which limits the set of interactions one may perform in data exploration. In this talk I will focus on how machine learning can be used to improve data representations and visual encodings for user interaction. My research is centered on building machine learning models when visualization, and in particular how a user interacts with data, is the primary objective. I will first discuss how to learn data representations for the purpose of interactive document exploration. I will demonstrate how compositional properties of neural language models, built from large amounts of text data, empower the user to semantically explore document collections. Secondly, I will show how to learn visual encodings for the purpose of exploring volumetric data. Deep generative models are used to learn the distribution of outputs produced from a volume renderer, providing the user both guidance and intuitive interfaces for volume exploration.

About the Speaker: Matthew Berger is a postdoctoral research associate in the Department of Computer Science at the University of Arizona, advised by Joshua A. Levine. Previously he was a research scientist with the Air Force Research Laboratory. He received his PhD in Computing from the University of Utah in 2013, advised by Claudio T. Silva. His research interests are at the intersection of machine learning and data visualization, focusing on the development of visualization techniques that are driven by machine learning models.
Feb 19

Predictive Modeling of Drug Effects: Learning from Biomedical Knowledge and Clinical Records

Ping Zhang IBM T. J. Watson Research Center

Abstract: Drug discovery is a time-consuming and laborious process. Lack of efficacy and safety issues are the two major reasons for which a drug fails clinical trials, each accounting for around 30% of failures. By leveraging the diversity of available molecular and clinical data, predictive modeling of drug effects could lead to a reduction in the attrition rate in drug development. In this talk, I will introduce my recent work on machine-learning techniques for analyzing and predicting clinical drug responses (i.e., efficacy and safety), including: 1) integrating multiple drug/disease similarity networks via joint matrix factorization to infer novel drug indications; and 2) revealing previously unknown effects of drugs, identified from electronic health records and drug information, on laboratory test results. Experimental results demonstrate the effectiveness of these methods and show that predictive models could serve as a useful tool to generate hypotheses on drug efficacy and safety profiles.

About the Speaker: Ping Zhang is a Research Staff Member at the Center for Computational Health, IBM T. J. Watson Research Center. His research focuses on machine learning, data mining, and their applications to biomedical informatics and computational medicine. Dr. Zhang received his PhD in Computer and Information Sciences from Temple University in 2012. He has published more than 35 peer-reviewed scientific articles in top journals and conferences (e.g., Nucleic Acids Research, BMC Bioinformatics, Journal of the American Medical Informatics Association, KDD, AAAI, ECML, SDM, and CIKM) and filed more than 15 patent applications. Dr. Zhang has served on the program committees of leading international conferences, including KDD, IJCAI, UAI, and AMIA, and on the editorial boards of CPT: Pharmacometrics & Systems Pharmacology and Journal of Healthcare Informatics Research. He won the best in-use/industrial paper award for ESWC 2016 and received a Marco Ramoni Distinguished Paper nomination at AMIA Summits 2014. More details can be found at http://researcher.watson.ibm.com/researcher/view.php?person=us-pzhang.
Feb 28

From Consensus Clustering to K-means Clustering

Hongfu Liu Northeastern University

Abstract: Abstract: Consensus clustering aims to find a single partition which agrees as much as possible with existing basic partitions, which emerges as a promising solution to find cluster structures from heterogeneous data. It has been widely recognized that consensus clustering is effective to generate robust clustering results, detect bizarre clusters, handle noise, outliers and sample variations, and integrate solutions from multiple distributed sources of data or attributes. Different from the traditional clustering methods, which directly conducts the data matrix, the input of consensus clustering is the set of various diverse basic partitions. Therefore, consensus clustering is a fusion problem in essence, rather than a traditional clustering problem. In this talk, I will introduce the category of consensus clustering, illustrate the K-means-based Consensus Clustering (KCC), which exactly transforms the consensus clustering problem into a (weighted) K-means clustering problem with theoretical supports, talk about some key impact factors of consensus clustering, extend KCC to Fuzzy C-means Consensus Clustering. Moreover, this talk also includes how to employ consensus clustering for heterogeneous, multi-view, incomplete and big data clustering. Derived from consensus clustering, a partition level constraint is proposed as the new side information for semi-supervised clustering. Along this line, several interesting application based on the partition level constraint, such as feature selection, domain adaptation, gene stratification are involved to demonstrate the extensibility of consensus clustering. Some codes are available for practical use.

About the Speaker: Hongfu Liu is a final-year Ph.D. candidate of Department of Electrical & Computer Engineering, Northeastern University (NEU), supervised by Prof. Yun (Raymond) Fu. Before joining NEU, he got his master and bachelor degrees majored in management in Beihang University with Prof. Junjie Wu in 2011 and 2014, respectively. His research interests generally focus on data mining and machine learning, with special interests in ensemble learning. Website: hongfuliu.com
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Mar 12

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Mar 19

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Aristeidis Sotiras University of Pennsylvania

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Apr 2

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Apr 9

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Apr 16

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Apr 23

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Apr 30

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