The Interplay Between Mathematics/Computation and Analytics Haesun Park Division of Computational Science and Engineering Georgia Institute of Technology.

Slides:



Advertisements
Similar presentations
Pat Langley Computational Learning Laboratory Center for the Study of Language and Information Stanford University, Stanford, California
Advertisements

Prof. Carolina Ruiz Department of Computer Science Worcester Polytechnic Institute INTRODUCTION TO KNOWLEDGE DISCOVERY IN DATABASES AND DATA MINING.
Career Opportunities in Statistical Computing. Two Perspectives on Careers in Statistical Computing 1.Software development opportunities at SAS 2.Emerging.
A. John Bailer Statistics and Statistical Modeling in The First Two Years of College Math.
EvaluationIntroVis/GfxInteractionWrap-up Thinking Interactively with Visualizations Remco Chang UNC Charlotte Charlotte Visualization Center.
Prof. Carolina Ruiz Computer Science Department Bioinformatics and Computational Biology Program WPI WELCOME TO BCB4003/CS4803 BCB503/CS583 BIOLOGICAL.
VALTChessVA IntroAppsWrap-up 1/25 User-Centric Visual Analytics Remco Chang Tufts University Department of Computer Science.
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Introduction to IR Research ChengXiang Zhai Department of Computer.
Data Visualization STAT 890, STAT 442, CM 462
New Geometric Methods of Mixture Models for Interactive Visualization PIs: Jia Li, Xiaolong (Luke) Zhang, Bruce Lindsay Department of Statistics College.
Automated Changes of Problem Representation Eugene Fink LTI Retreat 2007.
Intelligent Systems Group Emmanuel Fernandez Larry Mazlack Ali Minai (coordinator) Carla Purdy William Wee.
Building Knowledge-Driven DSS and Mining Data
Data Mining – Intro.
Advanced Database Applications Database Indexing and Data Mining CS591-G1 -- Fall 2001 George Kollios Boston University.
Presented To: Madam Nadia Gul Presented By: Bi Bi Mariam.
Data Mining.
© Heikki Topi Computing and University Education in Analytics ACM Education Council San Francisco, CA November 2, 2013 Heikki Topi, Bentley University.
LLNL-PRES This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
APPLICATION : DIAGNOSTIC CODING 1 SIEMENS  Coding is the translation of diagnosis terms describing patients diagnosis or treatment into a coded number.
OLAM and Data Mining: Concepts and Techniques. Introduction Data explosion problem: –Automated data collection tools and mature database technology lead.
Data Mining. 2 Models Created by Data Mining Linear Equations Rules Clusters Graphs Tree Structures Recurrent Patterns.
CS598CXZ Course Summary ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign.
Last Words COSC Big Data (frameworks and environments to analyze big datasets) has become a hot topic; it is a mixture of data analysis, data mining,
COMP 875 Machine Learning Methods in Image Analysis.
MACHINE LEARNING 張銘軒 譚恆力 1. OUTLINE OVERVIEW HOW DOSE THE MACHINE “ LEARN ” ? ADVANTAGE OF MACHINE LEARNING ALGORITHM TYPES  SUPERVISED.
Steps Toward an AGI Roadmap Włodek Duch ( Google: W. Duch) AGI, Memphis, 1-2 March 2007 Roadmaps: A Ten Year Roadmap to Machines with Common Sense (Push.
Information Systems Basic Core Specialization Clinical Imaging BioInformatics Public Health Computer Science Methods (formal models) Biomedical Decision.
Chapter 1 Introduction to Data Mining
David S. Ebert David S. Ebert Visual Analytics to Enable Discovery and Decision Making: Potential, Challenges, and.
FODAVA-Lead Research Dimension Reduction and Data Reduction: Foundations for Interactive Visualization Haesun Park Division of Computational Science and.
1 SUPPORT VECTOR MACHINES İsmail GÜNEŞ. 2 What is SVM? A new generation learning system. A new generation learning system. Based on recent advances in.
FODAVA-Lead Education, Community Building, and Research: Dimension Reduction and Data Reduction: Foundations for Interactive Visualization Haesun Park.
Computing and Communications and Biology Molecular Communication; Biological Communications Technology Workshop Arlington, VA 20 February 2008 Jeannette.
Ihr Logo Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Turban, Aronson, and Liang.
Data Mining – Intro. Course Overview Spatial Databases Temporal and Spatio-Temporal Databases Multimedia Databases Data Mining.
Visual Analytics Research and Education Kwan-Liu Ma Department of Computer Science University of California, Davis.
Chapter 5: Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization DECISION SUPPORT SYSTEMS AND BUSINESS.
Data Mining BY JEMINI ISLAM. Data Mining Outline: What is data mining? Why use data mining? How does data mining work The process of data mining Tools.
Machine Learning Extract from various presentations: University of Nebraska, Scott, Freund, Domingo, Hong,
Data Visualization Michel Bruley Teradata Aster EMEA Marketing Director April 2013 Michel Bruley Teradata Aster EMEA Marketing Director.
Chapter 4 Decision Support System & Artificial Intelligence.
ICT-enabled Agricultural Science for Development Scenarios, Opportunities, Issues by ICTs transforming agricultural science, research & technology generation.
Digital Libraries1 David Rashty. Digital Libraries2 “A library is an arsenal of liberty” Anonymous.
JOURNAL Which event was most interesting for your birth year? Why are the numbers in these events important? What event was most surprising to you?
Pertemuan 16 Materi : Buku Wajib & Sumber Materi :
Data Mining Concepts and Techniques Course Presentation by Ali A. Ali Department of Information Technology Institute of Graduate Studies and Research Alexandria.
David M. Kroenke and David J. Auer Database Processing Fundamentals, Design, and Implementation Appendix J: Business Intelligence Systems.
Copyright © 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 28 Data Mining Concepts.
Introduction to Azure Machine Learning and Data Mining algorithms Oleksandr Krakovetskyi CEO, DevRain Solutions PhD, Microsoft Regional
FNA/Spring CENG 562 – Machine Learning. FNA/Spring Contact information Instructor: Dr. Ferda N. Alpaslan
CS570: Data Mining Spring 2010, TT 1 – 2:15pm Li Xiong.
The KDD Process for Extracting Useful Knowledge from Volumes of Data Fayyad, Piatetsky-Shapiro, and Smyth Ian Kim SWHIG Seminar.
Term Project Proposal By J. H. Wang Apr. 7, 2017.
Data Mining – Intro.
Eick: Introduction Machine Learning
School of Computer Science & Engineering
Introduction to IR Research
中国计算机学会学科前沿讲习班:信息检索 Course Overview
Course Summary (Lecture for CS410 Intro Text Info Systems)
Datamining : Refers to extracting or mining knowledge from large amounts of data Applications : Market Analysis Fraud Detection Customer Retention Production.
What is Pattern Recognition?
Data Warehousing and Data Mining
CSc4730/6730 Scientific Visualization
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Introduction to Visual Analytics
Data Mining: Concepts and Techniques
Data Mining.
Christoph F. Eick: A Gentle Introduction to Machine Learning
Presentation transcript:

The Interplay Between Mathematics/Computation and Analytics Haesun Park Division of Computational Science and Engineering Georgia Institute of Technology FODAVA Review Meeting Dec. 3, 2009

FODAVA and Me PhD 87 Cornell, numerical comp. (numerical linear algebra), parallel computing, signal processing,.. Early 00’s, data analysis, text analysis, bioinformatics – dimension reduction (LDA, NMF), classification, clustering, NSF Program Director CCF –TF/Numeric, Symbolic, Geometric Computing –Graphics and Vis until Larry Rosenblum –MSPA/MCS (Visual Analytics from Larry R.) 2005, Georgia Tech, Division of Computational Science and Eng. 2007, Apr., DIMACS Workshop: Recent Advances in Math. and Information Sciences for Analysis and Understanding of Massive and Diverse Sources of Data, Wen Masters of ONR, and Fred Roberts of DIMACS, 2007 Fall, FODAVA, on campus team already formed based on CSE Seminar series (J. Stasko, …)

Visual Analytics Visual Analytics is the Science of Analytical Reasoning facilitated by Interactive Visual Interfaces (Thomas and Cook) Visual Analytics combines automated analysis techniques with interactive visualizations for an effective understanding, reasoning and decision making on the basis of very large and complex data sets (Keim et al.) Analytical Reasoning Data Representation and Transformation Production Presentation and Dissemination Visual Representation and Interaction I see, therefore, I analyze better. I see, therefore, I analyze better. “Solving a problem simply means representing it so that the solution is obvious.” Herbert Simon, 96

Visual Analytics is Truly Interdisciplinary Community very broad –Vis, HCI, Database, Cognitive Science, –FODAVA : math, statistics, computational science, data analysis, … Challenges –Communications –Different communities are used to different problem settings Opportunities –Maximally utilizing what human and computer can offer –Visual Representation and Interaction Writable vis (in contrast to readable vis) makes vis useful (P. Hanrahan) is the key facilitator between human and data

Data Representation & Transformation Tasks Classification Clustering Regression Dimension reduction Density estimation Retrieval of similar items Automatic summarization … Mathematical, Statistical, and Computational Methods Modules in Data and Visual Analytics System Analytical Reasoning Tasks Identify the individual leaking classified information Determine if a set of suspicious events are related Predict the next stock market crash Identify best medical treatment based on genomic, population data … Human Knowledge Visual Representation and Interaction

Challenges Data Representation and Transformation vs. Analytical Reasoning Tasks Data representation and transformation concerned with answering questions for which solution process is rather well defined Analytical reasoning concerned with determining what questions to ask (e.g., formulating hypotheses) Analysts must continually iterate between these tasks Evaluation ? Scalability ? How does the Interplay come together ? Visualization is the way for the interplay to occur effectively Better identification of mapping between existing data representation and transformation and the steps of analytical reasoning tasks needed Expansion and refinement of data analytical tasks needed for extended mapping Careful design of visual representation and interaction

Interdisciplinary Activities Close collaboration of FODAVA teams with people in VA from vis and/or analytical reasoning community critical (ex. J. Stasko, NVAC (J. Thomas, S. Bohn), W. Ribarsky, DHS CoE: David Ebert ) FODAVA Test bed

Where to publish? IEEE TPAMI - IEEE Trans. on Pattern Analysis and Machine Intelligence JMLR – Journal of Machine Learning Research Information Visualization InfoVis - IEEE Information Visualization Conference VAST - IEEE Visual Analytics Science and Technology NIPS - Neural Information Processing Systems ICML - International Conference on Machine Learning SIGKDD - ACM Special Interest Group on Knowledge Disc. and Data Mining SDM - SIAM Conference on Data Mining ICDM - IEEE International Conference on Data Mining CVPR - Conference on Computer Vision and Pattern Recognition WWW - International World Wide Web Conference WSDM - International Conference on Web Search and Data Mining AISTATS - International Conference on Artificial Intelligence and Statistics CompStat - International Conference on Computational Statistics JSM - (American Statistical Association's) Joint Statistical Meetings