PAKDD2011

List of Keynotes

Hadoop: Industry View and Future Directions

Dr. Amr Awadallah
Co-Founder and CTO of Cloudera

Abstract:
Apache Hadoop is an open source data management solution that is being used to address the massive volume, velocity and variety of data that enterprises are facing today. In this keynote, Dr. Awadallah will provide an overview of Hadoop, explore common Hadoop use cases, and discuss how it is has been integrated with traditional technologies currently used in the modern data center. In the second half of the talk Amr will cover some of the future directions for Hadoop and its associated ecosystem of applications (HBase, Hive, Pig, Oozie, Flume, Hue, Zookeeper, Mahout, and Sqoop).

Bio:
Dr. Amr Awadallah is Co-Founder and CTO of Cloudera where he is responsible for all engineering efforts from product development to release, for both the open source projects and Cloudera’s proprietary software. Prior to Cloudera Amr served as Vice President of Engineering at Yahoo!, and led a team that used Hadoop extensively for data analysis and business intelligence across the Yahoo! Online services. Amr holds a Bachelor’s and Master’s degrees in Electrical Engineering from Cairo University, Egypt, and a Doctorate in Electrical Engineering from Stanford University.

Supernetworks: Opportunities and Challenges for Decision-Making in the 21st Century

Professor Anna Nagurney
University of Massachusetts Amherst, USA

Abstract:
Supernetworks as “Networks of networks” provide a rich conceptual, visual, and methodological framework for capturing the complexities and realities of decision-making in the 21st century. In this talk, I will highlight the origins of supernetworks, and will present an overview of centralized versus decentralized behavior (and associated paradoxes with relevance to both congested transportation networks and the Internet), along with some of the fascinating applications of supernetworks. Specific applications that will be discussed include: the integration of social networks with supply chains and financial networks, electric power generation and distribution networks, the teaming of organizations for humanitarian operations, and knowledge networks. Highlights of recent methodological advances, empirical results, and approaches to supernetwork analysis and design (as well as new opportunities for research) will also be discussed.

Bio:
Anna Nagurney is the John F. Smith Memorial Professor in the Department of Finance and Operations Management in the Isenberg School of Management at the University of Massachusetts Amherst. She is also an Affiliated Faculty Member in the Department of Civil and Environmental Engineering and the Department of Mechanical and Industrial Engineering at UMass Amherst. She is the first female to be appointed to a named Professorship in the University of Massachusetts system. She is the Founding Director of the Virtual Center for Supernetworks and the Supernetworks Laboratory for Computation and Visualization at UMass Amherst. She received her AB, ScB, ScM, and PhD degrees from Brown University in Providence, Rhode Island. She devotes her career to education and research that combines operations research/management science, economics, and engineering. Her focus is the applied and theoretical aspects of decision-making on network systems, particularly in the areas of transportation and logistics, energy and the environment, and economics and finance. Her most recent book, with Q. Qiang, is Fragile Networks: Identifying Vulnerabilities and Synergies in an Uncertain World, published by Wiley in July 2009. She is also the author of Supply Chain Network Economics: Dynamic of Prices, Flows, and Profits, published in July 2006. She has authored or co-authored 8 other books including Supernetworks: Decision-Making for the Information Age, Financial Networks, Sustainable Transportation Networks, and Network Economics, edited the book, Innovations in Financial and Economic Networks, and authored or co-authored more than 140 refereed journal articles and numerous book chapters.

She has given invited and plenary talks in Austria, Ukraine, Sweden, New Zealand, China, Germany, Italy, Canada, Australia, Cyprus, Iceland, the US, and other countries and her research has garnered funding from many foundations, including the National Science Foundation.

A Learning Framework for Data Objects with Complex Semantics

Professor Zhi-Hua Zhou
Nanjing University, China

Abstract:
A data object is usually represented by a single feature vector in traditional learning settings. Though such a formulation has achieved great success, its utility is limited in dealing with data objects involving complex semantics where one object can belong to multiple semantic categories simultaneously. For example, an image showing a lion besides an elephant can be recognized simultaneously as an image on lion, elephant, wild or even Africa; the text document “Around the World in Eighty Days” can be put into multiple categories such as scientific novel, Jules Verne’s writings or even books on traveling simultaneously; a web page introducing the Bird’s Nest Stadium can be categorized as a web page on Olympics, sports or even Beijing city, etc. Such data objects are ubiquitous in real applications and need to be tackled in learning tasks. In this talk we will introduce the MIML framework which has been shown promising for learning data objects with complex semantics

Bio:
Zhi-Hua Zhou is a Cheung Kong Professor at the Department of Computer Science and Technology, Nanjing University, China. His research interests are mainly in machine learning, data mining, pattern recognition, and artificial intelligence. In these areas he has published over 80 papers in leading international journals or conferences, and holds 11 patents. He is an Associate Editor-in-Chief of "Chinese Science Bulletin", Associate Editor of "IEEE Transactions on Knowledge and Data Engineering" and "ACM Transactions on Intelligent Systems and Technology", and on the editorial boards of various other journals. He also serves/served as guest editor for many journals such as “Machine Learning”, “Pattern Recognition” , “IEEE Intelligent Systems”, etc. He is the Founding Steering Committee Co-Chair of ACML, and Steering Committee member of PAKDD and PRICAI. He served as Program Committee Chair/Co-Chair of PAKDD'07, PRICAI'08 and ACML'09, Vice Chair or Area Chair or Senior Program Committee member of many conferences such as KDD, ICDM, SDM, ECMLPKDD, etc. He is the Chair of the Machine Learning Society of the China Association of Artificial Intelligence (CAAI), the Vice Chair of the Artificial Intelligence and Pattern Recognition Society of the China Computer Federation (CCF), and the Chair of the IEEE Computer Society Nanjing Chapter.