Dr. Aggarwal is Director of the Computer and Vision Research Center (CVRC) which is devoted to the analysis and understanding of images, signals and data, and to the development of the computer resources required to accomplish these tasks. Research objectives include the development of computational models, algorithms, and architectures for computer vision. Current Center activities include projects in automatic target recognition and automatic recognition of human motion and interactions. Dr. Aggarwal is the Cullen Trust For Higher Education Endowed Professor In Engineering No. 2.

Computer Vision and Recognition of Human Activities
Abstract: Computer Vision is the use of a camera and a computer to recognize objects, people and/or events, automatically. It is a relatively young field of research and development that has had its beginning in the early 60’s. However, it has matured fairly quickly. Today it is contributing to the solution of some of the most pressing societal problems. The overall goals of computer vision include the construction of scene description from images, understanding of images or making useful decisions about physical objects through sensed images. In general, the construction of 3D scene from 2D images is an ill-posed problem. Noise, occlusion, variable lighting conditions and distortion due to projection make the problem difficult and the solution very domain specific. The talk will describe what computer vision entails and its components, issues, accomplishments, applications and challenges. Professor Aggarwal has been interested in computer vision from its inception. The talk will focus on two aspects of his work - motion and recognition of human activities. Motion has been the focus of interdisciplinary studies since the time when Zeno posed his paradox in 500BC. Recognition of human is subject of intense research in view of need for surveillance and other similar interests. His past and current research interests - the recognition of human activities, interaction of human with objects, and the recognition of faces will be discussed. Recent products that computer vision research has produced and the future directions of computer vision research will also be explored.

Dr. Paul J.M. Havinga is professor in the Computer Science department at the University of Twente in the Netherlands and CTO of Ambient Systems in Enschede. He received his PhD on the thesis entitled "Mobile Multimedia Systems" in 2000, and was awarded with the 'DOW Dissertation Energy Award' for this work. His current research interests are in the field of ambient intelligence, distributed computing, and embedded wireless networking. Research questions cover architectures, protocols, programming paradigms, algorithms, and applications. This research has resulted in over 200 scientific publications in journals and conferences. He is project manager of several international projects on Ambient Intelligence and wireless sensor networks. In 2004 he founded the company Ambient Systems B.V., which develops very low-power embedded wireless networking platforms and applications, with a special focus on industrial applications, logistics and transport.

Smart Dust - science, fiction, or a real business opportunity
Abstract: Rapid advances in technology have enabled a new generation of tiny, inexpensive, networked sensors. Sensors are tiny devices capable of capturing physical information, such as heat, light or motion, about an environment. Embedding numerous of sensors into an environment creates a digital skin or wireless network of sensors, each sensor capable of capturing physical information about its immediate space. These massively distributed sensor networks communicate with one another and summarize the immense amounts of low-level information to produce data representative of the overall environment. Cooperative wireless sensor networks present information in a qualitative, human-interpretable form, which allows the system and the people to respond intelligently. The past several years of wireless sensor network research have resulted in advancements in many areas. The generality and potential of this will be investigated in various application domains, which span the land, the sea, and also the air. This talk will highlight some of the challenges as we are advancing this technology from academic prototypes to broad commercial usage.

Dr. Chunming Qiao is a Professor at SUNY Buffalo where he directs the Lab for Advanced Network Design, Analysis, and Research (LANDER). His pioneering research on Optical Internet, in particular, the optical burst switching (OBS) paradigm is internationally acclaimed. In addition, his work on integrated cellular and ad hoc relaying systems (iCAR) is recognized as the harbinger for today's convergence between heterogeneous wireless technologies, and has been featured in BusinessWeek and Wireless Europe, as well as at the websites of New Scientists and CBC. Dr. Qiao have given several keynotes, tutorials and invited talks on the above research topics. He is on the editorial board of several journals and magazines including IEEE/ACM Transactions on Networking (ToN) and has chaired and co-chaired a dozen of international conferences and workshops and currently chairs the IEEE Technical Committee on High Speed Networks (HSN) and also a Subcommittee on Integrated Fiber and Wireless Technologies (FiWi).

Optical and Wireless Integration for Access and Metro Networks
Abstract: While fiber-optic technologies have been traditionally deployed in backbone networks, they are also finding niche applications in the access domain (e.g., in the form of Passive Optical Networks or PONs). Meanwhile, RF-based wireless technologies have already changed the way we communicate, and yet the demands for higher bandwidth, longer reach and better interoperability remain strong. In this talk, I will briefly outline a vision for an integrated system using both optics and wireless technologies and discuss the cost benefit of such an integrated system based on a combination of PON and WiMAX, for example, as well as its flexibility in terms of resource allocation and load balancing among multiple “cells”.

Jaideep Srivastava is a professor at the University of Minnesota, where he has established and led a research laboratory which conducts research in the information and knowledge aspects of computing. He has supervised 25 Ph.D. dissertations and 50 M.S. theses, and authored or co-authored over 200 papers in refereed journals and conferences. Dr. Srivastava have served on the editorial boards of various journals, including IEEE TPDS, IEEE TKDE, and the VLDB journal. He has also served as Program and Conference Chair for a number of prominent conferences, especially in the area of data mining, and is on the Steering Committee for the PAKDD series of conferences. He has delivered a number of keynote addresses, plenary talks, and invited tutorials at major conferences. Dr. Srivastava has a very active interaction with the industry, in both consulting and executive roles. Specifically, during a 2-year sabbatical during 1999-2001, he lead a corporate data mining team at Amazon.com (www.amazon.com) and built a data analytics department at Yodlee (www.yodlee.com) from the ground up. More recently, he spent two years as the Chief Technology Officer for Persistent Systems (http://en.wikipedia.org/wiki/Persistent_Systems), where he built an R&D division and oversaw the redesign of the training and technical vitalization program for 2,200+ engineers. He has provided technology and technology strategy advice to a number of large corporations including Cargill, United Technologies, IBM, Honeywell, 3M, and Eaton. He has served in an advisory capacity to a number of small companies, including Lancet Software and Infobionics. Dr. Srivastava has also played an active advisory role in the government sector. Specifically, he has served as the US federal government's expert witness in a nationally significant tax case. He is presently serving as Senior Technology Advisor to the State of Minnesota, and is on the Technology Advisory Council to the Chief Minister of Maharashtra, India. He is a Fellow of the IEEE, and has been an IEEE Distinguished Visitor.

Data Mining for Social Network Analysis
Abstract: A social network is defined as a social structure of individuals, who are related (directly or indirectly to each other) based on a common relation of interest, e.g. friendship, trust, etc. Social network analysis is the study of social networks to understand their structure and behavior. Social network analysis has gained prominence due to its use in different applications - from product marketing (e.g. viral marketing) to search engines and organizational dynamics (e.g. management). Recently there has been a rapid increase in interest regarding social network analysis in the data mining community. The basic motivation is the demand to exploit knowledge from copious amounts of data collected, pertaining to social behavior of users in online environments. A prime example of this are the research efforts dedicated towards the Enron email dataset. Data mining based techniques are proving to be useful for analysis of social network data, especially for large datasets that cannot be handled by traditional methods. This talk will provide an up-to-date introduction to the increasingly important field of data mining in social network analysis, and a brief overview of research directions in this field. We first provide an introduction to social network analysis and then briefly survey the research in this field. Next, an overview of emerging research in data mining for social network analysis is presented. Finally, we will present our own work in two areas: (i) data mining for socio-cognitive analysis of email networks, and (ii) data mining on logs from massively multi-player online (MMO) games to understand social and group dynamics amongst players.