Distinguished Lecture Series: Fall 2005

• November 18 : John Stankovic
  Title: Self-Organizing Wireless Sensor Networks in Action
  Location and time: Shaffer 3, 10:45 am
  
  Abstract:
  
  Wireless sensor networks (WSN), composed of large numbers of small devices that self-organize, are being investigated for a wide variety of applications. Two key advantages of these networks over more traditional sensor networks are that they can be dynamically and quickly deployed, and that they can provide fine-grained sensing. Applications, such as emergency response to natural or manmade disasters, detection and tracking, and fine grained sensing of the environment are key examples of applications that can benefit from these types of WSNs. Current research for these systems is widespread. However, many of the proposed solutions are developed with simplifying assumptions about wireless communication and the environment, even though the realities of wireless communication and environmental sensing are well known. Many of the solutions are evaluated only by simulation. In this talk I describe a fully implemented system consisting of a suite of more than 30 synthesized protocols. The system supports a power aware surveillance and tracking application running on 203 motes and evaluated in a realistic, large-area environment. Technical details and evaluations are presented for power management, dynamic group management, and for various system implementation issues. Several illustrations of how real world environments render some previous solutions unusable will also be given.
  
  BIO: Professor John A. Stankovic is the BP America Professor in the Computer Science Department at the University of Virginia. He recently served as Chair of the department, completing two terms (8 years). He is a Fellow of both the IEEE and the ACM. He also won the IEEE Real-Time Systems Technical Committee's Award for Outstanding Technical Contributions and Leadership. Professor Stankovic also served on the Board of Directors of the Computer Research Association for 9 years. Before joining the University of Virginia, Professor Stankovic taught at the University of Massachusetts where he won an outstanding scholar award. He has also held visiting positions in the Computer Science Department at Carnegie-Mellon University, at INRIA in France, and at the Scuola Superiore S. Anna in Pisa, Italy. He was the Editor-in-Chief for the IEEE Transactions on Distributed and Parallel Systems and is a founder and co-editor-in-chief for the Real-Time Systems Journal. He was also General Chair for ACM SenSys 2004 and will serve as General Chair for ACM/IEEE Information Processing in Sensor Networks (IPSN) 2006. His research interests are in distributed computing, real-time systems, operating systems, and wireless sensor networks. Prof. Stankovic received his PhD from Brown University.
  
  
• November 11 : Dr. Leonidas Guibas
  Title: Point-Based Methods in Shape Modeling and Physical Simulation
  Location and time: Shaffer 3, 10:45 am
  
  Abstract:
  
  Point-based methods have a long history in graphics for rendering, but their use in modeling and simulation is more recent. Shape representations based on sampled points faithfully reflect several 3-D acquisition technologies and point-based techniques can provide a flexible representation of geometry for situations where forming and maintaining a complete mesh can be cumbersome or complex. This talk will briefly describe some current work in simulating wide area contacts, large-scale deformation and fracture, collision-detection, and qualitative shape analysis of point-sampled data -- all carried out using meshless methods. The irregular and dynamic sampling these applications require creates new challenges and leads to methods with a distinctly more combinatorial and topological character.
  
  BIO: Leonidas Guibas obtained his Ph.D. from Stanford in 1976, under the supervision of Donald Knuth. His main subsequent employers were Xerox PARC, MIT, and DEC/SRC. He has been at Stanford since 1984 as Professor of Computer Science, where he heads the Geometric Computation group within the Graphics Laboratory. He is also part of the AI Laboratory and the Bio-X Program. Professor Guibas' interests span computational geometry, geometric modeling, computer graphics, computer vision, robotics, ad hoc communication and sensor networks, and discrete algorithms --- all areas in which he has published and lectured extensively. At Stanford he has developed new courses in algorithms and data structures, geometric modeling, geometric algorithms, sensor networks, and biocomputation. Professor Guibas is an ACM Fellow.
  
  
• November 1 : Dan Boneh
  Title: New Tools in Cryptography
  Location and time: Shaffer 202, 4:00pm
  
  Abstract:
  
  During the past five years we have seen a wealth of new cryptographic constructions based on an algebraic tool called `pairings.' These constructions give new key management mechanisms that are often simpler than what is possible with traditional cryptography. They also lead to public-key cryptographic primitives better suited for bandwidth constrained environments. This talk will survey some of these new constructions and pose a few open problems in this area. More specifically, we will present a recent broadcast encryption system and a short digital signature. The talk will be self contained.
  
  
• October 20 : Feng Zhao
  Title: Challenges in Programming Sensor Networks
  Location and time: Shaffer 3, 10:45 am
  
  Abstract:
  
  The proliferation of networked embedded devices such as wireless sensors ushers in an entirely new class of computing platforms. We need new ways to organize and program them. Unlike existing platforms, systems such as sensor networks are decentralized, embedded in physical world, and interact with people. In addition to computing, energy and bandwidth resources are constrained and must be negotiated. Uncertainty, both in systems and about the environment, is a given. Many tasks require collaboration among devices, and the entire network may have to be regarded as a processor.
  
  We argue that the existing node-centric programming of embedded devices is inadequate and unable to scale up. We need new service architectures, inter-operation protocols, programming models that are resource-aware and resource-efficient across heterogeneous devices that can range from extremely limited sensor motes to more powerful servers. I will supplement these discussions with concrete examples arising from our own work and the work of others.
  
  BIO: Feng Zhao (http://research.microsoft.com/~zhao) is a Senior Researcher at Microsoft, where he manages the Networked Embedded Computing Group. He received his PhD in Electrical Engineering and Computer Science from MIT and has taught at Stanford University and Ohio State University. Dr. Zhao was a Principal Scientist at Xerox PARC and founded PARC?s sensor network research effort. He serves as the founding Editor-In-Chief of ACM Transactions on Sensor Networks, and has authored or co-authored more than 100 technical papers and books, including a recent book published by Morgan Kaufmann - Wireless Sensor Networks: An information processing approach. He has received a number of awards, and his work has been featured in news media such as BBC World News, BusinessWeek, and Technology Review.
  
  
• October 7 : Eric Grimson
  Title: Computer Vision in Medicine and Neuroscience: Image Guided Neurosurgery and Computational Neuroanatomy
  Location and time: Shaffer 3, 10:45 am
  
  Abstract:
  
  Algorithmic methods from computer vision and machine learning are dramatically changing the practice of health care and the exploration of fundamental issues in neuroscience. By coupling knowledge of tissue response, atlases of normal anatomy, and statistical models of shape variation, these methods are used to build detailed, patient-specific reconstructions of neuroanatomical structure from MRI imagery. Such structural models can be automatically augmented with information about function (using fMRI), and about connectivity (using DT-MRI) to create detailed models of a patient's brain. These models are routinely used for surgical planning - how to reach the target tumor with minimal damage to nearby critical structures; and for surgical navigation - guiding the surgeon to the target site rapidly and safely.
  
  By combining with statistical models of population variation, these methods can also be used to investigate basic neuroscience questions - how different are the shapes of subcortical structures between normal subjects and patients with a specific disease (such as schizophrenia or Alzheimer's); how do these shapes change with development in children, or with administration of pharmaceuticals; how do physiological properties differ between populations (such as the local structure of fiber orientation in white matter tracts). These computational methods provide a toolkit for exploring the structure and connectivity of neuroanatomical structures, in normal subjects and in diseased patients.