Invited Speakers
Special Tracks
Registration Information
Conference Schedule
Images of Key West
Directions to Conference Site
Hotel
Alternative Hotels
Getting to Key West
Presenter Instructions
Key West: History and things to do
Call for Papers
Author Instructions

Invited Speakers

FLAIRS is pleased to announce that Tim Finin, Ramon López de Mántaras, and Sebastian Thrun have agreed to present invited talks at FLAIRS-20:

  • Tim Finin is a Professor in the Computer Science and Electrical Engineering Department at UMBC, the University of Maryland Baltimore County. Finin is a member of the UMBC ebiquity group where he is working on projects involving intelligent agents, the semantic web, pervasive computing , and mobile computing. He holds degrees from MIT and the University of Illinois. Prior to joining the UMBC, he held positions at Unisys, the University of Pennsylvania, and the MIT AI Laboratory. He is the author of over 250 refereed publications and has received research grants and contracts from a variety of sources. He is currently on the editorial board of several journals and is an editor in chief of the Journal of Web Semantics. Finin is a former AAAI councilor and on the the board of directors of the Computing Research Association.

    Talk Title: Finding knowledge, data and answers on the Semantic Web
    Abstract: Web search engines like Google have made us all smarter by providing ready access to the world's knowledge whenever we need to look up a fact, learn about a topic or evaluate opinions. The W3C's Semantic Web effort aims to make such knowledge more accessible to computer programs by publishing it in machine understandable form. As the volume of Semantic Web data grows software agents will need their own search engines to help them find the relevant and trustworthy knowledge they need to perform their tasks. We will discuss the general issues underlying the indexing and retrieval of RDF based information and describe Swoogle, a crawler based search engine whose index contains information on over a million RDF documents. We will illustrate its use in several Semantic Web related research projects including a distributed platform for constructing end-to-end use cases that demonstrate the semantic web's utility for integrating scientific data. We describe ELVIS (the Ecosystem Location Visualization and Information System), a suite of tools for constructing food webs for a given location, and Triple Shop, a SPARQL query interface which searches the Semantic Web for data relevant to a given query ELVIS functionality is exposed as a collection of web services, and all input and output data is expressed in OWL, thereby enabling its integration with Triple Shop and other semantic web resources.

  • Ramon López de Mántaras is a Research Professor of the Spanish Council for Scientific Research (CSIC), and Deputy Director of the Artificial Intelligence Research Institute of the CSIC. He is pioneer of Artificial Intelligence in Spain, with contributions, since 1976, in the following areas: Unsupervised Learning and Clustering, Pattern Classification, Tactile Recognition in Robotics, Multiple-valued and Fuzzy Logics for Approximate Reasoning, Knowledge Acquisition, Medical Expert Systems, Inductive Learning, Case-Based Reasoning, Autonomous Robots, AI and Music, and Bayesian Learning. In 1979-1980, while working for IKERLAN, he was adevelopper of the first expert system (for designing high power electric transformers) in Spain and one of the earliest expert systems in Europe. He is presently working in Machine Learning, Case-Based Reasoning, on the problem of Qualitative Autonomous Robot Navigation, and on AI and Music.

    Talk Title: Playing With Cases: Tempo Transformations Of Jazz Performances Using Case-Based
    Reasoning

    Abstract: An important issue when performing music is the effect of tempo on expressivity. It has been argued that temporal aspects of performance scale uniformly when tempo changes. That is, the durations of all performed notes maintain their relative proportions. This hypothesis is called relational invariance of timing under tempo changes. However, counter-evidence for this hypothesis has been provided, and a recent study shows that listeners are able to determine above chance-level whether audio recordings of jazz and classical performances are uniformly time stretched or original recordings, based solely on expressive aspects of the performances. In my talk I will address this issue by focusing on our research on tempo transformations of audio recordings of saxophone jazz performances. More concretely, we have investigated the problem of how a performance played at a particular tempo can be automatically rendered at another tempo while preserving its expressivity. To do so we have developed a case-based reasoning system called TempoExpress. Our approach also experimentally refutes the relational invariance hypothesis by comparing the automatic transformations generated by TempoExpress against uniform time stretching.

  • Sebastian Thrun, Associate Professor of Computer Science and Electrical Engineering at Stanford University, where he also serves as the Director of the Stanford AI Lab. His research focuses on robotics and artificial intelligence. Thrun has delivered numerous invited plenary presentations at leading conferences and symposia. Thrun was the leader of the team that won the DARPA Grand Challenge in 2005, was named one of the "Brilliant Ten" by Popular Science in 2005, and was elected an AAAI Fellow in 2006.

    Talk Title: Self-Driving Cars - an AI-Robotics Challenge
    Abstract: In recent years, all major automotive companies have launched initiatives towards cars that assist people in making driving decisions. The ultimate goal of all these efforts are cars that can drive themselves. The benefit of such a technology could be enormous. At present, some 42,000 people die every year in traffic accidents in the U.S., mostly because of human error. Self-driving cars could make people safer and more productive. Self-driving cars is a true AI challenge. To endow cars with the ability to make decisions on behalf of their drivers, they have to sense, perceive, and act. Recent work in this field has extensively built on probabilistic representations and machine learning methods. The speaker will report on past work on the DARPA Grand Challenge, and discuss ongoing work on the Urban Challenge, DARPA's follow-up program on self-driving cars.
________________
This website is maintained by Doug Dankel.
Page updated on 30-Apr-2007