Knowledge Discovery from Sensor Data
(SensorKDD-2009)
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Invited Speakers
In addition to the oral presentations of accepted papers, there will be three invited speakers:
Aurélie Lozano
Research Staff Member
IBM T.J. Watson Research Center
Title:
A data modeling approach to climate change attribution
Abstract:
Climate modeling and analysis of climate change have largely been based on forward simulation with physical models. We propose here a data centric approach to climate study based solely on the actual observed data. This novel approach utilizes a variety of relevant statistical modeling and machine learning techniques such as spatial-temporal causal modeling and extreme value modeling, and suggests multiple future research directions. We will describe preliminary results using data for North America from CRU, NOAA, NASA, NCDC, and CDIAC, as well as certain technical challenges encountered. It is hoped that this alternative perspective will help uncover new insights, improve aspects of simulation models with known uncertainties, and provide a useful complementary approach to climate study.
Bio:
Aurélie Lozano is a Research Staff Member in the Business Analytics and Mathematical Sciences department at the IBM T.J. Watson Research Center. Her research interests are in machine learning, data mining, and their applications. She received a Ph.D. in Electrical Engineering from Princeton University, where her research focused on statistical learning and wireless sensor networks.
Alessandro Donati
Head of Advanced Mission Concepts and Technologies Office
OPS-HSC Human Spaceflight and Exploration Operations Department
European Space Agency - Darmstadt, Germany
Title:
Space Missions & Sensor Networking: Challenging Scenarios
Abstract:
Sensor networking is a paradigm getting familiar in space missions and services. The talk will provide a panoramic view of examples of challenging missions related to earth environment and to space exploration, where sensor knowledge discovery techniques might become instrumental to fulfill mission objectives.
ESA missions such as GMES (Global Monitoring for Environment and Security) and the series of possible Mars exploration missions will be presented and put in context with the topic of the workshop.
Bio:
Alessandro Donati is the lead of the Advanced Mission Concepts and Technology Office at the European Space Operations Centre of the European Space Agency, ESA. His team's interests are artificial intelligence technology applied to mission operations processes such as planning & scheduling, monitoring and diagnosis, resource management. He received an electronic engineering degree from La Sapienza University of Rome.
Carlos Guestrin
Assistant Professor
Machine Learning and Computer Science Departments
Carnegie Mellon University
Title:
How Optimized Environmental Sensing Helps Address Information Overload on the Web
Abstract:
In this talk, we tackle a fundamental problem that arises when using sensors to monitor the ecological condition of rivers and lakes, the network of pipes that bring water to our taps, or the activities of an elderly individual when sitting on a chair: Where should we place the sensors in order to make effective and robust predictions?
Such sensing problems are typically NP-hard, and in the past, heuristics without theoretical guarantees about the solution quality have often been used. In this talk, we present algorithms which efficiently find provably near-optimal solutions to large, complex sensing problems. Our algorithms are based on the key insight that many important sensing problems exhibit submodularity, an intuitive diminishing returns property: Adding a sensor helps more the fewer sensors we have placed so far. In addition to identifying most informative locations for placing sensors, our algorithms can handle settings, where sensor nodes need to be able to reliably communicate over lossy links, where mobile robots are used for collecting data or where solutions need to be robust against adversaries and sensor failures.
We present results applying our algorithms to several real-world sensing tasks, including environmental monitoring using robotic sensors, activity recognition using a built sensing chair, and a sensor placement competition. We conclude with drawing an interesting connection between sensor placement for water monitoring and addressing the challenges of information overload on the web. As examples of this connection, we address the problem of selecting blogs to read in order to learn about the biggest stories discussed on the web, and personalizing content to turn down the noise in the blogosphere.
Bio:
Carlos Guestrin is the Finmeccanica Assistant Professor in the Machine Learning and in the Computer Science Departments at Carnegie Mellon University. Previously, he was a senior researcher at the Intel Research Lab in Berkeley. Carlos received his PhD in Computer Science from Stanford
University. Carlos' work received awards at a number of conferences and a journal: KDD 2007, IPSN 2005 and 2006, VLDB 2004, NIPS 2003 and 2007, UAI 2005, ICML 2005, JAIR in 2007, and JWRPM in 2009.
He is also a recipient of the ONR Young Investigator Award, NSF Career Award, Alfred P. Sloan Fellowship, and IBM Faculty Fellowship.
Carlos was named one of the 2008 "Brilliant 10" by Popular Science Magazine and received the IJCAI Computers and
Thought Award. He is currently a member of the Information
Sciences and Technology (ISAT) advisory group for DARPA.
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