1. What are the key information processing challenges in the next generation of intelligent transportation systems?
2. What are the challenges in making transportation “green”-er and how data mining can help?
3. Why predictive vehicle health monitoring is important and why should data miners care?
4. What are the emerging business models for Green IT that data miners can benefit from?
5. How can data miners help vehicle manufacturers in building better and cleaner vehicles?
6. How can data miners help maintaining and monitoring vehicles after market?
7. What is the current status of the technology and what are the achievable return of investments for this market?
8. What are the projections for the next five years and what can data miners do to help?
9. What are the challenges against large-scale adoption of data mining-based decision support tools for clean vehicles and transportation systems?
10. How can policy makers and funding organizations help?

• Ashok Srivastava, PI Integrated Vehicle Health Management, NASA Ames
• Ramasamy Uthurusamy, General Motors (Retd.)
• Eugene Tierney, US Environmental Protection Agency
• Lisa Amini, IBM

• Hillol Kargupta, Professor, University of Maryland Baltimore County and President, AGNIK, LLC.
  

Ashok Srivastava, PI, Integrated Vehicle Health Management, NASA Ames
Ashok N. Srivastava, Ph.D. is the Principal Investigator for the Integrated Vehicle Health Management research project at NASA. His current research focuses on the development of data mining algorithms for anomaly detection in massive data streams, kernel methods in machine learning, and text mining algorithms. Dr. Srivastava is also the leader of the Intelligent Data Understanding group at NASA Ames Research Center. The group performs research and development of advanced machine learning and data mining algorithms in support of NASA missions. He performs data mining research in a number of areas in aviation safety and application domains such as earth sciences to study global climate processes and astrophysics to help characterize the large-scale structure of the universe. For more information please visit https://dashlink.arc.nasa.gov/member/ashok/

Ramasamy Uthurusamy, General Motors (Retd.)
Dr. Ramasamy Uthurusamy was General Director of Emerging Technologies, Information Systems and Services Division of General Motors Corporation. He received his Ph.D. from Purdue University. Prior to joining General Motors he was with Exxon Production Research Company where he was involved in applied AI research. He has taught at Purdue University and at the University of Idaho. At GM, he led the emerging technologies initiatives in the Global Technology Management Group headed by GM Chief Technology Officer. Currently his research interests and expertise spans four major areas: Knowledge Discovery in Databases and Data Mining (KDD); Artificial Intelligence (AI); Knowledge Management; and Advanced Web Technologies. He assessed, evaluated, piloted, and developed GM specific proof-of-concepts of promising new information technologies as part of his responsibilities. He worked with and leveraged his extensive internal and external contacts in academia, industry, government, and relevant organizations. His professional activities include serving on the editorial board of journals, reviewing technical publications, and serving on conference steering committees. He is a Co-Editor of the book on KDD titled "Advances in Knowledge Discovery and Data Mining" published by MIT/AAAI Press in 1996. He Co-Edited two special issues for the Communications of ACM on data mining. He is the Secretary-Treasurer of the International Joint Conferences on AI (IJCAI). For more information please visit http://www.kd2u.org/NGDM09/samy.txt

Eugene Tierney, US Environmental Protection Agency
Eugene Tierney is a Scientist at United State Environmental Protection Agency (EPA). His research interests include emissions analysis for vehicles and emissions monitoring over wireless networks. He has extensive experience in US EPA programs like SmartWay and MOVES that deal with modeling and analyzing vehicle emissions.

Lisa Amini, IBM
Lisa Amini is a Distinguished Engineer and the first Director of IBM Research's Smarter Cities Technology Center in Dublin, Ireland. Researchers at the Smarter Cities Technology Centre focus on advancing science and technology for intelligent urban and environmental systems, with a current focus on creating analytics, optimizations, and systems for sustainable energy, constrained resources (e.g., municipal water management), transportation, and the underlying city fabric that assimilates and shares data and models for these domains. Previously, Lisa was Senior Manager of the Exploratory Stream Processing Research Group at the IBM TJ Watson Research Center, and was the founding Chief Architect for IBM's InfoSphere Streams product. The Streams product is the result of a Research technology, System S, for which Lisa was also architectural lead from inception. Streams is a software platform for continuous, high throughput, and low latency mining of intelligence from massive amounts of sensor and other machine generated data. She also led her team in formative Smarter Planet/Cities pilots analyzing real-time data for cyber security, manufacturing, telecom, market data analysis, radio astronomy, environmental (water) monitoring, and transportation. She has served on program committees, hosted panels, and presented keynotes and papers in numerous IEEE, ACM and other conferences and workshops, and she has filed 40 and issued 12 patents. Lisa has worked at IBM the areas of stream processing systems and algorithms, distributed and high performance systems, content distribution, multimedia, and networking for over 19 years. She received her PhD degree in Computer Science from Columbia University.

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The KDD-2010 organizing committee invites proposals for panels to be held at the conference. Panel proposals should address emerging, controversial and critical issues in data mining that are likely to have impact on the field and lead to exciting discussions and debates. We encourage panels that involve industry, academic and government participants.

Topics might be any frontier research or frontier applications of Data Mining. For illustrative propose some examples of welcome topics include: Data Mining for: Energy efficiency, Environment and Climate Change, Nanosciences and Nanotechnologies, Transport and Aeronautics, etc. Of course, this is not a restrictive list.

Panel proposals should be no more than four pages long and should include the following:

• Title of the panel
• The topic and issues to be discussed in the panel
• Name, affiliation, and contact information for the panel organizer
• Names and affiliations of up to four panelists (in addition to the panel organizer) who have made a commitment to participate
• List of 10 questions that the panel organizer will ask the panelists
• Brief biography of each participant

• Panel Proposals due: February 26, 2010
• Notification of Acceptance: April 16, 2010

Please send proposals by email in pdf format to Panel chairs Wei Fan and João Gama