April 26, 2014
To be held in conjunction with SDM 2014
Vipin Kumar
William Norris Professor and Head of the Computer Science and Engineering Department at the University of Minnesota
Vipin Kumar is currently William Norris Professor and Head of Computer Science and Engineering at the University of Minnesota. His research interests include High Performance computing and data mining, and he is currently leading an NSF Expedition project on understanding climate change using data driven approaches. He has authored over 250 research articles, and co-edited or coauthored 10 books including the widely used text book ''Introduction to Parallel Computing", and "Introduction to Data Mining" both published by Addison-Wesley. Kumar co-founded SIAM International Conference on Data Mining and served as a founding co-editor-in-chief of Journal of Statistical Analysis and Data Mining (an official journal of the American Statistical Association). Kumar is a Fellow of the ACM, IEEE and AAAS. He received the Distinguished Alumnus Award from the Indian Institute of Technology (IIT) Roorkee (2013), the Distinguished Alumnus Award from the Computer Science Department, University of Maryland College Park (2009), and IEEE Computer Society's Technical Achievement Award (2005). Kumar's foundational research in data mining and its applications to scientific data was honored by the ACM SIGKDD 2012 Innovation Award, which is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD).
Talk Title: Understanding Global Change: Opportunities and Challenges for Data Driven Research
The world's population is growing steadily and many countries are
simultaneously industrializing, developments that have been ongoing at varying rates for two centuries but have accelerated over the past several decades. These processes are increasingly straining already scarce natural and food resources, which must scale up to keep pace with growing demand. The consequences of such large-scale changes include tremendous stresses on the environment that would be calamitous at the current rate of change if they are not managed sustainably. As a result, scientists are tasked with providing answers to challenging questions such as: What is the effect of urbanization on regional land use and ecology? What is the impact of climate change on global water resources? How does deforestation affect the net carbon balance? How does increased biofuel production impact crop patterns and food availability? Addressing these interconnected, societally-relevant questions requires development of new computational methods that enable monitoring, analysis and understanding of changes in the Earth system, interactions between different
processes, and their impacts on factors such as the carbon cycle, hydrology, air quality, and biodiversity.
This talk will present an overview of research being done in a large interdisciplinary project on the development of novel data driven approaches that take advantage of the wealth of climate and ecosystem data now available from satellite and ground-based sensors, the observational record for atmospheric, oceanic,
and terrestrial processes, and physics-based climate model simulations. These information-rich datasets offer huge potential for monitoring, understanding, and predicting the behavior of the Earth's ecosystem and for advancing the science of global change.
This talk will discuss some of the challenges in analyzing such data sets and our early research results.
Dragos Margineantu
Senior Scientist, Boeing Research & Technology
Dragos Margineantu is a Senior Scientist and a Technical Fellow with Boeing Research & Technology. His interests focus on machine learning algorithms for sensor data, active learning, decision making and learning with experts in the loop, inverse reinforcement learning, anomaly detection, and on testing & validation of decision systems. Dragos is the technical lead of multiple internal machine learning and reasoning research projects, and served as the Boeing investigator for DARPA’s Bootstrapped Learning, LAGR and XData programs. He served as Senior Program Committee member for KDD, AAAI, ICML, served as a reviewer for all major data mining, AI, and machine learning journals, is the current action editor for the Machine Learning journal, and was the organizer ICML 2011. Dragos received his Ph.D. in machine learning from Oregon State University in 2001.
User-in-the-loop Learning and Optimization for Anomalous Action Detection
An increasing number of users, collect transaction data and need scalable tools that assist them in identifying abnormalities. This talk will present an interactive user-in-the-loop approach based on inverse reinforcement learning and linear optimization methods for detecting anomalies and intent in data. We implemented and tested our algorithms on real-world GMTI and AIS sensor data.