James Faghmous (Mount Sinai)
Title: Precision global health: An endeavor at the nexus of Big Data, climate, and health
Abstract: Precision medicine -- combining clinical and genetic information -- has become one of the fastest growing research topics of the past five years. However, ensuring that people and communities live healthy productive lives hinges much more on our understanding of the environment than on genetic information. In this talk, I will highlight the known links between climate change and population health, and how climate change has emerged as the biggest health threat of the 21st century. Furthermore, I will introduce the concept of precision global health and highlight areas where data scientists can be catalysts to solving this momentous challenge.
Yulia Gel (U Texas- Dallas)
Title: How Can Statistics and Data Science Help to Quantify Climate-Induced Risks in Insurance?
Abstract: In the last few years the insurance industry in North America and Europe have experienced a record number of claims due to severe weather. According to the 2013 World Bank study, annual average losses from natural disasters have increased from $50 billion in the 1980s to about $200 billion nowadays. While it is not clear whether the causes of such damages are due to global warming, outdated building standards, non-sustainable city infrastructure and planning, or combination of these factors, the problem becomes increasingly acute and virtually concerns every citizen. Adaptation to such changes requires early recognition of vulnerable areas and the extent of the future risk due to weather and climate factors. Despite the well documented findings on the broad spectrum of weather- and climate-induced risks in the insurance sector, most studies focus pre-dominantly on disaster and catastrophes assessment, and there still exists a relatively limited number of studies, addressing the effect of the so-called ``normal'' extreme weather (i.e., higher frequency, lower individual but high cumulative impact events) on the insurance dynamics. In this talk we discuss utility and limitations of statistical and machine learning procedures to address modelling and forecasting of such weather-related insurance losses and the potential impact of uncertainty quantification on the insurance sector and policy holders.
Suzanne Pierce (U Texas- Austin)
Title: The Research Collaboration Network on Intelligent Systems in the Geosciences - What is it and how can you get involved?
Abstract: Intelligent Systems for Geosciences (IS-GEO) is a new NSF-sponsored research collaboration network. It represents an emerging community of interdisciplinary researchers aiming to create fundamental new capabilities for understanding Earth systems. Collaborative efforts across IS-GEO fields of study offer opportunities to accelerate scientific discovery and understanding. The IS-GEO community has an active membership of approximately 65 researchers and includes researchers from across the US, international members, and an early career committee. Current working groups are open to new participants and are focused on four thematic areas with regular coordination meetings and upcoming sessions at professional conferences. (1) The Sensor-based data Collection and Integration Working group looks at techniques for analyzing and integrating of information from heterogeneous sources, with a possible application for early warning systems. (2) The Geoscience Case Studies Working group is creating benchmark data sets to enable new collaborations between geoscientists and data scientists. (3) The Geo-Simulations Working group is evaluating the state of the art in practices for parametrizations, scales, and model integration. (4) The Education Working group is gathering, organizing and collecting all the materials from the different IS-GEO courses. Innovative IS-GEO applications will help researchers overcome common challenges while will redefining the frontiers of discovery across fields and disciplines. (Visit IS-GEO.org for more information or to sign up for any of the working groups.)
Ranga Raju Vatsavai (NCSU)
Title: Mining Global Earth Observations: Algorithms, Applications, and Challenges
Abstract: Global Earth Observations (GEO) from satellites for more than 40 years have proven to be highly useful in monitoring the Earth for settlement mapping, flood inundation mapping, land use/land cover changes, forest fires, deforestation, and precision agriculture. Advances in sensing technology have led to the development of high-spectral and very high spatial resolution imagery leading to petabytes of image archives. Mining these images allows us to monitor changes at global scales for characterizing vegetation dynamics, understanding complex interactions between food, energy, and water systems, and analyzing settlements and human migration patterns. In this talk, we present novel applications of data mining on global earth observations.
Detailed panel information can be found here.
Chid Apte (IBM)
Biography: Dr. Chid Apte is Director of Mathematical Sciences in the IBM Research Division, at the Thomas J. Watson Research Center in Yorktown Heights, New York. His department's research agenda covers theory and applications of Data Science and Operations Research. In addition to maintaining a basic research agenda in Mathematical Programming & Optimization, Algorithmic Theory, Applied Mathematics, Operations Management, Statistical Modeling & Forecasting, Machine Learning & Predictive Modeling, and Data Mining Systems, his teams are engaged in several solutions initiatives focused on Business Analytics for Enterprise Transformation, Agriculture, Manufacturing Optimization, and High-Performance (Big Data & HPC) Analytics. Chid has over twenty five years experience as a research scientist and technical leader in the data science industry with significant experience in predictive analytics solutions for insurance, finance, retail, manufacturing, and agriculture. Chid has led several projects to develop analytics extensions for IBM's analytics platform and solutions portfolio, and advanced analytics applicatipns for IBM's clients. He received his Ph.D. in Computer Science from Rutgers University, and a B. Tech. in Electrical Engineering from the Indian Institute of Technology (Bombay). He has published extensively in his areas of expertise, and actively involved in organizational aspects of leading data science conferences.
Imme Ebert-Uphoff (Colorado State)
Biography: Imme Ebert-Uphoff is a research faculty member in the Department of Electrical and Computer Engineering at Colorado State University. Her primary research interest is in knowledge discovery from data, primarily applied to geoscience applications. She strongly believes in the importance of building a research community that bridges the fields of data science and geoscience and is heavily involved in many related activities. She serves on the steering committee of the annual Climate Informatics Workshop and on the steering committee of the Research Collaboration Network on Intelligent Systems in the Geosciences (IS-GEO)
James Faghmous (Mount Sinai)
Biography: James Faghmous is computer scientist leading a global health research institute at the Icahn School of Medicine at Mount Sinai. His long-term research goal is to develop data science tools that accelerate scientific discovery and yield actionable insights that cannot be attained using traditional modes of discovery (e.g. experimentation or simulations). He received his Ph.D. in computer science from the University of Minnesota under Prof. Vipin Kumar (ACM SIGKDD 2012 Innovation Award winner and author of "Introduction to Data Mining"), where he was part of a 5-year $10M Expeditions in Computing project to understand climate change from data. His Ph.D. thesis at the intersection of data mining and global climate change received the 2014 Best Dissertation Award in Science and Engineering at the University of Minnesota. While at Minnesota, James mentored over 20 students. His research has been generously funded by the US National Science Foundation (NSF) and the National Institutes of Health (NIH).
Yulia Gel (U Texas- Dallas)
Biography: Yulia R. Gel is professor in the Department of Mathematical Science at the University of Texas at Dallas. Her research interests include statistical foundation of data science, inference for random graphs and complex networks, time series analysis, and spatio-temporal processes, with applications in environmental sciences, biosurveillance, finance and predictive analytics. Yulia earned her Ph.D in mathematics from Saint-Petersburg State University, Russia, followed by a postdoctoral position in statistics at the University of Washington. Prior to joining UT Dallas, she was a faculty member at the Department of Statistics and Actuarial Science of the University of Waterloo, Canada. She also held visiting positions at Johns Hopkins University, University of California, Berkeley, and the Isaac Newton Institute for Mathematical Sciences, Cambridge University, UK. Yulia is a Fellow of the American Statistical Association, and a recipient of the Abdel El-Shaarawi Young Researcher’s Award in environmental statistics.
Joydeep Ghosh (U Texas- Austin)
Biography: Joydeep Ghosh is currently the Schlumberger Centennial Chair Professor of Electrical and Computer Engineering at the University of Texas, Austin. He joined the UT-Austin faculty in 1988 after being educated at IIT Kanpur (B. Tech '83) and The University of Southern California (Ph.D '88). He is the founder-director of IDEAL (Intelligent Data Exploration and Analysis Lab), an IEEE Fellow (2004), and the 2015 recipient of IEEE CS Technical Achievement Award. Dr. Ghosh has taught graduate courses on data mining and web analytics to both UT students and to industry, for over two decades. He was voted as "Best Professor" in the Software Engineering Executive Education Program at UT. Dr. Ghosh's research interests lie primarily in data mining and web mining, predictive modeling / predictive analytics, machine learning approaches such as adaptive multi-learner systems, and their applications to a wide variety of complex real-world problems, including health informatics. He has published more than 400 refereed papers and 50 book chapters, and co-edited over 20 books. His research has been supported by the NSF, Yahoo!, Google, Paypal, ONR, ARO, AFOSR, Intel, IBM, etc. He has received 14 Best Paper Awards over the years, including the 2005 Best Research Paper Award across UT and the 1992 Darlington Award given by the IEEE Circuits and Systems Society for the overall Best Paper in the areas of CAS/CAD.
Anuj Karpatne (UMN)
Biography: Anuj Karpatne is a PhD candidate at the University of Minnesota, where he works with his advisor Prof. Vipin Kumar on an NSF Expeditions in Computing project on "understanding climate change: a data-driven approach." Anuj's research focuses on addressing some of the pertinent challenges in analyzing complex physical data in inter-disciplinary problems. His research has resulted in a system to monitor the dynamics of surface water bodies on a global scale, which was featured as a major highlight in a recent NSF news story. This system is enabling a number of environmental studies on the impact of climate change and/or human actions on water availability. Anuj's system will also be key to providing information about surface area changes in lakes around the world (considered one of the 50 essential climate variables) to inform the climate change mitigation and adaptation efforts of the United Nations Framework Convention on Climate Change (UNFCCC). Anuj has received the Doctoral Dissertation Fellowship and the Informatics Institute Fellowship at the University of Minnesota. He is also a co-author of the second edition of the leading textbook, "Introduction to Data Mining." Before joining the University of Minnesota, Anuj received his bachelor's and master's degrees from the Indian Institute of Technology Delhi.
Suzanne Pierce (U Texas- Austin)
Biography: Dr. Suzanne A Pierce is a Research Scientist at the Texas Advanced Computing Center (TACC) and a Lecturer with the Jackson School of Geosciences at the University of Texas at Austin (UT-Austin). Dr. Pierce has extensive experience in integrated modeling, participatory decision support, and scientific research for groundwater and energy-water-mining case studies. She leads the Dynamic Decision Support team which is housed in the Data and Statistics group at TACC. Her focus is on developing generalized decision support systems, integrated models, and cyberinfrastructure tools for Earth Resources Management. She teaches case-based computational modeling skills and leads a dual exchange field program between Mexico and the US for applications of Intelligent Systems to Geosciences.
Ranga Raju Vatsavai (NCSU)
Biography: Raju is a Chancellor’s Faculty Excellence Program Cluster Associate Professor in Geospatial Analytics in the Department of Computer Science, North Carolina State University (NCSU). He works at the intersection of spatial and temporal big data management, analytics, and high performance computing with applications in the national security, geospatial intelligence, natural resources, climate change, location-based services, and human terrain mapping. As the Associate Director of the Center for Geospatial Analytics (CGA), Raju plays a leadership role in the center’s strategic vision for spatial computing research.