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Two presentations

Kamalika Das: "Using Machine Learning to Study the Effects of Climate on the Amazon Rainforests."

Abstract: The Amazonian forests are a critical component of the global carbon cycle, storing about 100 billion tons of carbon in woody biomass, and accounting for about 15% of global net primary production and 66% of its inter-annual variability. There is growing concern that these forests could succumb to precipitation reduction in a progressively warming climate causing extensive carbon release and feedback to the carbon cycle. Contradicting research, on the other hand, claims that these forests are resilient to extreme climatic events. In this work we describe a unifying machine learning and optimization based approach to model the dependence of vegetation in the Amazon on climatic factors such as rainfall and temperature in order to answer questions about the future of the rainforests. We build a hierarchical regression tree in combination with genetic programming based symbolic regression for quantifying the climate-vegetation dynamics in the Amazon. The discovered equations reveal the true drivers of resilience (or lack thereof) of these rainforests, in the context of changing climate and extreme events.

Kamalika Das: "ASK-the-Expert: Active Learning Based Knowledge Discovery Using the Expert."

Abstract: Often, the manual review of large data sets, either for purposes of labeling unlabeled instances or for classifying meaningful results from uninteresting (but statistically significant) ones is extremely resource intensive, especially in terms of subject matter expert (SME) time. Use of active learning has been shown to diminish this review time significantly. However, since active learning is an iterative process of learning a classifier based on a small number of SME-provided labels at each iteration, the lack of an enabling tool can hinder the process of adoption of these technologies in real-life, in spite of their labor-saving potential. In this talk we present ASK-the-Expert, an interactive tool that allows SMEs to review instances from a data set and provide la- bels within a single framework. ASK-the-Expert is powered by an active learning algorithm for training a support vector machine based classifier in the back-end. Its uniqueness lies in its ability to learn from explanations provided by the expert regarding the anomalousness of the instances reviewed. We demonstrate this system in the context of an aviation safety application, but the tool can be adopted to work in other domains where labels are difficult to obtain.

Bio: Kamalika Das is a senior researcher in the Data Sciences Group in the Intelligent Systems Division at NASA Ames Research Center. She manages the Data Science & Machine Learning task at the NASA University Affiliated Research Center (UARC), University of California at Santa Cruz. She is also the Principal Investigator/Co-Investigator on two NASA-funded projects on machine learning for climate science. Kamalika’s research interests are building machine learning solutions for problems in aviation, climate science, and computational sustainability. In the past, she has lead NASA's efforts on the The Defense Advanced Research Projects Agency (DARPA) funded Anomaly Detection at Multiple Scales (ADAMS) project for anomaly detection in social networks that identifies potentially malicious entities. She has more than 35 peer-reviewed publications in top tier conferences and journals. Kamalika has a Ph.D. in computer science with specialization in developing scalable data mining solutions for analyzing "big data" in distributed computing environments.

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