Adaptation, Learning, and Optimization over Networks
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The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains, such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing ...
We develop a model that successfully learns social and organizational human network structure using ambient sensing data from distributed plug load energy sensors in commercial buildings. A key goal for the design and operation of commercial buildings is t ...
Intelligent Tutoring Systems (ITS) are required to intervene in a learning activity while it is unfolding, to support the learner. To do so, they often rely on performance of a learner, as an approximation for engagement in the learning process. However, i ...
Whether it occurs in artificial or biological substrates, {\it learning} is a {distributed} phenomenon in at least two aspects.
First, meaningful data and experiences are rarely found in one location, hence {\it learners} have a strong incentive to work t ...
Machine learning and deep learning in particular have made a huge impact in many fields of science and engineering. In the last decade, advanced deep learning methods have been developed and applied to remote sensing and geoscientific data problems extensi ...
Recent advances in signal processing, machine learning and deep learning with sparse intrinsic structure of data have paved the path for solving inverse problems in acoustics and audio. The main task of this thesis was to bridge the gap between the powerfu ...
In this article, we show how postphenomenology can be used to analyze a visual method that reveals the hidden dynamics that exist between individuals within large organizations. We make use of the Affinity Map to expand the classic postphenomenology that p ...
By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal–organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many ma ...
The climate and weather are modeled by running computer simulations. In a data-driven approach, scientists tailor the simulation to resemble reality (partly through an understanding of the physical processes, partly through their parameterization). With th ...
Ecological surveys increasingly rely on large‐scale image datasets, typically terabytes of imagery for a single survey. The ability to collect this volume of data allows surveys of unprecedented scale, at the cost of expansive volumes of photo‐interpretati ...
