Visus: An Interactive System for Automatic Machine Learning Model Building and Curation

While the demand for machine learning (ML) applications is booming, there is a scarcity of data scientists capable of building such models. Automatic machine learning (AutoML) approaches have been proposed that help with this problem by synthesizing end-to-end ML data processing pipelines. However, these follow a best-effort approach and a user in the loop is necessary to curate and refine the derived pipelines. Since domain experts often have little or no expertise in machine learning, easy-to-use interactive interfaces that guide them throughout the model building process are necessary. In this paper, we present Visus, a system designed to support the model building process and curation of ML data processing pipelines generated by AutoML systems. We describe the framework used to ground our design choices and a usage scenario enabled by Visus. Finally, we discuss the feedback received in user testing sessions with domain experts.Comment: Accepted for publication in the 2019 Workshop on Human-In-the-Loop Data Analytics (HILDA'19), co-located with SIGMOD 201

Visualization and Analysis Tools for Ultrascale Climate Data

Increasingly large climate model simulations are enhancing our understanding of the processes and causes of anthropogenic climate change, thanks to very large public investments in high-performance computing at national and international institutions. Various climate models implement mathematical approximations of nature in different ways, which are often based on differing computational grids. These complex, parallelized coupled system codes combine numerous complex submodels (ocean, atmosphere, land, biosphere, sea ice, land ice, etc.) that represent components of the larger complex climate system