Abstract

Abstract: Data science and machine learning demand innovative visualization approaches to handle the ever-increasing size and complexity of data. Yet, this specialization has led to isolated systems that have limited purposes. These monolithic systems take time to build, are hard to maintain, and have limited research purpose long-term. In this talk, I argue that the HCI and visualization research community could foster a more vibrant ecosystem of tools by designing specifications and APIs and building composable systems on top of these declarative interfaces. Composable visualization frameworks can empower data scientists, machine learning engineers, or other end users to quickly create ephemeral data interfaces for their current needs. Specifications are intellectually challenging, yet encourage vibrant ecosystems and more reproducible research.

Bio: Dominik Moritz is on the faculty at Carnegie Mellon University where he co-directs the Data Interaction Group (https://dig.cmu.edu/) at the Human-Computer Interaction Institute. His group’s research develops interactive systems that empower everyone to effectively analyze and communicate data. Dominik also manages the visualization team in Apple’s machine learning organization. His systems (Vega-Lite, Falcon, Draco, Voyager, and others) have won awards at academic venues (e.g. IEEE VIS and CHI ), are widely used in industry, and by the Python and JavaScript data science communities. Dominik got his PhD from the Paul G. Allen School at the University of Washington, where he was advised by Jeff Heer and Bill Howe.