I'm a Computer Science Ph.D. candidate at Stanford University, working with Jeff Heer and the Interactive Data Lab. My research seeks to reduce the burden of design for less technical & non-expert audiences, with a focus on visualization and web design.
I also serve as an advisor to Apropose, Inc., a Bay Area startup I co-founded to build data-driven web design tools.
I graduated from UC San Diego, where I worked with Jim Hollan to explore interactions with wall-sized displays. During my time at UCSD's Revelle College, I helped establish an internship program and served as a Senator, a Resident Advisor, and an Orientation Leader.
Papers and Notes
Declarative visualization grammars can accelerate development, facilitate retargeting across platforms, and allow language-level optimizations. However, existing declarative visualization languages are primarily concerned with visual encoding, and rely on imperative event handlers for interactive behaviors. In response, we introduce a model of declarative interaction design for data visualizations. Adopting methods from reactive programming, we model low-level events as composable data streams from which we form higher-level semantic signals. Signals feed predicates and scale inversions, which allow us to generalize interactive selections at the level of item geometry (pixels) into interactive queries over the data domain. Production rules then use these queries to manipulate the visualization’s appearance. To facilitate reuse and sharing, these constructs can be encapsulated as named interactors: standalone, purely declarative specifications of interaction techniques. We assess our model’s feasibility and expressivity by instantiating it with extensions to the Vega visualization grammar. Through a diverse range of examples, we demonstrate coverage over an established taxonomy of visualization interaction techniques.
We present Lyra, an interactive environment for designing customized visualizations without writing code. Using drag-and-drop interactions, designers can bind data to the properties of graphical marks to author expressive visualization designs. Marks can be moved, rotated and resized using handles; relatively positioned using connectors; and parameterized by data fields using property drop zones. Lyra also provides a data pipeline interface for iterative, visual specification of data transformations and layout algorithms. Visualizations created with Lyra are represented as specifications in Vega, a declarative visualization grammar that enables sharing and reuse. We evaluate Lyra’s expressivity and accessibility through diverse examples and studies with journalists and visualization designers. We find that Lyra enables users to rapidly develop customized visualizations, covering a design space comparable to existing programming-based tools.
Data visualization is now a popular medium for journalistic storytelling. However, current visualization tools either lack support for storytelling or require significant technical expertise. Informed by interviews with journalists, we introduce a model of storytelling abstractions that includes state-based scene structure, dynamic annotations and decoupled coordination of multiple visualization components. We instantiate our model in Ellipsis: a system that combines a domain-specific language (DSL) for storytelling with a graphical interface for story authoring. User interactions are automatically translated into statements in the Ellipsis DSL. By enabling storytelling without programming, the Ellipsis interface lowers the threshold for authoring narrative visualizations. We evaluate Ellipsis through example applications and user studies with award-winning journalists. Study participants find Ellipsis to be a valuable prototyping tool that can empower journalists in the creation of interactive narratives.
Advances in data mining and knowledge discovery have transformed the way Web sites are designed. However, while visual presentation is an intrinsic part of the Web, traditional data mining techniques ignore render-time page structures and their attributes. This paper introduces design mining for the Web: using knowledge discovery techniques to understand design demographics, automate design curation, and support data-driven design tools. This idea is manifest in Webzeitgeist, a platform for large-scale design mining comprising a repository of over 100,000 Web pages and 100 million design elements. This paper describes the principles driving design mining, the implementation of the Webzeitgeist architecture, and the new class of data-driven design applications it enables.
Large-scale display walls, and the high-resolution visualizations they support, promise to become ubiquitous. Natural interaction with them, especially in collaborative environments, is increasingly important and yet remains an on-going challenge. Part of the problem is a resolution mismatch between low-resolution input devices and high-resolution display walls. In addition, enabling concurrent use by multiple users is difficult. In this paper, we present an overlay interface element superimposed on wall-display applications to help constrain interaction, focus attention on subsections of a display wall, and facilitate collaborative multi-user workflow.
Posters, Demos, and Technical Reports
CHI 2013 features 30-second "Video Preview" summaries for each of 500+ separate events. The Interactive Schedule uses large display screens and mobile applications to help attendees navigate this wealth of video preview in order to identify events they would like to attend.
Researchers have long envisioned a Semantic Web, where unstructured Web content is replaced by documents with rich semantic annotations. This paper introduces a method for automatically "semantifying" structural page elements: using machine learning to train classifiers that can be applied post-hoc.
We present a platform for large-scale machine learning on Web designs, which consists of a Web crawler and proxy server to store a lossless and immutable snapshot of the Web; a page segmenter that codifes a page's visual layout; and crowdsourced metadata which augments segmentations.
arvindsatya at cs dot stanford dot edu
|Feb 27–Mar 2||NICAR|
|Apr 24–25||OpenVis Conf|
|Jul 14–18||DARPA XDATA|
|Nov 9–14||IEEE VIS|