Keynote Speakers

Design at Large
Scott Klemmer
Tuesday, September 17

Abstract Designers in many fields rely on examples for inspiration, and examples play an important role in art and design curricula. Online media offer a corpus of examples at a scale and diversity never before seen. Design on the Web is not only large, it’s also “at large.” This scale and diversity enable powerful new opportunities for learning, mining, understanding, and assessing design. My group’s research tools harvest and synthesize examples to empower more people to design interactive systems, learners to acquire new skills, experts to be more creative, and programmers to engage in more design thinking. This research shapes my project-based design teaching, which emphasizes creating diverse alternatives, self-assessment, and using examples to provide design insights and teach abstract principles.

To illustrate these opportunities — and the technical and social challenges — of design that is large and at-large, I’ll focus on two recent projects in my group. The first explores peer assessment of design at a global scale. In 2012, we collaborated with Coursera to launch the first massive-scale class with self and peer assessment. This enabled online students to engage in open-ended design projects. It has also worked surprisingly well, and variants have since been used by more than 80 other massive online classes. The second project, Webzeitgeist, introduces a scalable platform for Web design mining. Applications built with this platform can dynamically curate design galleries, search for design alternatives, retarget content between page designs, and predict the semantic role of page elements from design data. In sharing this work, I’ll try to impart some useful strategies and spark discussion on design research at large.

Bio Scott is an Associate Professor of Cognitive Science and Computer Science & Engineering at the University of California, San Diego. Before joining UCSD, he was an Associate Professor of Computer Science at Stanford University, where he co-directed the Human-Computer Interaction Group and held the Bredt Faculty Scholar development chair. Organizations around the world use his lab's open-source design tools and curricula; several books and popular press articles have covered his research and teaching. He has been awarded the Katayanagi Emerging Leadership Prize, Sloan Fellowship, NSF CAREER award, Microsoft Research New Faculty Fellowship. He has authored and co-authored more than 40 peer-reviewed articles; eight were awarded best paper or honorable mention at the premier HCI conferences (CHI/UIST/CSCW). His former graduate students are leading professors, researchers, founders, social entrepreneurs, and engineers. He has a dual BA in Art-Semiotics and Computer Science from Brown University, Graphic Design work at RISD, and an MS and PhD in Computer Science from UC Berkeley. He serves on the editorial board of TOCHI and HCI, co-chaired the UIST 2011 program, and co-chaired the CHI 2010 systems area. He helped introduce peer assessment to open online education, and taught the first peer-assessed online course.

Explicitness in Language Design
Martin Erwig
Wednesday, September 18

Abstract The look and feel of a language is influenced by which of its concepts are made explicit. Explicit representations support language users in expressing their intent. They can help avoid hidden assumptions and provide manipulability of the exposed representation. On the other hand, explicit representations can lead to bigger and more complicated languages, cause extra burden for language users through notational overhead, and complicate the language design through a proliferation of feature interaction.

In this talk I will give several examples of explicit representations and demonstrate their effect on language expressiveness and opportunities for analyses. I will then address how explicitness can serve as a language design criterion and what it takes to guide language designers in their decisions about making concepts explicit or not.

Bio Martin Erwig is Professor of Computer Science at Oregon State University. After receiving his Ph.D. and Habilitation in Computer Science from the University of Hagen in Germany, he joined Oregon State University in 2000. His research is focused on the analysis and design of languages. He has developed languages in a wide range of areas, including spatial and spatio-temporal databases, graph algorithms, spreadsheets, machine learning, program generation, probabilistic programming, and variability. Some of these efforts have led to successful applications in specific domains such as XML querying, ocean modeling, genome evolution, and causal reasoning. He is the author or co-author of two books and over 120 peer-reviewed publications, for which he received several best paper awards. He is a member of several steering committees and a member of the editorial board for the Journal of Visual Languages and Computing.