Hello all, my name is Andrew J Hunsucker and I’m a PhD student at Indiana University, focusing on Human Computer Interaction in the Informatics department. You might remember me from my post on Virtual Reality a couple of months ago on this blog.
I’ll be blogging here on various topics, namely: virtual and augmented reality but also about design pedagogy. My main research interests for my PhD are how designers learn how to be designers. But I’m not just interested in what information they gather, I’m also interested in how they change as people over the course of this journey. I’ve been through a design Master’s program myself, and what I saw in myself and classmates was a complicated metamorphosis process by which they transformed into a designer. Continue reading →
In software engineering the “big data” catchphrase refers to in-homogeneous large-scale data that can stem from all software development cycles. Such data can be: source code, software bugs and errors, system logs, commits, issues from backtracking systems, discussion threads from consulting sites (e.g. stackoverflow.com), emails from mailing-lists, as well as developers’ demographic data and characteristics and user requirements and reviews. Software engineering can benefit from the aforementioned data in many ways, but there are several challenges regarding the handling of such data.
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On sept 22, 2015, the Interaccion 2015 International Conference of HCI came to the Universitat Politecnica of Catalonia on Vilanova i la Geltrú (Barcelona, Spain).
Interacción 2015 intl Conferences
More than 150 professionals, researchers and students come to that series of Conferences, known to be in the top of the Spanish HCI Conferences with the sponsoring of SCHI, the ACM and the AIPO organizations. These series of Conferences shows the work of 105 publications of 22 differents countries. Continue reading →
In a previous post I summarized some of the plenary talks from the most recent ICDM held in Atlantic city. In this follow up, I will discuss some of the ideas from sessions.
In the main conference track, there were sessions spanning over many of today’s trending topics in computer science: Big Data, social network mining, clustering, spatio-temporal and multilabel learning, classification, dimensionality reduction, and online and social learning. The approaches and applications varied from session to session and talk to talk, but there was, naturally, an overarching theme of efficiently and effectively working with data.
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