Abstract: We describe our experience designing and delivering a general education technological fluency course that frames the discussion of computer science and engineering technology (electronics and programming) in the context of sound-art: art that uses sound as its medium. This course is aimed at undergraduate students from a wide variety of backgrounds and is designed to fit into the "Intellectual Explorations" area of a general undergraduate program. The goal is to introduce computer engineering and computational principles to non-CS students through an exploration of sound-art, experimental and electronic music, noise-making circuits, hardware hacking, and circuit bending.
Awards at MICCAI and IEEE VIS 2016
With fall conferences in full swing, the SCI Institute and our alumni had an amazing showing.
Alex Bigelow and SCI alumnus Roni Choudhury, who, along with Jeff Baumes from Kitware received the Visualization in Practice Best Paper Award at the IEEE Visualization Conference for their paper: Resonant Laboratory and Candela: Spreading your Visualization Ideas to the Masses. Chris Johnson was a participant in the panel: Pathways for Theoretical Advances in Visualization, moderated by Min Chen, which won the best panel award at the IEEE Visualization Conference. The other panelists included Georges Grinstein, Jessie Kennedy, Tamara Munzner, and Melanie Tory. Finally, congratulations to SCI Alumnus Julien Tierny, who with Hamish Carr received the Scientific Visualization Best Paper Award at the IEEE Visualization Conference for their paper: Jacobi Fiber Surfaces for Bivariate Reeb Space Computation.
Yong Wan recieved best poster at BioVis for his display of the functionality and application of FluoRender.
Caleb Rottman, who was a finalist for the MICCAI 2016 Young Scientist Award for his paper: Diffeomorphic Density Registration in Thoracic Computed Tomography. Caleb Rottman, Ben Larson, Pouya Sabouri, Amit Sawant, Sarang Joshi. Also at MICCAI, recent SCI Alumnus Miaomiao Zhang (Ph.D. 2015), was another of the finalists for the Young Scientist Award for her paper: Low-Dimensional Statistics of Anatomical Variability Via Compact Representation of Image Deformations. Miaomiao Zhang, William Sandy Wells, Polina Golland.
From Amazon: You have a mound of data sitting in front of you and a suite of computation tools at your disposal. And yet, you're stumped as to how to turn that data into insight. Which part of that data actually matters, and where is this insight hidden?
If you're a data scientist who struggles to navigate the murky space between data and insight, this book will help you think about and reshape data for visual data exploration. It's ideal for relatively new data scientists, who may be computer-knowledgeable and data-knowledgeable, but do not yet know how to create effective, explorable representations of data.
Best Paper: H. Nguyen, P. Rosen, Improved identification of data correlations through correlation coordinate plots, Intl. Conf. on Information Visualization Theory and Applications (IVAPP), 2016.
Best PhD Project: H. Nguyen, P. Rosen, Data Scalable Approach for Identifying Correlation in Large and Muti-Dimensional Data, Intl. Conf. on Information Visualization Theory and Applications Doctoral Consortium (IVAPP), 2016.
Now Available: Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data
S. Durrleman, P.T. Fletcher, G. Gerig, M. Niethammer, X. Pennec (Eds.)
Third International Workshop, STIA 2014, Held in Conjunction with MICCAI 2014, Boston, MA, USA, September 18, 2014, Revised Selected Papers
Now Available: Topological and Statistical Methods for Complex Data
Edited by J. Bennett, F. Vivodtzev, V. Pascucci
Latest peer-reviewed results in a growing research area
Many applications in science and engineering
Important contributions to the fields of mathematics and computer science
This book contains papers presented at the Workshop on the Analysis of Large-scale, High-Dimensional, and Multi-Variate Data Using Topology and Statistics, held in Le Barp, France, June 2013. It features the work of some of the most prominent and recognized leaders in the field who examine challenges as well as detail solutions to the analysis of extreme scale data.