The phase transition in inhomogeneous random graphs
Random Structures & Algorithms
The Unreasonable Effectiveness of Data
IEEE Intelligent Systems
Communications of the ACM - A Blind Person's Interaction with Technology
Algorithmic and statistical challenges in modern largescale data analysis are the focus of MMDS 2008
ACM SIGKDD Explorations Newsletter
VECPAR'06 Proceedings of the 7th international conference on High performance computing for computational science
| Hi-index | 0.00 |
The 2010 Workshop on Algorithms for Modern Massive Data Sets (MMDS 2010) was held at Stanford University, June 15-18. The goals of MMDS 2010 were (1) to explore novel techniques for modeling and analyzing massive, high-dimensional, and nonlinearly-structured scientific and Internet data sets; and (2) to bring together computer scientists, statisticians, applied mathematicians, and data analysis practitioners to promote cross-fertilization of ideas. MMDS 2010 followed on the heels of two previous MMDS workshops. The first, MMDS 2006, addressed the complementary perspectives brought by the numerical linear algebra and theoretical computer science communities to matrix algorithms in modern informatics applications [1]; and the second, MMDS 2008, explored more generally fundamental algorithmic and statistical challenges in modern large-scale data analysis [2]. The MMDS 2010 program drew well over 200 participants, with 40 talks and 13 poster presentations from a wide spectrum of researchers in modern large-scale data analysis. This included both academic researchers as well as a wide spectrum of industrial practitioners. As with the previous meetings, MMDS 2010 generated intense interdisciplinary interest and was extremely successful, clearly indicating the desire among many research communities to begin to distill out and establish the algorithmic and statistical basis for the analysis of complex large-scale data sets, as well as the desire to move increasingly-sophisticated theoretical ideas to the solution of practical problems.