The goal of this project is to design and test mathematically well-founded algorithmic and statistical techniques for analyzing large-scale, heterogeneous and noisy data. The proposed research is transformative in its emphasis on rigorous, analytical evaluation of algorithms' performance and statistical measures of output uncertainty, in contrast to the primarily heuristic approaches currently used in data mining and machine learning. Any progress in that direction will have a significant contribution to the reliability and scientific impact of the massive data analysis.
Eli Upfal, Professor of Computer Science, Brown University
VISIT project website
NSF, NIH, corporation gifts