High-dimensional multivariate data is commonly encountered nowadays in a variety of disciplines, including genomics, finance and economics, information technology systems, and biomedical engineering. Understanding the structure of and uncovering relationships among variables measured by these data will have crucial impacts in the corresponding scientific areas.
Though some heuristic algorithms and intuitive methods have been designed for and widely applied in both industrial and scientific applications, as of now, our understandings of them are still limited. The advances of random matrix theory provide a tool set for researchers to study behaviors of many practical algorithms. For example, establishing estimation rates of the algorithms of interest is very helpful in understanding when they should be used in practice.
In anticipation of the Center of Mathematical Sciences and Applications’ Big Data Conference (Aug. 18-19), Jun Liu, professor of statistics at Harvard University and a Scientific Board member of CMSA, describes the growing relevance of big data in various fields.
Scott Kominers, associate professor at the Harvard Business School and faculty affiliate at the Center of Mathematical Sciences and Applications, speaks briefly on the upcoming Big Data Conference (Aug. 18-19).
Prof. Lucy Colwell spoke with us about her CMSA Big Data conference talk, “Using evolutionary sequence variation to make inferences about protein structure and function: Modeling with Random Matrix Theory,” and her research on big data.