Data sets with millions of records and thousands of fields are increasingly common in business, engineering, medicine, and the sciences.
With the amount of data doubling every few years the problem of uncovering hidden patterns or extracting useful information from such data sets is becoming an important practical issue.
Research on this topic focuses on key questions such as how can one build useful models which both allow us to make predictions and also aid us to figure out the underlying process of the data generation.
Research projects in our lab use theories and techniques from the intersection of computer science, statistics, and mathematics, including foundational ideas from algorithms, artificial intelligence, multivariate data analysis,
Bayesian estimation, and computational statistics (from statistics), and optimization and probability theory (from mathematics).
Machine learning and pattern recognition, in particular, are central to our research, providing both a sound theoretical basis and a practical framework for developing useful data analysis algorithms.
Research activities in our lab range across areas as different as hospital fraud detection, direct marketing in CRM, oil price prediction, protein function prediction in bioinformatics, etc.
We hope you find our web-site useful and encourage you to explore its contents (publications, courses, seminars, and other information).