Current research by members of the Boston University statistics and probability group includes work in the following areas:

• High-dimensional data analysis
• Mathematical finance
• Mathematical physics
• Multiscale methods
• Network analysis
• Non-parametric and semi-parametric estimation
• Point-process modeling
• Self-similar processes
• Statistical learning theory
• Time series analysis

Much of the above research is done in conjunction with various inter-disciplinary applications, including:

• Actuarial Science (Gangophadyay)
• Bioinformatics (Kolaczyk, Kon, Ray)
• Data integration for protein function prediction
• Drug target prediction using network filtering
• Motif finding through SVM-based classifiers
• Statistical methods for vaccine development
• Biomedical Statistics (D'Agostino)
• Longitudinal analysis of cardiovascular data
• Genetic factors in cardiovascular disease
• Physician practice patterns for hypertensive & diabetic patients
• Computer Network Traffic Analysis (Kolaczyk, Taqqu)
• scaling properties
• anomaly detection
• Financial Data Modeling(Gangophadyay, Guasoni, Kardaras, Lyasoff, Taqqu)
• Asset pricing
• GARCH models
• Utility maximization
• Levy models
• Image Processing and Geo-spatial analysis (Kolaczyk)
• Deconvolution and segmentation
• Classification of remotely sensed images
• Regional planning from population data
• Neural Data Modeling (Eden)
• Neural estimation
• Characterization of plasticity
• Tracking learning states