A Radial Basis Function Approach to Financial Time Series Analysis

Nonlinear multivariate statistical techniques on fast computers offer the potential to capture more of the dynamics of the high dimensional, noisy systems underlying financial markets than traditional models, while making fewer restrictive assumptions. This thesis presents a collection of practical techniques to address important estimation and confidence issues for Radial Basis Function networks arising from such a data driven approach, including efficient methods for parameter estimation and pruning, a pointwise prediction error estimator, and a methodology for controlling the "data mining'' problem. Novel applications in the finance area are described, including customized, adaptive option pricing and stock price prediction.

Segmentation of Brain Tissue from Magnetic Resonance Images

Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. Brain tissue is a particularly complex structure, and its segmentation is an important step for studies in temporal change detection of morphology, as well as for 3D visualization in surgical planning. In this paper, we present a method for segmentation of brain tissue from magnetic resonance images that is a combination of three existing techniques from the Computer Vision literature: EM segmentation, binary morphology, and active contour models. Each of these techniques has been customized for the problem of brain tissue segmentation in a way that the resultant method is more robust than its components. Finally, we present the results of a parallel implementation of this method on IBM's supercomputer Power Visualization System for a database of 20 brain scans each with 256x256x124 voxels and validate those against segmentations generated by neuroanatomy experts.

Prototype of a Configurable Web-Based Assessment System

The MIT Prototype Educational Assessment System provides subjects and courses at MIT with the ability to perform online assessment. The system includes polices to handle harassment and electronic "flaming" while protecting privacy. Within these frameworks, individual courses and subjects can make their own policy decisions about such matters as to when assessments can occur, who can submit assessments, and how anonymous assessments are. By allowing assessment to take place continually and allowing both students and staff to participate, the system can provide a forum for the online discussion of subjects. Even in the case of scheduled assessments, the system can provide advantages over end-of-term assessment, since the scheduled assessments can occur several times during the semester, allowing subjects to identify and adjust those areas that could use improvement. Subjects can also develop customized questionnaires, perhaps in response to previous assessments, to suit their needs.