In recent years, the interest given to disease biomarkers has boomed. Biotechnology and pharmaceutical companies are exploring ways to use biomarkers to speed up the drug development process, as well as to rapidly assess diseases state, staging, progression and response to therapy. Multiple reaction monitoring (MRM) Mass Spectrometry (MS) has been shown to be well suited for the selective and sensitive quantification of proteins in plasma and has recently emerged as the technology of choice for disease biomarker study.
Together, the students and their professor developed a system to visualize the evolution of a software program from its first inception to the latest edition. The system provides useful information to software engineers and designers as they continually advance computer software packages to be faster and more user-friendly for new computer operating systems.
The project will develop methods for constructing profiles and predictive models for geo-spatial data. The socio-demographic profile will be developed for the geographical areas of interest. In addition, the models will be developed to identify areas with high potential; for acquiring new customers. The targeting of these areas may be conducted through unaddressed direct mail. Methods of testing effectiveness of marketing campaign and predictive models will be developed.
The classification of acoustic signals whose factors of variation are due to different atmospheric and sound propagation effect is a challenging problem. The internship will explore new learning algorithms for this application, which have the potential to capture some of the complex structure in the data.
The overall objective is to design a learning system that takes a training set of acoustic signals and produces a classifier that can identify the category of the acoustic signal, out of a small number of categories. We have already found that it is important to consider the factors of variation that can influence the signal, and the proposed project aims at exploiting physical modeling knowledge to structure a generative model of the acoustic production of the observed signals.