Topics in machine learning and data privacy

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My research is in the area of machine learning/ data mining, known also as knowledge discovery. I am interested in increasing the capacity of the existing learning systems, and in making them more suitable for using text data. I am also working on responsible ways of using this technology: hence my interest in data privacy.  Computer's ability of using data about individuals to learn new information about them is an important factor in their privacy. My main idea here is to develop tools that will give people control over their data: everyone should be able to decide who can use their information and for what purpose. One way to achieve this is to control the applications software, and allow it to access and process the data only according to the authorizations of individuals who own the information. I propose to use formal methods, i.e. proving program properties, to guarantee that software that claims to respect user authorizations, indeed does so. The second topic of my proposed research are new ways in which documents should be represented so that they can be efficiently categorized by a computer. My approach is to further explore linguistic properties of documents by looking at their structure, terminology, and the meaning of the terms they contain. I am particularly hopeful that this will improve learning performance of specialized categorization tasks, particularly when data is sparse, e.g. in sentence classification. My final area of interest is scalability of relational learning systems: enabling them to learn from large, real-life datasets. To cope with the information overflow phenomenon, we must employ knowledge information extraction and discovery tools. An identified gap in the knowledge discovery research is the scalability of tools, especially the relational techniques. And yet relational data abounds in the socially most relevant applications: systems biology and pharmacology, and social networks. Hence the importance of relational scaling-up research for knowledge discovery.