My dissertation focuses on providing efficient digital access to handwritten document collections. Although research advances in information retrieval has improved the quality of document search to a large extent, such methods are mostly applicable to documents with ASCII text and machine printed document images where the task of automatic word recognition is easier. On the other hand, recognition of unconstrained handwritten documents is difficult due to a large variation in writing styles, document image quality and a large vocabulary which severely degrades the performance of information retrieval techniques .

My approach to this problem is three pronged which derives solutions from machine learning, computer vision and information retrieval. The motivation is to apply learning models which can represent uncertainties associated with noisy text. The other guiding factor is to use as much information as possible while determining the relevance of document images with respect to an input query. In our case, the new sources of information can be found in the document image characteristics or meta-descriptors (e.g. authorship, document class) which are robust to the OCR errors. Finally, an aggregation of all the information extracted at various levels would aim to discount the errors introduced by the recognition phase and provide a robust, federated view of the data for efficient document search.

Contact Information

E-Mail: EMAIL OBFUSCATED

Phone: 7165105752

Contact Information

E-Mail: EMAIL OBFUSCATED

Phone: 269 267 1724

Recently, I finished my PhD studies in Computer Science at the University of Vienna, Austria– my PhD thesis focused on efficient feature reduction techniques and their relationship to the resulting classification accuracy. Now I plan to gather experience at an international university or research organization where I can develop and gain my skills, and I am seeking a position where I can bring in my knowledge and motivation in a
new team.

Research interests

In the last years my research activities focused on machine learning and data mining applications in various fields. Besides text-mining and e-mail classification I have worked on classification algorithms, feature subset selection and dimensionality reduction in the field of QSAR-modeling and drug discovery. Currently, I am working on initialization techniques as well as parallelization and
distribution of different low-rank approximation techniques (NMF, SVD, PCA, etc). Moreover, I am interested in high performance computing aspects of these algorithms.

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E-Mail: EMAIL OBFUSCATED

Phone: 0043 1 4277 39674

My research statement is available at http://ragibhasan.com/jobapp/research-statement.pdf

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E-Mail: EMAIL OBFUSCATED

Phone: 217-721-4413

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E-Mail: EMAIL OBFUSCATED

Phone: 510-861-3716

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E-Mail: EMAIL OBFUSCATED

Phone: 6142189084

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E-Mail: EMAIL OBFUSCATED

I expect to graduate this summer (August 2010) and am looking for a postdoc or permanent position starting in September 2010. My long-term goal is to become a tenured professor at a research university that values teaching. Please consult my web page for my CV and/or copies of my publications. For all other inquiries, including requests for research and teaching statements, please send me an email.

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E-Mail: EMAIL OBFUSCATED

Phone: (603)-738-2154

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E-Mail: EMAIL OBFUSCATED

My publication list can be found in my personal web page.

Please check my LinkedIn profile for update information.

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E-Mail: EMAIL OBFUSCATED