Pervasive Computing and Social Networking: Enhancing user level experience in pervasive smart spaces by utilizing social networking information. The system aims to capture and provide recommendations based on the proximity of users that share the same interests.

Tag Recommendation System for BibSonomy: Creating a K-Partite graph technique for recommendation of tags during bookmarking of resources, whether they are internet bookmarks or bibliographic entries that represent the literature.

Spam Detection in Social Networking: Using machine learning techniques to identify spam in social bookmark systems such as “BibSonomy”. We were motivated to identify the features that define spamming bookmarks. The programming language used was Perl for parsing the dataset files and for evaluation of the system.

An Automated Method for Arabic Text Document Filtering: Automatic filtering of documents by learning the user topic-document associations. We use NLP and Information retrieval techniques to create a feature vector that would represent a document. We then use a classifier such as a Support Vector Machine to learn this model and then provide topic judgments to unseen documents.

Pervasive Open Spaces: Open Spaces is a smart spaces pervasive environment that harnesses the power of scalable systems in terms of available resources. Users requesting resources in the Open Space will neither be bound by their current location, nor their current cluster (dome).

Optimized methodology for Arabic Cross Document Named Entity Normalization: Utilizing a machine learning approach based on an SVM classifier coupled with preprocessing rules for cross-document named entity normalization. The process involves disambiguating different entities with common name mentions and normalizing identical entities with different name mentions.

Bionoculars: Bionoculars is a system that automatically extracts interactions between entities such as proteins, chemicals, and diseases from biomedical text, namely biomedical journal articles and abstracts, with preliminary focus on protein-protein interaction.

Machine Assisted Translation: Machine assisted human translation system or MAHT for short aims to assist the translator by providing him with suggestions for auto-completion relating to the document he is translating. MAHT uses machine translation capabilities and probabilistic models based on the document at hand in order to provide accurate translations to the user.

Information Retrieval on Genomics Data: Classifying documents containing experimental evidence allowing assignment of Gene Ontology Codes. This work is part of TREC 2004 Genomics workshop. All scripts for parsing the dataset was developed using Perl.