The Machine Learning and Applications Lab (MLAL) is specialized in developing machine learning systems that learn from data and improve with experience. We are interested in all aspects of machine learning, but as the Lab's name suggests, our research puts more emphasis on applications of machine learning in different real-life problems. The MLAL research is linked to a broad range of application domains, such as:
Information Filtering: recommender systems, collaborative filtering; hybrid recommendation; recommendations for online forums; contextual advertising.
Bioinformatics: sequence analysis; gene expression and regulatory networks.
Ubiquitous computing: human activity recognition; human behavior monitoring; pervasive healthcare; context-aware computing.
Natural Language Processing: legal text processing; social media text processing; discourse processing; paraphrasing; sentiment analysis and opinion mining.
Pattern recognition: object detection and categorization from still images and videos.
Research results includes methods and software for analyzing genomic data, natural language understanding, text/Web mining, human activity recognition, recommender systems, recognizing objects from still images and videos, just to name o few.