Dimitrios Athanasakis
Awarded VEIV EngD
Computational Statistics and Machine Learning for Proteomic Profile Analysis
Development of computational statistics and machine learning strategies for the selection of relevant patterns within proteomic profiles of infectious disease processes. This will include algorithms for identification of salient features as well as assessment of their statistical significance. Methods that exploit multiple view data will be developed for clustering, manifold identification and classification. The project will aim to develop new machine learning methods tuned to the demands of the application domain, while contributing to the state of the art in analysis of proteomic data