AI-CFPD is a 3-year project (2018-2021) that aims at facilitating the design of clothing products, particularly in the field of product concept development, by providing personalized proposals to the designer as inspiration, by developing tool based on Artificial Intelligence methods. The tools will work in addition to existing support systems for the development of clothing products such as Life Cycle Management Systems, CAD (Graphics), 3D Modeling systems, commonly used by companies and designers.

The research is moving in two dimensions: The first dimension concerns the perfect transformation of the product design field into a field of action planning that can be supported by Artificial Intelligence methods, providing timely and valid information to the designer drawing information from a wider range of sources than today's method. The second dimension of the research concerns the design and implementation of an intelligent and semi-autonomous decision support system for fashion designers.

Part of my dissertation is to develop a workflow of generative and scalable algorithmic modules, that provides big data analytics services to datasets coming from various scientific fields, including data preprocessing, clustering, graph mining and prediction.

Most of the modules are implemented in R and are available in both script-based format and through a graphical user interface. The graphical modules are implemented in R shiny, an R package that allows to build web applications in R, and are available for demonstration purposes through the following link.

TRIP is a software framework that provides analytics services on antigen receptor (B cell receptor immunoglobulin, BcR IG | T cell receptor, TR) gene sequence data. It is a web application written in R Shiny, an R package that can be used to build interactive web apps straight from R.

The tool takes as input the output files of the IMGT/HighV-Quest tool and processes the data according to user-selected parameters and provides visualization of the results. The analytic processes that it offers include clonotype computation, highly similar clonotype computation, shared clonotype computation, repertoire extraction, repertoire comparison, multiple variable comparison, sequence alignment, somatic hypermutations, and amino acid position-based frequency estimation.

RAPP was a 3-year project (2013-2016) that provided an open source software platform to support the creation and delivery of Robotic Applications (RApps), which, in turn, are expected to increase the versatility and utility of robots. The emphasis of this project was on applications that enable robots to understand and respond to the intentions and needs of people at risk of exclusion, especially the elderly.