DataCATz is a collaborative project run by a group of friends with overlapping intellectual and professional interests. We started this blog with two aims: provide a reference for problems and possible solutions in our domain of interest and increase our visibility to the wider data analyst community. Each contributing member to this blog comes from a different formal training background e.g. Medicine, Biological Sciences, Engineering, Physics and Chemistry; and have over time converged towards analytics. Please feel free to browse the topics, and leave helpful comments.
Senior Bioinformatician/Data Scientist. After a master in computer engineering from Politecnico di Milano (Italy) and a master in general engineering from Ecole Centrale de Lyon (France), both achieved under the T.I.M.E (Top Industrial Managers for Europe) program, I follewed my passion for human healthcare and obtained a PhD in translational medicine from the Universita’ di Milano Bocconi (Italy) in collaboration with Singapore Immunology Network.
My interests are varied and encompass multiple domains: from python/R coding to databases, from multivariate linear regression and unsupervised multidimensional clustering methods to immunology and healthcare. In my current position I mainly apply my statistical and computational skills to the analysis of transcriptomics (microarrays, RNA-Seq) and single-cell proteomics (CyTOF) datasets. I also have a strong interest in soft skills development and, in particular, in leadership models and strategies to improve productivity.
I am a data scientist with a MSc (with distinction) from University of Leicester and PhD from University of Manchester with extensive experience in modelling big-data using Machine learning, Bayesian statistical and Graph/Network based methods for estimation, decision making, clustering and dimension reduction. My interests include: Bayesian statistics; Graphs and networks; Statistical and Machine Learning; Clustering and Dimension Reduction; Markov Chain Monte Carlo (MCMC) simulations; Biomarker discovery; Bioinformatics & Cheminformatics; Longitudinal Case-Control Studies; and Next Generation Sequencing (NGS) data analysis.
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