Machine Learning Engineer - Team per la trasformazione digitale
I received the BS and MS in Computer Science at University of Bari and a Ph.D. degree in Computer Science in May 2011 from the University of Bari, Italy. I defended the thesis "Scalable Algorithms for Relational Frequent Pattern Discovery From Data Streams" with supervisor Professor Donato Malerba. During the Ph.D. period, I spent several months at the Department of Computer Science at the University of Illinois at Urbana Champaign working with the Professor Jiawei Han. During this period I did research on Information Network Analysis with Tim Weninger and produced great research published on top ranked journal and conferences. Got my Ph.D, I spent one year at the the Department of Biomedical Science and Human Oncology as research assistant working on Bioinformatic applied to microarray analysis. From 2012 to 2015, I was research assistant at University of Bari working on Data Stream Mining applied to photovoltaic plants, and in Big Data. In 2013, I worked as Data Scientist at Mer.Mec. group and I founded an Innovative Startup with the goal of applying Machine Learning algorithms to create map that helps in monitoring the quality of city neighborhoods. In 2015, I started a position as Data Scientist in Unicredit Research and Development. I spent almost three years there working on a lot of interesting stuffs ranging from Natural Language Processing, Graph Mining, Big Data, data encryption and High Frequency Trading. Starting from late 2017 I work as Machine Learning Engineer at the Digital Transformation Team. I describe myself as passionate about using data to solve issues, to uncovering actionable insights and to create descriptive/predictive models to help teams and companies become more productive. I consider myself a communicative person that values building strong relationships with colleagues and stakeholders and have the ability to explain complex topics in simple terms. The characteristic that represents me the most is my intellectual curiosity and the desire to improve myself.
Speeches di Fabio Fumarola
How AI can improve the PA: A case study
Nella PA ci sono diverse attivitÃ che potrebbero essere automatizzare utilizzando tecniche di Apprendimento Automatico e Intelligenza Artificiale. Tutto ciÃ² al fine di rendere la PA piÃ¹ efficace e efficiente. In questo talk analizziamo come l'Intelligenza Artificiale puo' essere utilizzata per rendere efficiente la PA. In particolare, presentiamo i risultati di una sperimentazione con Regione Toscana per la classificazione della posta in ingresso.Lingua speech: Italian