Via Marco Biagi 5 – 73100 Lecce, Italy
(+39) 0832 1902411
Alessandro D’Anca is a Scientist at the CMCC Foundation. He earned a degree in Computer Engineering at the University of Lecce in 2006.
From 2008 to 2011, he worked as Junior Computer Systems Analyst for the ComputerVar S.r.l company at the Euro-Mediterranean Center on Climate Change (CMCC) in Lecce. He joined the Scientific Computing and Operations Division (current ASC division) at CMCC in 2011, where he started working in the Scientific Data Management research group. In 2015, he became Head of the Scientific Data Management research group of the ASC Division and was later appointed Division Director in 2018. His research activities focus on high performance computing, distributed and grid computing, in particular on distributed data management, data analytics/mining and high-performance database management. He has been involved in many national and international projects (TESSA, CLIP-C, OFIDIA, MARSOP4, INDIGO-Datacloud, ESIWACE), working on development, project or scientific management and coordination tasks. He is also the author and co-author of several papers on big data analytics for eScience.
ULTIME PUBBLICAZIONI
- High performance computing to support land, climate, and user-oriented services: The HIGHLANDER Data Portal
- End-to-End Workflows for Climate Science: Integrating HPC Simulations, Big Data Processing, and Machine Learning
- PyOphidia: A Python library for High Performance Data Analytics at scale
- OFIDIA2: An Operational Platform for Fire Danger Prevention and Monitoring
- Enabling dynamic and intelligent workflows for HPC, data analytics, and AI convergence
- Towards High Performance Data Analytics for Climate Change
- Towards an Open (Data) Science Analytics-Hub for Reproducible Multi-Model Climate Analysis at Scale
- SeaConditions: A web and mobile service for safer professional and recreational activities in the Mediterranean Sea
- Big Data Analytics on Large-Scale Scientific Datasets in the INDIGO-DataCloud Project
- On the Use of In-Memory Analytics Workflows to Computer eScience Indicators from Large Climate Datasets