Antonio Miele is an assistant professor at the Department of Electronics, Information and Bioengineering (DEIB),
Politecnico di Milano, Milano, Italy (according to the Italian Law n. 240/2010 - art. 24, par. 3, letter B).
He received his Ph.D. in Information Technology in 2010 from the same institution where he worked as postdoc research assistant from 2010 to 2014.
During his doctoral studies he spent a 4-month period at European Space Agency - ESTEC in Nordwjik, Netherlands.
Previously, he received the M.Sc. and the B.Sc.
in Computer Science Engineering from Politecnico di Milano in 2006 and in 2003 respectively. In 2006
he also got the M.Sc. in Computer Science at the University of Illinois at Chicago, USA.
His main research interests are related to the definition of design and analysis methodologies for
embedded computing systems, in particular focusing on fault tolerance and reliability issues, runtime resource management in
heterogeneous multi-/many-core systems
and FPGA-based systems design.
Dr. Miele is co-author of more than 70 scientific publications in international
conference proceedings and selected journals. He has served as Program Chair the International Symposium on
Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT) in 2016 and in 2017; moreover, he is the "Adaptive and
Learning Systems" DATE A6 Topic Chair from 2018 to 2021. Dr. Miele has served as Guest co-Editor two special sessions both
focusing on fault tolerance in digital systems, one in the IEEE Transactions in Emerging Topics in Computing (2016-2017 and
published in 2020), and one in the IET Computers & Digital Techniques (2017-2018, published in 2019). Finally, he is part
of the technical program committees of various conferences, such as DATE, DFT, FPL, IOLTS, ARC, DSD.
Dr. Miele actively participated in various national and EU funded project: "SAVE" FP7 STREP project (2013-2016), "SMECY"
EU-ARTEMIS project (2010-2013), "SCALOPES" EU-ARTEMIS project (2009-2010) and a MIUR-PRIN 2008 project (2010-2012). Finally, current activities on the definition of novel hardening strategies for image processing and machine learning applications are partially supported by an Intel Corporation research grant.
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