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X-WR-CALNAME;VALUE=TEXT:Eventi DIAG
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DTSTART:20231029T030000
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UID:calendar.27626.field_data.0@www.diag.uniroma1.it
DTSTAMP:20240529T075805Z
CREATED:20240125T095826Z
DESCRIPTION:In ottemperanza ai requisiti previsti dalla procedura valutativ
a ai fini del passaggio a professore di II Fascia SC 09/H1 SSD ING-INF/05
- Dipartimento di Ingegneria Informatica Automatica e Gestionale 'A. Ruber
ti'\, martedì 30 gennaio 2024 alle ore 12:00\, in aula A5 si terrà il semi
nario di Giuseppe Antonio Di Luna che illustrerà le sue attività di ricerc
a svolte e in corso di svolgimento.Il seminario sarà anche trasmesso in mo
dalità telematica su Zoom. Per partecipare da remoto connettersi all'indir
izzo seguente:https://uniroma1.zoom.us/j/82300723224 Titolo: Theory of Dyn
amic Networks and Practice of Binary Analysis Abstract: The talk will pro
vide an overview of Di Luna's recent research activities\, ranging from th
eoretical work in distributed computing to practical applications in compi
ler testing and cybersecurity. The first part of the talk will concentrate
on a series of recent studies concerning the computability of functions i
n anonymous dynamic networks. In dynamic networks\, unlike static networks
\, the topology connecting processes changes continuously and unpredictabl
y. This paradigm models several real-world scenarios\, such as a peer-to-p
eer network of smartphones. A seminal paper presented at STOC 2010 demonst
rated that when processes have unique identifiers\, it is possible to comp
ute any function of the process inputs in time linear to the network size.
However\, the situation is different for anonymous processes without uniq
ue identifiers. This limitation is common in scenarios where privacy is a
concern\, such as COVID-19 tracking apps. In such cases\, all known determ
inistic algorithms for basic\, non-trivial computations\, such as counting
the number of processes or calculating the average of inputs\, have at le
ast quartic complexity in terms of network size. This complexity made dete
rministic computation seem impractical in real-world scenarios. Through a
series of works conducted during my RTD-B at DIAG\, we introduced a new co
mbinatorial data structure\, the history tree (FOCS 2022). This structure
fully captures the dynamics of anonymous networks\, enabling us to develop
definitive linear-time algorithms for computing all computable functions
in anonymous dynamic networks (FOCS 2022\, DISC 2023). These algorithms ha
ve enhanced the state of the art in several areas\, including the well-kno
wn average consensus problem\, showing that deterministic computation in a
nonymous dynamic networks can be done efficiently. In the second part of
the talk\, Dr. Di Luna will discuss his research in practical systems\, fo
cusing on the correctness and completeness of debug information in optimiz
ed binaries (ASPLOS 2021\, ASPLOS 2023) and on techniques using deep neura
l networks for automated binary analysis. Short Bio: Giuseppe Antonio Di
Luna got his Ph.D. from Sapienza University of Rome in 2015. After his Ph.
D. he did a postdoc at the University of Ottawa\, working on fault-toleran
t distributed algorithms\, distributed robotics\, and algorithm design for
programmable particles. In 2018 he started a postdoc at the Aix-Marseille
University\, where he worked on dynamic graphs. He has been a postdoctora
l fellow at Sapienza funded by the AXA fellowship and performing research
on applying NLP techniques to the binary analysis domain. Currently\, is a
n RTD-B at the DIAG department working on theory of distributed computing
and practical aspects of systems.
DTSTART;TZID=Europe/Paris:20240130T120000
DTEND;TZID=Europe/Paris:20240130T120000
LAST-MODIFIED:20240125T101912Z
LOCATION:Aula A5
SUMMARY:Seminario pubblico Giuseppe Antonio Di Luna - \n\n\n \n \n\n
\n\n\nGiuseppe Antonio\n\n\nDi Luna \n\n \n\n \n\n\n\n\n\nProfessore
Associato\n\nstanza: \n\nB215\n\ntelefono: \n\n+39 0677274044 \n\n \n\n
\n\nBiografia: \n\n\n\nhttps://sites.google.com/site/gadiluna/\n\n\n\nL
ink alla biografia\n\nqualifica_rr: \n\nAssociate professors
URL;TYPE=URI:http://www.diag.uniroma1.it/node/27626
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