Κατάλογος Εκδηλώσεων

20
Μαϊ

Ομιλία του Επίκουρου Καθηγητή Χαράλαμπου Τσουρακάκη (Boston University), Δευτέρα 20/5, 13:00, Αμφιθέατρο Κτ. Επιστημών
Κατηγορία: Ομιλία/Διάλεξη   ΗΜΜΥ  
ΤοποθεσίαΛ - Κτίριο Επιστημών/ΗΜΜΥ, 141Π-98,141Θ-97, Αμφιθέατρο Κτιρίου Επιστημών, Πολυτεχνειούπολη
Ώρα20/05/2019 13:00 - 14:00

Περιγραφή:

Ζωντανή μετάδοση από το κανάλι της Σχολής ΗΜΜΥ: https://www.youtube.com/user/ecetuc

Ομιλητής: Χαράλαμπος Τσουρακάκης, Boston University

Title: Dense subgraph discovery in large-scale networks 

Abstract: Finding dense subgraphs in massive networks is a major problem with numerous applications, including anomaly detection in security, community detection in social networks, and mining the Web graph. How can we exploit properties of real-world networks in order to find large near-cliques? Can we find dense subgraphs in graph streams with a single pass over the stream?  Can we design real-time algorithms  for time-evolving networks? How can we find subgraphs in uncertain graphs that induce large weight in expectation and are associated with low risk? In this talk I will present state-of-the-art answers to these questions, and I will conclude with various open research directions.

Bio:  Charalampos Tsourakakis is an assistant professor in computer science at Boston University and a research associate at Harvard. Dr. Tsourakakis obtained his PhD in Algorithms, Combinatorics and Optimization at Carnegie Mellon under the supervision of Alan Frieze, was a postdoctoral fellow at Brown University and Harvard under the supervision of Eli Upfal and Michael Mitzenmacher respectively. Before joining Boston University, he worked as a researcher in the Google Brain team. He has received the 10-year highest impact paper award from IEEE, has won a best paper award in IEEE Data Mining, has delivered three tutorials in the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, and has designed two graph mining libraries for large-scale graph mining, one of which has been officially included in Windows Azure. His research focuses on large-scale graph mining, and machine learning. 

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