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DTSTAMP:20260602T005822Z
DESCRIPTION:\n	Title: Distances on and between complex networks\n\n	Abstract:
 \n\n\nDistance plays a pivotal role in statistics. Meanwhile\, recent tech
 nologies and social networks have yielded large complex network data sets\
 , which require customized statistical tools. From a mathematical viewpoin
 t\, these complex networks are graphs with non-trivial structures (in cont
 rast to Erdös-Rényi graphs\, for example). These networks are models of sy
 stemic phenomena and cases where individual-level analyses are insufficien
 t. Such models are not only used in the study of social networks\, but are
  also widely employed in neurology\, biology\, telecommunication and finan
 ce\, among many areas of application. Unfortunately\, however\, distances 
 on graphs are not clearly defined.\n\nI will begin with a general introduc
 tion to complex networks\, through a few illustrative examples. I will the
 n introduce two key (statistical) problems in the analysis of complex netw
 orks\, vertex clustering/community detection and the study of temporal gra
 phs. Both these problems rely on distances.\n\nFollowing this introduction
 \, I will present our work on distances for use in the statistical analyse
 s (unsupervised learning) of these novel complex data sets. I will focus o
 n the tailoring of traditional\, so-called “general purpose” statistical t
 echniques to the specific case of network data\, through the use of these 
 distances. I will also very briefly highlight possible applications for qu
 antum and “quantum-like” computing.\n\nThis presentation is aimed at a bro
 ad mathematical sciences audience. No prior knowledge of graph theory or c
 omplex networks will be assumed.\n\nSpeaker\n\nPierre is a MITACS post-doc
 toral fellow at the University of Toronto\, where he is affiliated with th
 e Data Sciences Institute\, the Mechanical & Industrial Engineering and Ch
 emical Engineering Departments. His work is co-supervised by Prof. Yuri La
 wryshyn of UofT and Prof. Cristian Bravo of the Statistics & Actuarial Sci
 ences Department at the University of Western Ontario.\n\nPierre’s current
  research\, which is partially supported by RBC\, focuses on tailoring tra
 ditional statistical techniques to complex networks\, with a view on finte
 ch applications. In the past\, he has extended traditional significance te
 sts to network science problems such as clustering quality and clusterabil
 ity. Prior to his current job\, Pierre worked as a quantitative analyst in
  the financial industry and in a large research hospital and as a post-doc
 toral researcher in a high performance computing lab.\n\nhttps://mcgill.zo
 om.us/j/83477865796\n\nMeeting ID: 834 7786 5796\n\nPasscode: None\n\n \n
DTSTART:20231013T193000Z
DTEND:20231013T203000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Pierre Miasnikof (University of Toronto)
URL:/mathstat/channels/event/pierre-miasnikof-universi
 ty-toronto-351744
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