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UID:20251121T034424EST-3674Vn9uu5@132.216.98.100
DTSTAMP:20251121T084424Z
DESCRIPTION:Statistical inference in networks and genomics\n\n \n\nAbstract
 : From networks to genomics\, large amounts of data are increasingly avail
 able and play critical roles in helping us understand complex systems. Sta
 tistical inference is crucial in discovering the underlying structures pre
 sent in these systems\, whether this concerns the time evolution of a netw
 ork\, an underlying geometric structure\, or reconstructing a DNA sequence
  from partial and noisy information. In this talk I will discuss several f
 undamental detection and estimation problems in these areas.\n\nI will pre
 sent an overview of recent developments in source detection and estimation
  in randomly growing graphs. Can one detect the influence of the initial s
 eed graph? How good are root-finding algorithms? I will also discuss infer
 ence in random geometric graphs\; can one detect and estimate an underlyin
 g high-dimensional geometric structure? Finally\, I will discuss statistic
 al error correction algorithms for DNA sequencing that are motivated by DN
 A storage\, which aims to use synthetic DNA as a high-density\, durable\, 
 and easy-to-manipulate storage medium of digital data.\n
DTSTART:20161207T200000Z
DTEND:20161207T200000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Miklos Racz - Microsoft Research
URL:/mathstat/channels/event/miklos-racz-microsoft-res
 earch-264582
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