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UID:629@fds.yale.edu
DTSTART;TZID=America/New_York:20240904T113000
DTEND;TZID=America/New_York:20240904T130000
DTSTAMP:20250916T142136Z
URL:https://fds.yale.edu/events/fds-colloquium-quanquan-liu/
SUMMARY:FDS Colloquium: Quanquan C. Liu\, “Massive Graph Algorithms from 
 Theory to Practice and Back”
DESCRIPTION:"Massive Graph Algorithms from Theory to Practice and Back"\n\n
 \n\nAbstract: In the face of massive graph data\, there is increased inter
 est in developing novel algorithmic foundations for practical graph algori
 thms that are scalable\, efficient\, and private. Algorithm designers face
  many challenges when creating algorithms for real-world deployment. First
 \, modern datasets often reach sizes of hundreds of gigabytes or even tera
 bytes of data. Second\, data is constantly evolving\, and we require accur
 ate temporal statistics despite these changes. Finally\, the widespread us
 e of sensitive user data poses a great threat to user privacy. Theoretical
  models have been developed to model each of these situations including th
 e shared-memory parallel\, massively parallel computation\, dynamic/batch-
 dynamic\, and differential privacy models. This talk will introduce these 
 models\, give example algorithms in these models\, and discuss challenges 
 and potential solutions in translating these algorithms from theory to pra
 ctice and back. \n\n\n\nSpeaker Bio: Quanquan C. Liu is an assistant pr
 ofessor in the Computer Science department at Yale and is a member of the 
 Yale Institute for Foundations of Data Science. Her current interests fea
 ture the theory and practice of algorithms for large data\, graph algorith
 ms\, and various notions of stability\, especially in the connections betw
 een these notions.\n
ATTACH;FMTTYPE=image/jpeg:https://fds.yale.edu/wp-content/uploads/2024/05/
 LIU-Quanquan.jpg
CATEGORIES:FDS Events,Colloquium,Seminar Series
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DTSTART:20240310T030000
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