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DTSTART:20240310T030000
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DTSTAMP:20240614T015350Z
UID:AEE079A5-43C9-4A69-BF4C-5C15708B26EA
DTSTART;TZID=America/New_York:20240613T190000
DTEND;TZID=America/New_York:20240613T200000
DESCRIPTION:Graphs are ubiquitous data structures providing powerful repres
 entations for objects with interactions. Empowered by recent progress in A
 I and machine learning\, rapid technical progress has been achieved in gra
 ph mining. On the other hand\, research and clinical practices in public h
 ealth have generated large volumes of interconnected data\, where the expl
 oration of modern graph mining principles and techniques is still rather l
 imited. In this talk\, Dr. Yang will introduce their research vision and a
 genda for graph mining for health\, followed by successful examples from t
 heir recent exploration of multi-modality graph construction\, trustworthy
  graph modeling\, and federated graph learning. Finally\, Dr. Yang will co
 nclude the talk with discussions on future directions that can benefit fro
 m further collaborations with researchers interested in data mining or hea
 lth informatics in general.\n\nJoin us for an enlightening session! Let's 
 explore graph data and delve into the latest techniques and their practica
 l applications in healthcare.\n\nSpeaker(s): Carl Yang\n\nVirtual: https:/
 /events.vtools.ieee.org/m/417510
LOCATION:Virtual: https://events.vtools.ieee.org/m/417510
ORGANIZER:lifanghescut@gmail.com
SEQUENCE:37
SUMMARY:Graph Mining for Health
URL;VALUE=URI:https://events.vtools.ieee.org/m/417510
X-ALT-DESC:Description: <br /><p>Graphs are ubiquitous data structures prov
 iding powerful representations for objects with interactions. Empowered by
  recent progress in AI and machine learning\, rapid technical progress has
  been achieved in graph mining. On the other hand\, research and clinical 
 practices in public health have generated large volumes of interconnected 
 data\, where the exploration of modern graph mining principles and techniq
 ues is still rather limited. In this talk\, Dr. Yang will&nbsp\;introduce 
 their research vision and agenda for graph mining for health\, followed by
  successful examples from their recent exploration of multi-modality graph
  construction\, trustworthy graph modeling\, and federated graph learning.
  Finally\, Dr. Yang will conclude the talk with discussions on future dire
 ctions that can benefit from further collaborations with researchers inter
 ested in data mining or health informatics in general.</p>\n<p>Join us for
  an enlightening session! Let's explore graph data and delve into the late
 st techniques and their practical applications in healthcare.</p>\n<p><img
  src="https://www.ontotext.com/wp-content/uploads/2020/02/A-knowledge-grap
 h-structure.png" width="870" height="435"></p>
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