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DTSTART:19700927T020000
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CATEGORIES:ECS Seminars
CONTACT:Carlos Soares
DESCRIPTION:Synthetic data generation has gained relevance recently\, parti
 cularly to provide more data for learning models (e.g. with GANs). However
 \, (semi-)synthetic data is also important for an even more important task
 \, which is to improve our understanding of the behavior of ML models and 
 algorithms. In this talk\, Carlos Soares will discuss some of the work und
 er development in this direction at the Center for Responsible AI in Porto
 .\n\nBio: Carlos Soares is an Associate Professor at the Faculty of Engine
 ering of U. Porto\, where he served in several management roles\, includin
 g Subdirector of the Department of Informatics Engineering and Director of
  the Ph.D. Programme in Informatics Engineering. Carlos is also an Externa
 l Advisor for Intelligent Systems at Fraunhofer Portugal AICOS\, a researc
 her at LIACC and a collaborator at LIAAD-INESC TEC. He is a lecturer at th
 e Porto Business School. The focus of his research is on metalearning/auto
 ML but he has a general interest in Data Science. He has participated in 2
 0+ national and international R&ID as well as consulting projects. Carlos 
 regularly collaborates with companies\, including recent projects with Fee
 dzai\, Accenture and InovRetail. He has published/edited several books and
  150+ papers in journals and conferences\, (90+/125+ indexed by ISI/Scopus
 ) and supervised 10+/50+ Ph.D./M.Sc. thesis. Recent participation in the o
 rganization of events\, includes ECML PKDD 2015\, IDA 2016 and Discovery S
 cience 2021 as programme co-chair. In 2009\, he was awarded the Scientific
  Merit and Excellence Award of the Portuguese AI Association.
DTEND;TZID=Pacific/Auckland:20250225T170000
DTSTAMP:20260404T131255Z
DTSTART;TZID=Pacific/Auckland:20250225T160000
LOCATION:Cotton Club\, Cotton 350
ORGANIZER:Carlos Soares
SUMMARY:Carlos Soares - Synthetic Data for a Better Understanding of Machin
 e Learning Models and Algorithms
UID:seminar_ecs1402_20250211162800
URL:
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