Alfredo Vellido

I am currently an associate professor (formerly a Ramón y Cajal researcher) at CS / UPC, and part of the SOCO SGR Research Group
and the IDEAI Research Center. Member of the
CIBER-BBN, and the IEEE-CIS Data Mining and Big Data Analytics Technical Committee ,
in which I am Chair of the Task Force on Medical Data Analysis and a member of the Explainable Machine Learning (EXML) Task Force.
Member of the Editorial Boards of PLoS ONE and Neural Processing Letters.

CS academic coordinator at Escola Superior d’Enginyeries Industrial, Aeroespacial i Audiovisual de Terrassa (ESEIAAT)

Recently organized the WSOM+2019 Conference in Barcelona, Spain (June 26-28, 2019).

Recent invited talks: 
1st Industrial Conference on AI and Health (ICAIH 2019
), Milano, Italy, 2019:
 "Cautionary Tales about the Application of AI in Health"
Workshop on the Impact of Artificial Intelligence in Healthcare (UPF, Barcelona, Spain), 2019:
 "Interpretability, explainability, and rigurous validation of AI tools for clinical decison making"

Science for Dialysis Meeting: Artificial Intelligence at Bellvitge University Hospital (Barcelona, Spain), 2018:
"Social beasts: societal challenges of AI and ML in medicine"
(youtube video) 

Currently organizing conference special sessions/workshops
you might want to know about
IJCNN 2021 (virtual event) "Transparent and Explainable Artificial Intelligence (XAI) for Health"

Some past special sessions/workshops
WCCI/IJCNN 2020 (Glasgow, Scotland, UK) "Explainable Computational/Artificial Intelligence Methods"
IJCNN 2019 (Budapest, Hungary) "Explainable Machine Learning
IDEAL 2019 (Manchester, UK)
"Machine Learning in Healthcare"
ESANN 2019 (Bruges, Belgium) "Societal Issues in Machine Learning: When Learning from Data is Not Enough"

ESANN 2018 (Bruges, Belgium) "Deep Learning in Bioinformatics and Medicine",
IWBBIO 2018 (Granada, Spain)
"Interpretable Models in Biomedicine and Bioinformatics"

IJCNN 2017 (Anchorage, Alaska, USA) "Machine Learning for Enhancing Biomedical Data Analysis", and

NIPS 2017 (Long Beach, CA, USA) "Transparent and interpretable Machine Learning in Safety Critical Environments"

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