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 IEEE Systems, Man &
Cybernetics Society Spanish Chapter, 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"
2nd 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:
WCCI/IJCNN
2020 (Glasgow, Scotland, UK) "Explainable
Computational/Artificial Intelligence Methods"
IV
Science4Dialysis Meeting (Hospital de Bellvitge,
Barcelona), November 12th 2020.
Recently organized:
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"
