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Jose Luis
Jose Luis
 
Fernandez Marquez
Fernandez Marquez

Visiting Scientist
Visiting Scientist


Jose Luis
Jose Luis
 
Fernandez Marquez
Fernandez Marquez
Visiting Scientist
Visiting Scientist

Learning Systems
Learning Systems
(+34) 93 580 9570 ext.
206
206
JoseLuisFernandez@unige.ch
JoseLuisFernandez@unige.ch
JoseLuisFernandez@unige.ch
JoseLuisFernandez@unige.ch
Research areas:
  • Citizen Science
  • Deep Learning
  • Machine Learning
  • Probabilistic Graphical Models
  • Probabilistic Learning
  • Citizen Science
  • Deep Learning
  • Machine Learning
  • Probabilistic Graphical Models
  • Probabilistic Learning
Impact areas:
  • Smart Cities
  • Social Networking
  • Healthcare
  • Smart Cities
  • Social Networking
  • Healthcare
SDGs:
  • GOAL 11: Sustainable Cities and Communities
  • GOAL 13: Climate Action
  • SDGs: General
  • GOAL 03: Good Health and Well-being
  • GOAL 16: Peace, Justice and Strong Institutions
  • GOAL 06: Clean Water and Sanitation
  • GOAL 11: Sustainable Cities and Communities
  • GOAL 13: Climate Action
  • SDGs: General
  • GOAL 03: Good Health and Well-being
  • GOAL 16: Peace, Justice and Strong Institutions
  • GOAL 06: Clean Water and Sanitation
2024
Hafiz Budi Firmansyah,  Jose Luis Fernandez Marquez,  Oguz Mulayim,  Jorge Gomes,  & Valerio Lorini (2024). Accelerating Crisis Response: Automated Image Classification for Geolocating Social Media Content. Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 77–81). Association for Computing Machinery. https://doi.org/10.1145/3625007.3627831. [BibTeX]  [PDF]
2023
Carlo Bono,  Oguz Mulayim,  Cinzia Cappiello,  Mark James Carman,  Jesus Cerquides,  Jose Luis Fernandez Marquez,  Maria Rosa Mondardini,  Edoardo Ramalli,  & Barbara Pernici (2023). A Citizen Science Approach for Analyzing Social Media With Crowdsourcing. IEEE Access, 11, 15329-15347. https://doi.org/10.1109/ACCESS.2023.3243791. [BibTeX]  [PDF]
Hafiz Budi Firmansyah,  Jose Luis Fernandez-Marquez,  Jesus Cerquides,  Valerio Lorini,  Carlo Alberto Bono,  & Barbara Pernici (2023). Enhancing Disaster Response with Automated Text Information Extraction from Social Media Images. 2023 IEEE Ninth International Conference on Big Data Computing Service and Applications (BigDataService) (pp. 71--78). [BibTeX]
Rocco Ballester,  Yanis Labeyrie,  Oguz Mulayim,  Jose Luis Fernandez Marquez,  & Jesus Cerquides (2023). Mathematical and Computational Models for Crowdsourced Geolocation. Ismael Sanz, Raquel Ros, & Jordi Nin (Eds.), Frontiers in Artificial Intelligence and Applications, Vol. 375: Artificial Intelligence Research and Development (pp 301--310). IOS Press. https://doi.org/10.3233/FAIA230699. [BibTeX]  [PDF]
2022
Fabio Murgese,  Gerard Alcaina,  Oguz Mulayim,  Jesus Cerquides,  & Jose Luis Fernandez Marquez (2022). Automatic Outdoor Image Geolocation with Focal Modulation Networks. Atia Cortés, Francisco Grimaldo, & Tommaso Flaminio (Eds.), Frontiers in Artificial Intelligence and Applications, Vol. 356: Artificial Intelligence Research and Development (pp 279--288). IOS Press. https://doi.org/10.3233/FAIA220349. [BibTeX]  [PDF]
Borja Velasco,  Jose L Fernandez-Marquez,  Nerea Luqui,  Jesus Cerquides,  Josep Lluis Arcos,  Analia Fukelman,  & Josep Perelló (2022). Is the phase of the menstrual cycle relevant when getting the covid-19 vaccine?. American Journal of Obstetrics & Gynecology. [BibTeX]  [PDF]
Carlo Bono,  Barbara Pernici,  Jose Luis Fernandez Marquez,  Amudha Ravi Shankar,  Oguz Mulayim,  & Edoardo Nemni (2022). TriggerCit: Early Flood Alerting using Twitter and Geolocation - a comparison with alternative sources. Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 674--686). [BibTeX]  [PDF]
2021
Jesus Cerquides,  Oguz Mulayim,  Jeronimo Hernandez-Gonzalez,  Amudha Ravi Shankar,  & Jose Luis Fernandez Marquez (2021). A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data. Mathematics, 9. https://doi.org/10.3390/math9080875. [BibTeX]  [PDF]
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