Daniel Gibert Llauradó

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Dades personals-contacte / Datos personales-contacto / Personal information-contact

Nom/Nombre/Name:

 Daniel Gibert Llauradó

Categoria/Categoría/Category:

Investigador predoctoral / Investigador predoctoral / Predoctoral researcher

Àrea/Área/Area:

Intel·ligència artificial / Inteligencia Artificial / Artificial Intelligence

Departament/Departamento/Department:

Enginyeria Informàtica i Disseny Digital / Ingeniería Informática y Diseño Digital / Computer Engineering and Digital Design

 Centre/Centro/Center:

 Escola Politècnica Superior / Escuela Politécnica Superior/ Polytechnic School

Despaxt/Despacho/Office:

Campus de Cappont. Edifici EPS. Despatx 3.24 / Campus de Cappont. Edificio EPS. Despacho 3.24Campus of Cappont. EPS Building. Office 3.24

daniel.gibert@udl.cat

+34 973 70 32 64

Code ORCID:

https://orcid.org/0000-0002-2448-1297

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Formació acadèmica / Formación académica / Academic training

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  • Engineria Informàtica (UdL)
  • Màster en Intel·ligència Artificial (UPC)
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  • Ingeniería Informática (UdL)
  • Màster en Intel·ligència Artificial (UPC)
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  • Computer Science Engineering (UdL)
  • Master in Artificial Intelligence (UPC) 

 

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Experiència professional / Experiencia profesional / Professional experience

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  • Analista de dades, Blueliv, 2017-2018
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  • Analista de datos, Blueliv, 2017-2018
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  • Data Scientist, Blueliv, 2017-2018

 

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Docència / Docencia / Teaching

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  • Grau en Enginyeria Informàtica
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  • Grado en Ingeniería Informática
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  • Degree in Computer Science

 

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Gestió / Gestión / Management

Recerca / Investigación / Research

Àmbit de recerca / Ambito de investigación / Research area

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  • ntel·ligència artificial, Aprenentatge automàtic
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  • Inteligencia artificial, Aprendizaje automático
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  • Artificial Intelligence, Machine Learning

Activitats de recerca / Actividades de investigación / Research activities

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Participació en projectes d’investigació: TIN2015-71799-C2-2-P

Publications:

GIBERT, D., MATEU, C., PLANES, J., SOLIS, D., & VICENS, R.

(2017, October). Convolutional neural networks for classification of malware assembly code. In Recent Advances in Artificial Intelligence Research and Development: Proceedings of the 20th International Conference of the Catalan Association for Artificial Intelligence, Deltebre, Terres de L'Ebre, Spain, October 25-27, 2017 (Vol. 300, p. 221). IOS Press.

Gibert, D., Mateu, C., Planes, J., & Vicens, R. (2018, April). Classification of malware by using structural entropy on convolutional neural networks. In Thirty-Second AAAI Conference on Artificial Intelligence.

Gibert, D., Mateu, C., Planes, J., & Vicens, R. (2019). Using convolutional neural networks for classification of malware represented as images. Journal of Computer Virology and Hacking Techniques, 15(1), 15-28.

Gibert, D., Mateu, C., & Planes, J. (2018, October). An End-to- End Deep Learning Architecture for Classification of Malware’s Binary Content. In International Conference on Artificial Neural Networks (pp. 383-391). Springer, Cham.

EspLOGO

Participación en projectos de investigación: TIN2015-71799-C2- 2-P

Publications:

GIBERT, D., MATEU, C., PLANES, J., SOLIS, D., & VICENS, R.

(2017, October). Convolutional neural networks for classification of malware assembly code. In Recent Advances in Artificial Intelligence Research and Development: Proceedings of the 20th International Conference of the Catalan Association for Artificial Intelligence, Deltebre, Terres de L'Ebre, Spain, October 25-27, 2017 (Vol. 300, p. 221). IOS Press.

Gibert, D., Mateu, C., Planes, J., & Vicens, R. (2018, April). Classification of malware by using structural entropy on convolutional neural networks. In Thirty-Second AAAI Conference on Artificial Intelligence.

Gibert, D., Mateu, C., Planes, J., & Vicens, R. (2019). Using convolutional neural networks for classification of malware represented as images. Journal of Computer Virology and Hacking Techniques, 15(1), 15-28.

Gibert, D., Mateu, C., & Planes, J. (2018, October). An End-to- End Deep Learning Architecture for Classification of Malware’s Binary Content. In International Conference on Artificial Neural Networks (pp. 383-391). Springer, Cham.

 

EngLOGO

Participation in research projects: TIN2015-71799-C2-2-P

Publications:

GIBERT, D., MATEU, C., PLANES, J., SOLIS, D., & VICENS, R.

(2017, October). Convolutional neural networks for classification of malware assembly code. In Recent Advances in Artificial Intelligence Research and Development: Proceedings of the 20th International Conference of the Catalan Association for Artificial Intelligence, Deltebre, Terres de L'Ebre, Spain, October 25-27, 2017 (Vol. 300, p. 221). IOS Press.

Gibert, D., Mateu, C., Planes, J., & Vicens, R. (2018, April). Classification of malware by using structural entropy on convolutional neural networks. In Thirty-Second AAAI Conference on Artificial Intelligence.

Gibert, D., Mateu, C., Planes, J., & Vicens, R. (2019). Using convolutional neural networks for classification of malware represented as images. Journal of Computer Virology and Hacking Techniques, 15(1), 15-28.

Gibert, D., Mateu, C., & Planes, J. (2018, October). An End-to- End Deep Learning Architecture for Classification of Malware’s Binary Content. In International Conference on Artificial Neural Networks (pp. 383-391). Springer, Cham.

 

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