Projects

Research publications

Short-Term Solar Irradiance Forecasting in Streaming with Deep Learning

P Lara-Benítez, M Carranza-García, JM Luna-Romera, JC Riquelme

Neurocomputing

2022 | CURRENTLY UNDER REVIEW

Data streams classification using deep learning under different speeds and drifts

P Lara-Benítez, M Carranza-García, D. Gutiérrez-Avilés, JC Riquelme

Logic Journal of the IGPL

2022 | DOI: 10.1093/jigpal/jzac033

Feature Selection on Spatio-Temporal Data for Solar Irradiance Forecasting

M Carranza-García, P Lara-Benítez, JM Luna-Romera, JC Riquelme

16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021) Advances in Intelligent Systems and Computing, vol 1401, p. 654-664

2021 | DOI: 10.1007/978-3-030-87869-6_62

Evaluation of the Transformer Architecture for Univariate Time Series Forecasting

P Lara-Benítez, L. Gallego-Ledesma, M Carranza-García, JC Riquelme

Advances in Artificial Intelligence. CAEPIA 2021. Lecture Notes in Computer Science, vol 12882, p. 106-115

2021 | DOI: 10.1007/978-3-030-85713-4_11

Enhancing Object Detection in Autonomous Vehicles by Optimizing Anchor Generation and Addressing Class Imbalance

M Carranza-García, P Lara-Benítez, J García-Gutiérrez, JC Riquelme

Neurocomputing vol. 449, p. 229-244

2021 | DOI: 10.1016/j.neucom.2021.04.001

On the performance of one-stage and two-stage object detectors in autonomous vehicles using camera data.

M Carranza-García, Jesús Torres-Mateo, P Lara-Benítez, J García-Gutiérrez

Remote Sensing vol. 13, no 1, p. 89

2020 | DOI: 10.3390/rs13010089

An Experimental Review on Deep Learning Architectures for Time Series Forecasting

P Lara-Benítez, M Carranza-García, JC Riquelme

International Journal of Neural Systems (IJNS)

2020 | DOI: 10.1142/S0129065721300011

Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

P Lara-Benítez, M Carranza-García, JM Luna-Romera, JC Riquelme

Applied Sciences 10 (7), 2322

2020 | DOI: 10.3390/app10072322

On the Performance of Deep Learning Models for Time Series Classification in Streaming

P Lara-Benítez, M Carranza-García, F Martínez-Álvarez, JC Riquelme

15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268

2020 | DOI: 10.1007/978-3-030-57802-2_14

Asynchronous dual-pipeline deep learning framework for online data stream classification

P Lara-Benítez, M Carranza-García, J García-Gutiérrez, JC Riquelme

Integrated Computer-Aided Engineering, 1-19

2020 | DOI: 10.3233/ICA-200617

Personal projects

ADLStream framework

ADLStream is a novel asynchronous dual-pipeline deep learning framework for data stream mining. This system has two separated layers for training and testing that work simultaneously in order to provide quick predictions and perform frequent updates of the model. The dual-layer architecture allows to alleviate the computational cost problem of complex deep learning models, such as convolutional neural networks, for the data streaming context, in which speed is essential.

Contribution to Tensoflow with Echo State Network (ESN) implementation

Echo state networks (ESN) provide an architecture and supervised learning principle for recurrent neural networks (RNNs). The main idea is (i) to drive a random, large, fixed recurrent neural network with the input signal, thereby inducing in each neuron within this "reservoir" network a nonlinear response signal, and (ii) combine a desired output signal by a trainable linear combination of all of these response signals

See more about me

Studies

PhD in Machine learning and data science

University of Seville (Spain) 2019 - 2022

Researching about data science, machine learning and artificial intelligence. Mainly focused on deep learning, time series analysis, data stream mining and object detection.

Master's degree · Software Engineering: Cloud, Data science and IT service management

University of Seville (Spain) 2018 - 2019 9.3/10  |  A

Relevant coursework

Data Engineering 9/10     |  A
Machine Learning Engineering 8.3/10  |  A
Data visualization techniques 9/10     |  A
Big Data 9.5/10  |  A+
Data Science 10/10   |  A+

Bachelor of Science · Computer Science

Middlesex University (UK) 2017 - 2018 (Erasmus+ year)

Relevant coursework

Artificial intelligence              8/10     |  B
Open Source software 8/10     |  B

Bachelor of Engineering · Computer Science - Software Engineering

University of Seville (Spain) 2014 - 2018 8.55/10  |  B

Relevant coursework

Data structures and Algorithms 10/10   |  A+
Statistics 7/10     |  B
Artificial Intelligence 9/10     |  A

Languages

Spanish

Native speaker

English

Advanced - C1

Italian

Basic

Download full academic transcript

See more about me

Work

Quantitative Analyst (AVP)

Bank of America     |     2021 - Present · 1+ years

Artificial Intelligence and Data Science Researcher

University of Seville     |     2018 - 2021 · 3 years

Analyst and developer of business management software

PetMaxi - Spain     |     2019 - 2020 · 6 month

Analyst and developer of patien management information system

BBA Medical Center     |     2017 - 2018 · 1 years

Software developer - Backend and Android app

MSIG Smart Management     |     2017 · 3 months

Awards


Winer of OpenWebinars' prize - HackForGood2017 Sevilla

Think Big, Fundación Telefonica     |     March 2017

Second prize in startup hackathon "HackForGood2017"

Think Big, Fundación Telefonica     |     February 2017

Finalist in GO APP! Seville. Circular economy startup

GO APP! by Google     |     November 2016

Winner first prize in code competition "Everis Codefest Sevilla"

Everis     |     November 2016

See more about me

Contact Me