Hi There! I'm Gabriel Jiménez Perera a computer scientist a data scientist a researcher

Resume

About Me

About Me

Resume
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Personal Information

I'm a Data Scientist & researcher based in Granada, Spain.
I have serious passion for new challenges, travelling and adapting to new environments.

  • First Name: Gabriel
  • Last Name: Jiménez Perera
  • Nationality: Spanish
My LinkedIn
  • Address: Granada, Spain
  • Email: gabriel@jimpere.com
  • Spoken Languages: English, Spanish
My Github
Experience
Education
Skills
Experience
Researcher - University of Granada
2020 - currently

Computer vision and robotics researcher

Technology Developer - SDG Ibérica
2019 - 2020

Data Scientist and B2B Developer

Education
Master's Degree - University of Granada
2017 - 2018

Master's Degree in Data Science and Computer Engineering

Engineering Degree - University of Las Palmas de Gran Canaria
2013 - 2017

Degree in Computer Science Engineering - Computing

Skills
Python

R

Pandas

Pytorch

Keras

Scikit Learn

5+

Years Experience

5+

Done Projects

4+

Years Research
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works

my projects

my projects

works
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Contact

get in touch

get in touch

Contact
Email
gabriel@jimpere.com
Address
Granada, Spain
Feel free to drop me a line

If you have any suggestion, project, or even you just want to say Hello, please email me and I will reply you shortly.

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project

Degree Final Dissertation

  • Studies : Degree
  • Date : 24/07/2017
  • Used Technologies : Python, Tensorflow, LSTM, MemN2N, DNC

This project proposes experimenting and comparing three artificial neural network models that have had quite accomplishment in natural language processing: LSTM (Long Short-Term Memory), MemN2N (model proposed by Facebook) and DNC (model proposed by Google). For this task, these optimized models have been adapted to a concrete scope, with the objective of comparing the results of each

Code
project

Master's Degree Final Dissertation

  • Studies : Master's Degree
  • Date : 25/09/2018
  • Used Technologies : R, Python, Matlab, Keras, Tensorflow, Scikit Learn

The present end of master's degree work proposes the use of medical image processing tools for feature extraction of cortical thickness and its subsequent analysis and classification through pattern recognition algorithms. Resampling and feature extraction techniques will be used to compensate the effect of small sample size in statistical validation systems based on boosting, bagging, RFs, SVM, ANNs, etc.

Code