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  • Actually Ph.D student, graduated with Master's Degree in Data Science and aspire to work in the field of Deep Learning, Machine Learning(ML), Artificial Intelligence(AI) and Cognitive Science & Engineering with applications involving Speech Recognition, Voice Interaction, and Natural Language Processing(NLP). I am interested in new opportunities in organisations that will allow me to apply machine learning and natural language processing to solve real-world problems in industry.
October 2019 - Current position
ENSI • tunis • Tunisia
  • Conducting literature review and original research to answer the named entity recognition task.
  • Prototyping novel machine learning algorithms and models in Python.
  • Research and implement NLP methods to extract named entity from both english and arabic
  • Research and implement NLP methods to classify toxic comments in social media.
  • Techniques:
  • Word embedding: ELMO, BERT,Word2vec,GLOVE, Fastext
  • Neural networks: RNN, LSTM, Bi-LSTM, GRU,Bi-GRU, CNN
  • probabilistic models: CRF,Naive Bayes classifier, SVM
  • Tools:
  • Python, Pytorch, NumPy, SciKit-Learn, Pandas, Stanford CoreNLP, Spacy, Git,
    TensorFlow, Keras, Matplotlib, TensorFlow
September 2017 - Current position
ENSI, RIADY Laboratory • Tunis • Tunisia
  • Project: ELMO-BERT based deep neural network model for named entity recognition task
  • - Research and implement deep learning model to extract named entity from both english and arabic language using Long Short-Term Memory (LSTM,Bi-LSTM), word embedding techniques(ELMO, BERT) and CRF.
  • Datasets: Conll 2003, ontonotes 5.0, AQMAR, AnerCorpus
  • Techniques: Word embedding(ELMO, BERT), Neural networks( LSTM, Bi-LSTM, GRU,Bi-GRU), probabilistic models( CRF)
  • tools: Python, Pytorch, NumPy, SciKit-Learn, Pandas, Stanford CoreNLP, Spacy, Git, TensorFlow, Keras,
  • Project: Toxic Comments Classification
  • - This problem was a part of a competition on Kaggle where the participants had to suggest the solution for classifying the toxic comments in various categories using natural language processing concepts.
  • Techniques: fastText embeddings trained locally on the competition data, Pretrained embeddings(GLOVE,Word2vec), Neural networks(RNN,CNN), SVM
  • Tools: Python, Java, Numpy, Scikit-Learn, Pandas, Stanford CoreNLP, NLTK, Matplotlib, TensorFlow, Keras, Linux
Ph.D student in Computer science
September 2017 - Current position
National School of Computer Sciences (ENSI) • tunis • Tunisia
  • Research interests:
  • Text classification, Named Entity recognition, Multi-model NLP
Higher Institute of Arts And Multimedia OF MANOUBA (ISAMM) • Tunis • Tunisia
  • Major Courses: Machine Learning, Data Mining, Deep Learning Systems, Advanced Natural Language Processing, Algorithms Design and Analysis, High Performance Big Data Systems
Computer Engineer's degree
September 2012 - June 2015
Polytechnic School • Sousse • Tunisia
Higher Institute of Informatics and Multimedia of Sfax (ISIMS) • Sfax • Tunisia
Scientific baccalaureate
September 2008 - June 2009
Mixed school sidi bouzid (scientific baccalaureate) • Sidi Bouzid • Tunisia
  • Python, R, PL/SQL, JAVA
  • Pandas, NumPy, Seaborn, SciPy, Gensim, Matplotlib, Scikit-learn, ggplot2, RWeka, gmodels,NLTK, NLP,Stanford CoreNLP, Spacy
  • Linear Regression, Logistic Regression, Decision trees, Random forest, Association Rule Mining (Market Basket Analysis), Clustering (K-Means, Hierarchal), Gradient decent, SVM (Support Vector Machines), Deep Learning (CNN, ANN, RNN,LSTM,Bi-LSTM,GRU,Bi-GRU ) using TensorFlow (Keras)
  • Regression models, Dimensionality Reduction
  • Hadoop, Hive, HDFS, MapReduce, Pig, Kafka, Flume, Oozie, Spark
  • MySQL, SQL Server, Oracle, Hadoop/Hbase, Cassandra, DynamoDB, Azure Table Storage
  • Strong analytical, synthesizing, critical thinking and reasoning skills
  • Like writing. Project documentation, specification design, communication via email and messengers is an integral part of daily work
  • Punctual, responsible and reliable
  • Quality obsessed
  • Constant learner
  • Arabic
  • Frensh
  • English
  • New technologies
  • Sport
  • Travel