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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,NLU, 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, Elements of Artificial Intelligence, Algorithms Design and Analysis, High Performance Big Data Systems, Advanced Database Concepts.
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