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Bachir KHOUSSA

Data Scientist

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About Me


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✋🏻 I am passionate about data science and making data valuable by translating it into actionable insights.
👨🏻‍💻 During my professional experiences, I had the opportunity to work on several data-driven projects using cutting edge technologies on various types of data (building predictive/clustering models, creating data visualisation reports, storage and processing of data, etc.).
👨🏻‍💻 I participated in the different phases of an IT project life cycle (from design to delivery of the product and support for the client in the realisation of improvements and the correction of incidents).
🚀 I am dynamic, ambitious and passionate about the use of data and new technologies. Technical expertise is an area that attracts me, especially in its aspects of quality and reliability of the delivered products. Currently, I am looking for new technical challenges. I am happy every day to use my knowledge for the benefit of various data projects.

Experience

Upwork.com | Mostaql.com - Freelance

Data Scientist

  • Cyber-bullying detection in Arabic tweets using Deep Learning models (CNN, LSTM, GRU).
  • Image Segmentation (Threshold, K-means, Mean Shift, Mask R-CNN, FCN-16s).
  • Extraction of information from cadastral maps (OCR).
  • Image annotation and manual segmentation of medical images
  • Labeling content of academic papers
  • Technical environment : Python (NLTK, Pandas, Numpy), OCR (tesseract), OpenCV, Scikit-image, PIL, Keras, Scikit-Learn, Git, GitHub, Jupyter Notebook, Google Colab.

Stellantis (ex-Groupe PSA) - Poissy, France

Data Analyst

  • Manufacturing Data Analytics.
  • NLP project : Creation of a diagnostic assistance tool (search engine) for maintenance employees.
  • Data Visualization project : creation of a dashboard for vehicles' defects analysis.
  • Participating in the choice of Power BI architecture used in the Industrial Direction & Supply Chain.
  • Technical environment : Python (NLTK, Pandas, Numpy, PySpark, PyTest, etc.), SQL, Oracle, Microsoft Power BI, Git, GitHub, Jupyter Notebook.

IBISC, University of Paris-Saclay - Evry, France

Data Scientist

Research initiation project (TER) :
Deep learning methods have made significant progress in several areas such as object recognition in images, signal analysis, automated language processing, and so on. Despite their predictive power, deep neural networks are considered as black boxes, making their interpretation difficult. Over the past 20 years, many authors have proposed techniques for extracting rules from a neural network. The objective of this project is to study the different existing methods and to implement an appropriate method for rules extraction from a deep neural network.

  • TER's Best Poster Award.
  • Responsible for the AI Workshop (EvryBio 2018 Scientific Symposium).
  • Technical environment : Python (keras, scikit-learn, pandas, numpy), Jupyter notebook.

University of Mostaganem

Machine Learning and Computer Vision Engineer

Learned features versus engineered features for multi-concepts detection in images.
In addition to the standard low-level descriptors used in image indexing systems, other types of high-level features have emerged and yielded interesting results. This kind of descriptors are extracted either through deep learning approaches or by using detection scores of a set of semantics. As part of this work, a comparative study of the three types of features is carried out in the context of multi concepts detection in images. Experiments on the international standard corpus “Pascal VOC 2012” are conducted for concepts pairs and triplets of concepts detection.

  • Technical environment : Java, OpenCV using C/C++, MSVM, TensorFlow, Caffe, Shell Scripting, Python.

Sonelgaz

Software Developer

Design and realization of an application of management of the computer park for Sonelgaz.

  • Technical environment : Java, MySQL, UML.

Education

University of Paris-Saclay

Sept 2017 - Sept 2019

Master's degree in Design and Intelligence of Software and Systems

Activities and Societies:

University of Mostaganem

Sept 2015 - June 2017

Master's degree in Information Systems Engineering

Activities and Societies:

University of Mostaganem

Sept 2015 - June 2017

Bachelor's degree in Computer Science

Projects

Using semantic context for multiple concepts detection in still images

In this work, we propose two approaches that consider the semantic context for multi-concepts detection in still images. We tested and evaluated our proposal on the international standard corpus Pascal VOC for the detection of concepts pairs and triplets of concepts. Our contributions have shown that context is useful and improves multi-concepts detection in images. The combination of the use of semantic context and deep learning-based features yielded much better results than those of the state of the art. This difference in performance is estimated by a relative gain on mean average precision reaching + 70% for concepts pairs and + 34% for the case of triplets of concepts.

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Skills

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