Hussein AL-NATSHEH

Hussein AL-NATSHEH

Abu Dhabi, Abu Dhabi Emirate, United Arab Emirates
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About

Driven by my fascination for cutting-edge AI technology and its transformative power…

Experience

  • Beyond Limits Graphic

    Beyond Limits

    Glendale, California, United States

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    United Arab Emirates

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    Lyon Area, France

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    Lyon Area, France

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    Geneva Area, Switzerland

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    Geneva Area, Switzerland

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    Lyon Area, France and Alessandria, Italy

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    Lyon Area, France

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    Amman, Jordan

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    Geneva Area, Switzerland

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    International

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    Amman, Jordan

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    Jordan

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    Jordan

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    Amman, Jordan

Education

  • Université de Lyon Graphic

    Université de Lyon

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    Activities and Societies: PhD Student Representative at the Counsel of Doctoral School of the University of Lyon, France

    Data science with a thesis of using machine learning in text mining and automatic text understanding for information retrieval systems

    L'Ecole Doctorale InfoMaths (University de Lyon)
    ERIC laboratory (https://eric.msh-lse.fr/en/presentation/) (Unité de Recherche des Universités Lyon 2 et Lyon 1)
    CNRS
    A full scholarship by the French government (Regional scientific research fund)

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    A double master degrees (2 universities) of 2-year specialized courses including 6-month practical internship. The program is a collaboration between 6 European universities providing their best courses in Data Mining and Knowledge Management. Out of more than 800 of applicants, only 18 students were carefully selected for 2013-2015.

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    Activities and Societies: IEEE Jordan SAC

    Master Thesis in the field of Machine Learning and Data Mining

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    Activities and Societies: IEEE Student Branch Chairman

    Computer Engineering student
    Chair and co-founder of IEEE student branch

Licenses & Certifications

  • TRIZ-based Systematic Innovation in Business and Technology

    ICG Training and Consulting - Netherlands

    Issued
  • Empretec Graphic

    Empretec

    UNCTAD

Volunteer Experience

  • Program Committee Member

    Arabic Natural Language Processing Workshop (WANLP)

    - Present 4 years 8 months

    Science and Technology

    Committee member of the fifth (2020) and the sixth version (2021)

  • PC Member

    ICNLSP 2021

    - Present 3 years 9 months

    Science and Technology

  • Unihance Graphic

    Board Member

    Unihance

    - Present 4 years 2 months

    Education

    Started as a mentor of the founder, then an advisor of the startup. Currently, an investor and a board member.

  • Program Committee Member

    ACLing 2021

    - Present 4 years 3 months

    Science and Technology

  • Arabic Teacher at Badr (Children School of CCMPG)

    Centre Culturel des Musulmans du Pays de Gex (CCMPG)

    - 5 months

    Education

  • PhD Students Representative

    InfoMaths Doctoral School of the University of Lyon

    - 2 years

    Education

Publications

  • Deep Contextualized Pairwise Semantic Similarity for Arabic Language Questions

    https://arxiv.org

    Question semantic similarity is a challenging and active research problem that is very useful in many NLP applications, such as detecting duplicate questions in community question answering platforms such as Quora. Arabic is considered to be an under-resourced language, has many dialects, and rich in morphology. Combined together, these challenges make identifying semantically similar questions in Arabic even more difficult. In this paper, we introduce a novel approach to tackle this problem…

    Question semantic similarity is a challenging and active research problem that is very useful in many NLP applications, such as detecting duplicate questions in community question answering platforms such as Quora. Arabic is considered to be an under-resourced language, has many dialects, and rich in morphology. Combined together, these challenges make identifying semantically similar questions in Arabic even more difficult. In this paper, we introduce a novel approach to tackle this problem, and test it on two benchmarks; one for Modern Standard Arabic (MSA), and another for the 24 major Arabic dialects. We are able to show that our new system outperforms state-of-the-art approaches by achieving 93% F1-score on the MSA benchmark and 82% on the dialectical one. This is achieved by utilizing contextualized word representations (ELMo embeddings) trained on a text corpus containing MSA and dialectic sentences. This in combination with a pairwise fine-grained similarity layer, helps our question-to-question similarity model to generalize predictions on different dialects while being trained only on question-to-question MSA data

    See publication

  • NSURL-2019 Shared Task 8: Semantic Question Similarity in Arabic

    https://arxiv.org

    Question semantic similarity (Q2Q) is a challenging task that is very useful in many NLP applications, such as detecting duplicate questions and question answering systems. In this paper, we present the results and findings of the shared task (Semantic Question Similarity in Arabic). The task was organized as part of the first workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) The goal of the task is to predict whether two questions are semantically similar or not, even if…

    Question semantic similarity (Q2Q) is a challenging task that is very useful in many NLP applications, such as detecting duplicate questions and question answering systems. In this paper, we present the results and findings of the shared task (Semantic Question Similarity in Arabic). The task was organized as part of the first workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) The goal of the task is to predict whether two questions are semantically similar or not, even if they are phrased differently. A total of 9 teams participated in the task. The datasets created for this task are made publicly available to support further research on Arabic Q2Q.

    See publication

  • Mawdoo3 AI at MADAR Shared Task: Arabic Tweet Dialect Identification

    ACL

    Arabic dialect identification is an inherently complex problem, as Arabic dialect taxonomy is convoluted and aims to dissect a continuous space rather than a discrete one. In this work, we present machine and deep learning approaches to predict 21 fine-grained dialects form a set of given tweets per user. We adopted numerous feature extraction methods most of which showed improvement in the final model, such as word embedding, Tf-idf, and other tweet features. Our results show that a simple…

    Arabic dialect identification is an inherently complex problem, as Arabic dialect taxonomy is convoluted and aims to dissect a continuous space rather than a discrete one. In this work, we present machine and deep learning approaches to predict 21 fine-grained dialects form a set of given tweets per user. We adopted numerous feature extraction methods most of which showed improvement in the final model, such as word embedding, Tf-idf, and other tweet features. Our results show that a simple LinearSVC can outperform any complex deep learning model given a set of curated features. With a relatively complex user voting mechanism, we were able to achieve a Macro-Averaged F1-score of 71.84% on MADAR shared subtask-2. Our best submitted model ranked second out of all participating teams.

    See publication

  • Metadata Enrichment of Multi-disciplinary Digital Library: A Semantic-Based Approach

    Springer International Publishing

    In the scientific digital libraries, some papers from different research communities can be described by community-dependent keywords even if they share a semantically similar topic. Articles that are not tagged with enough keyword variations are poorly indexed in any information retrieval system which limits potentially fruitful exchanges between scientific disciplines. In this paper, we introduce a novel experimentally designed pipeline for multi-label semantic-based tagging developed for…

    In the scientific digital libraries, some papers from different research communities can be described by community-dependent keywords even if they share a semantically similar topic. Articles that are not tagged with enough keyword variations are poorly indexed in any information retrieval system which limits potentially fruitful exchanges between scientific disciplines. In this paper, we introduce a novel experimentally designed pipeline for multi-label semantic-based tagging developed for open-access metadata digital libraries. The approach starts by learning from a standard scientific categorization and a sample of topic tagged articles to find semantically relevant articles and enrich its metadata accordingly. Our proposed pipeline aims to enable researchers reaching articles from various disciplines that tend to use different terminologies. It allows retrieving semantically relevant articles given a limited known variation of search terms. In addition to achieving an accuracy that is higher than an expanded query based method using a topic synonym set extracted from a semantic network, our experiments also show a higher computational scalability versus other comparable techniques. We created a new benchmark extracted from the open-access metadata of a scientific digital library and published it along with the experiment code to allow further research in the topic.

    See publication

  • Étiquetage thématique automatisé de corpus par représentation sémantique

    EGC 2018, vol. RNTI-E-34, pp.323-328

    See publication

  • Semantic Search-by-Examples for Scientific Topic Corpus Expansion in Digital Libraries

    IEEE

    See publication

  • UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise Features

    Association for Computational Linguistics

    See publication

  • Ethnicity sensitive author disambiguation using semi-supervised learning

    arXiv.org

    Other authors
    See publication

  • Commercializing Computational Intelligence Techniques in a Business Intelligence Application

    IEEE

    This paper reports on the commercialization of a business intelligence application deploying computational intelligence techniques. Theoretical foundations are included where appropriate, along with implementation results and comparative benchmarks...

    Other authors
    See publication

  • Performance optimization of adaptive resonance neural networks using genetic algorithms.

    IEEE Xplore Press, Foundations of Computational Intelligence, 2007. FOCI 2007

    We present a hybrid clustering system that is based on the adaptive resonance theory 1 (ART1) artificial neural network (ANN) with a genetic algorithm (GA) optimizer, to improve the ART1 ANN settings. As a case study, we will consider text clustering. The core of our experiments will be the quality of clustering, multi-dimensional domain space of ART1 design parameters has many possible combinations of values that yield high clustering quality. These design parameters are hard to estimate…

    We present a hybrid clustering system that is based on the adaptive resonance theory 1 (ART1) artificial neural network (ANN) with a genetic algorithm (GA) optimizer, to improve the ART1 ANN settings. As a case study, we will consider text clustering. The core of our experiments will be the quality of clustering, multi-dimensional domain space of ART1 design parameters has many possible combinations of values that yield high clustering quality. These design parameters are hard to estimate manually. We proposed GA to find some of these sets. Results show better clustering and simpler quality estimator when compared with the existing techniques. We call this algorithm genetically engineered parameters ART1 or ARTgep

    Other authors
    See publication
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Courses

  • Advanced Business Internship in Technoport / Luxembourg

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  • Advanced Databases

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  • Complex Data Warehousing

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  • Data Processing: Cleaning, feature selection, feature construction

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  • Empretec

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  • Facilitation Skills Workshop by USAID

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  • Intellectual Property Rights (IPR) Management, Licencing and Technology Transfer

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  • Logic and Knowledge Representation

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  • Machine Learning

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  • Methodology and Tools for Research

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  • Mining Complex Data: Text, Image, Web

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  • Modelling Complex Systems in Social Science

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  • Multidimensional Data Analysis

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  • Optimization

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  • Probability and Statistics

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  • Project Management, HRM, Marketing, Sales, Business Planning

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  • Software Methodologies

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  • Symbolic Learning

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  • TRIZ and Systematic Innovation in Business and Technology

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Projects

  • Aramco MetaBrain Industrial LLM

    On-prem fine-tuned LLM on Aramco proprietary documents and a RAG conversational AI applications on top of it

  • Aramco Downstream Global Optimizer

    Data and AI Technology Lead/Executive

  • Beyond Search

    Enterprise data LLM powered semantic search platform. It enables companies to index their proprietary data before using fine tuned LLM for conversational question answering for more trusted and referenced answers

  • Blend Optimizer

    A no-code machine learning and Optimization SaaS products for regulated chemical industries including for example, Lubrication, gasoline blending, non-metalic, and pharmaceutical products.

  • Smart Gates

    A fully dynamic workflow builder for Autonomous Border Control (ABC). It replaces sensor based systems with a machine vision systems that better capture fraudulent cases.

  • Bibliographic Entity Automatic Recognition and Disambiguation (BEARD)

    Other creators
    See project

  • Mowjaz Personalized Arabic Content Recommendation Engine

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    See project

  • Qalam Keyboard (Arabic Grammarly like Mobile Keyboard)

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    See project

  • Salma Arabic Digital Assistant

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    See project

  • Author Name Disambiguation

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    Author name disambiguation based on distances between the citation details of the publications. The data model implements co-reference and co-authorship graphs as well as hierarchical clustering with a dynamic detection of the number of clusters. Experiments and results show superiority over modularity and community graph based clustering.

    Other creators
    • Marcello Benedetti
    • Francisco Andrés Rodríguez Drumond

  • OneCard business Intelligence and high-delivery-rate targeted marketing email program

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    Implementation of a self-service online business intelligence and data mining portal for the marketing and managerial team linked with a targeted email marketing system. The email program provide very high inbox delivery rate following anti-spam best practices. Targeting is based on not only calculated demographics but also behavioral predictive analysis of the online users. The business intelligence service also sends periodic emails listing some important visualized trending reports and KPIs…

    Implementation of a self-service online business intelligence and data mining portal for the marketing and managerial team linked with a targeted email marketing system. The email program provide very high inbox delivery rate following anti-spam best practices. Targeting is based on not only calculated demographics but also behavioral predictive analysis of the online users. The business intelligence service also sends periodic emails listing some important visualized trending reports and KPIs to OneCard.net for better market understanding and business decisions.

    Other creators

  • Personalized Recommender System for ChoozOn Corp (BlueKangaroo.com)

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    Real-time items recommendation for each user based on his profile, similar users and social activities. The system also infers some users interests and utilize that with the user's preferences to predict a list of items he would likely buy.

    Other creators
    See project

  • Cloud based Cross-Selling and Churn Management Solution for FoodCity Supermarkets(Piloting)

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    By analysing the users' spending amounts from each supermarket department, the system predict the possibility of loosing each loyal customer based on his similarity to users who have recently cut their spending from the supermarket.

  • Research-Industry Jordanian Applied Research Professionals Networking Hub Software-as-a-Service Solution for IPCO

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  • Data Warehousing and ERP (LIMS) project implementation of Ministry of Water and Irrigation in Jordan

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  • Job seekers' experience section text mining project for predicting best job title for job matching of bayt.com (Pilot)

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    Job tile suggestion for a given work description that would best match with job seeker keyword searches and their work experience profile

    Other creators

  • Cross-Selling and Customer Retention Data Mining Application for Telecom (AutoVAS)

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    behavioural targeting system built on customer profiler (clustering) engine that predicts relevant potential buyers based on their historical transactions.

    Other creators
    See project

  • Salma AI

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    See project

Honors & Awards

  • Gold Award

    Huawei Developer Competition 5 Countries

    Mowjaz App team ranked first in this competition of best Huawei AppGallary App

  • PhD Scholarship

    CNRS

  • Full Scholarship from Erasmus Mundus (EM DMKM)

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    Full Scholarship from Erasmus Mundus master course in Data Mining and Knowledge Management (EM DMKM) a project funded by the European Union

  • Top 30 Arab Tech Innovators Under 30

    UMEN Magazine

    Named as one of the "Top 30 Arab Tech Innovators Under 30" . Ranked 18 by Fouad Jeryes in UMEN Magazine

  • Semi-Finalist

    MIT Arab Business Plan Competition

  • Top 50 SMEs

    InfoDev, 4th Global Forum Innovation and Technology Entrepreneurship

    Selected among the top 50 SMEs by InfoDev, 4th Global Forum Innovation and Technology Entrepreneurship, Helsinki, Finland

  • Arab GoldenChip Award : Top 3 Best Software for Export

    MENA ICT Week, Bahrain

  • The best Jordanian start-up

    MedVentures Award

    MedVentures Award 2010, Marseille, FRANCE

  • First Award in Arab Technology Business Plan Competition

    Arab Science and Technology Foundaion

    First Award in Arab Technology Business Plan Competition - Seed Stage Category 2008-2009

  • Dedication Award

    First IEEE Middle East Student Branch Congress

  • Third Award

    Queen Rania National Entrepreneurship Competition

  • Best paper award at KESW 2016

    The 7th International Conference on Knowledge Engineering and Semantic Web, Prague, 2016

Languages

  • English

    Full professional proficiency

  • Arabic

    Native or bilingual proficiency

  • French

    Limited working proficiency

Organizations

  • Jodan Engineers Association

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    - Present

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