Nasr Allah Ahmad

Avenue Marechal Soult · 64100 Bayonne · +33 6 50 57 99 87 ·

Currently studying for a master's degree in Industry 4.0 related to Cloud Computing, Machine Learning, Internet of Things IoT, Big Data, Micro-services, Systems Engineering and Databases

Experienced Software Engineer with a demonstrated history of working in the computer software industry. Skilled in JAVA, Python, PHP, C++, Hibernate, and Spring Framework.

Strong engineering professional with a Master's degree focused in Computer Software Engineering from Université Antonine.



  • Web Application development using the SPRING MVC Framework(Back end and Front End)
    Environnement: JAVA, MySQL, JSP, JSTL, JavaScript, Web Socket and Web Service
  • Relationnal Database conception and integration
  • Integration of Hibernate Search Engine Using LUCENE, a full text search-engine written in Java
  • Android Mobile Application development
  • Conception and integration of a big data architecture for real-time data treatment using components from the Apache foundation to build a project about news classification using non-supervised machine learning algorithm (K-means)
    Environment: Python, Apache NIFI, Apache KAFKA, Apache SPARK, NoSQL DataBase: Cassandra and Redis
  • Integration between UNITY and VUFORIA for creating an augmented reality application for both Android and IOS
Jun 2016 – August 2018


Université de Pau et Pays de l’Adour

Master of Computer Science - INDUSTRY 4.0

Studies in Machine Learning, Cloud Computing, Micro-Services, Business Analytics, Databases, IoT and Systems Engineering

2018 – 2019

Université Antonine, Lebanon

Software Engineer

GPA: 79/100

2013 – 2018


Programming Languages
  • JAVA
  • C#
  • Python
  • C, C++
  • PHP
  • Xml
  • Javascript, JQuery
  • Html5, CSS
  • Assembly
  • PL/SQL
  • .NET
  • Spring
  • Hibernate
  • Apache
  • Apache Spark
  • Apache Kafka
  • Apache NIFI
  • NodeJS
  • MySQL
  • SQL server
  • Oracle
  • Cassandra
  • Redis
  • Netbeans
  • Visual Studio
  • MySQL workbench
  • Android Studio
  • Unity
  • VMware
  • Virtual Box
  • English
  • French
  • Arabic


Apart from being a developer, I enjoy traveling, meeting new people, learning new skills as I already start improving my photography skills from watching online video tutorials and also enrolled in a salsa dance school.

Master I4.0 project

  • Public Transportation Optimization
  • Based on multiple research, public transportation is currently facing a lot of problems that drove it to the loss of attractiveness due to various reasons: the long waits at transfer points, being prevented from boarding due to crowding, the long distance that requires some people to take in order to reach the bus stop and not have the minimum requirements to satisfy disabled people's needs.

    Responsive image

    As mentioned by TCRP, Transit Cooperative Research Program, multiple criteria must be applied in order to eliminate all problems that can affect the public transportation like the spacing between other bus routes and corridors, bus stop sitting requirements, bus stop spacing requirements, passengers per hour, passengers per trip, the maximum number of standees, timed meets, or time to be spent waiting at a transfer point and bus stop consolidation.

    To help respond to these criteria, our idea was born, an embedded system that can track the people waiting at the bus stop, count them, differentiate between an adult and a young person, count passengers with wheelchairs then finally analyze this data to optimize the whole bus trip. The optimization can target many layers, as the data collected can be studied and analyzed differently depending on the machine learning algorithm applied. For example, this real-time data collected can be used to know that a specific bus stop requires more buses to respond to the high demand, that multiple bus stops could be consolidated together which will result in a decrease of the bus running time and by that reducing operating costs, and many other benefits.

    Technically speaking, the system is composed of two parts: the physical part and the logical one. The physical part, which will be embedded in the bus stop is mainly composed of a camera and Raspberry PI and has a role to track the different type of people waiting, count them and only send counters to the logical system due to privacy. In the other part, the logical system will collect these data, analyze them, save the result and rapidly take action.