Joseph Azar

Associate Professor in Computer Science; Junior data scientist; Mobile/Web developer

Hello! I am an enthusiastic researcher in computer science with broad programming, statistics, and analytical skills and I use these skills to solve various business and health-related problems using machine learning, data mining, and other types of data analysis and data visualization tools such as Python, TensorFlow, Keras, etc.
I received my Ph.D. in computer science in 2020 from the "Université de Franche-Comté", France. I received the M.Sc. in Computer Science and Risk Management from the Lebanese University, Faculty of Sciences, in 2017. My current work focuses on health data science, data science for IoT and cybersecurity, and signal processing.
I am currently working as an associate professor in computer science at the University of Franche-Comté.
My goals are to build skills, work, and collaborate in the fields of data science, Internet of Things, and big data.

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  • 2012-2015

    Lebanese University - Faculty of Sciences

    Bachelor's Degree, Computer Science

  • 2015-2016

    Lebanese University - Faculty of Sciences

    Master's Degree, M1 in computer science

  • 2016-2017

    Lebanese University - Faculty of Sciences

    Master's Degree, M2 in computer science and risk management

  • 2017-2020

    University of Franche Comté - Femto-ST lab

    Ph.D. in computer science


Health Data Science

  • Anomaly detection in time series data
  • Analysis of vital signs data (ex: ECG,PPG,...)
  • Application of machine learning and deep learning techniques

IoT and Ubiquitous Computing

  • Use of wearable devices for research on affective computing
  • Data aggregation and fusion in IoT applications

Digital Signal Processing

  • Use of signal processing to address the energy consumption problem in IoT devices
  • Data analysis and compression using the Discrete Wavelet Transform
  • Time-frequency analysis
  • Lossless and lossy data compression


Using DenseNet for IoT multivariate time series classification

Joseph Azar, Abdallah Makhoul, Raphaël Couturier

2020 IEEE Symposium on Computers and Communications (ISCC), 1-6

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Robust IoT Time Series Classification with Data Compression and Deep Learning

J Azar, A Makhoul, R Couturier, J Demerjian


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An energy efficient IoT data compression approach for edge machine learning

J Azar, A Makhoul, M Barhamgi, R Couturier

Future Generation Computer Systems 96, 168-175, 2019

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Using Adaptive Sampling and DWT Lifting Scheme for Efficient Data Reduction in Wireless Body Sensor Networks

J Azar, C Habib, R Darazi, A Makhoul, J Demerjian

14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 2018

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Using DWT Lifting Scheme for Lossless Data Compression in Wireless Body Sensor Networks

J Azar, R Darazi, C Habib, A Makhoul, J Demerjian

14th International Wireless Communications & Mobile Computing Conference (IWCMC), 2018

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On the performance of resource-aware compression techniques for vital signs data in wireless body sensor networks

J Azar, A Makhoul, R Darazi, J Demerjian, R Couturier

IEEE Middle East and North Africa Communications Conference (MENACOMM), 1-6, 2018

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