Anthony TANNOURY

Anthony TANNOURY

Instructor

School of Engineering of Beirut – Chargé de Cours

Faculty of Science – Chargé de Cours

Faculty of Sciences- Branch of Zahle and the Bekaa – Chargé de Cours

+961 (8) 803 325 anthony.tannoury@usj.edu.lb


Education

Degree University Country Year
Using Wireless Multimedia Sensor Networks for 3D scene acquisition and reconstruction Université de Franche-Comté France 2018
MASTERS IN MULTIMEDIA AND NETWORKING Université Antonine (UA) Lebanon 2012

University teaching outside USJ

University teaching outside USJ Country Institution Start date End date
Lebanon

Professional experience outside USJ

Professional experience Organisation Start date End date
TRAINER/AI PROGRAM COORDINATOR ICMPD 01/10/2023
HEAD OF DIGITAL TRANSFORMATION AND TECHNOLOGY CRDP 01/08/2023
CEO/OWNER BTEKUP 01/06/2021

Areas of expertise

Engineering and Technology, Sciences

Research themes

AI
MOBILE
DATA
IOT
WMSN

Publications and communications

Efficient and accurate monitoring of the depth information in a Wireless Multimedia Sensor Network based surveillance

A Tannoury, R Darazi, C Guyeux, A Makhoul

2017 Sensors Networks Smart and Emerging Technologies (SENSET), 1-4    15        2017

Artificial intelligence in music composition

M Alaeddine, A Tannoury

Artificial Intelligence Applications and Innovations: 17th IFIP WG 12.5 …    7          2021

Wireless multimedia sensor network deployment for disparity map calculation

A Tannoury, R Darazi, A Makhoul, C Guyeux

Communications Conference (MENACOMM), IEEE Middle East and North Africa    5          2018

Human pose estimation for physiotherapy following a car accident using depth-wise separable convolutional neural networks.

A Tannoury, EM Choueiri, R Darazi

Advances in transportation studies 59           3          2023

PCT: A platform that promotes and improves collective taxi access.

R Darazi, EM Choueiri, MB Saleh, E Doumith, A Tannoury, A Sekaki, ...

Advances in Transportation Studies 57                      2022

Human Pose Estimation Using Depth-Wise Separable Convolutional Neural Networks