MOHAMMAD ALBARHAM

Bachelor in Mechatronics Engineering

September 2016 - June 2022

The University of Jordan, Amman, Jordan

• Core Subjects: Robotics, Artificial Intelligence, Control, Automation, FPGA, Embedded Systems, and Mechatronics System Design
• ABET accredited program (Accreditation Board for Engineering and Technology).
• CGPA: 3.94/4.00
• Rank: 1st

MACHiNE LEARNiNG SCiENTiST

December 2022 - August 2024

Research Institute of Sciences and Engineering (RISE) -University of Sharjah, Sharjah, UAE

• Responsible for working in NLP and CV projects.
• Developing a chatbot for the Arabic language and integrating it into other applications.
• Developing Arabic Sign Language projects using image classification and object detection.
• Deploying the models to be used in production "Prototypes and Demos".
• I was under the supervision of Ashraf Elnagar and Imad Afyouni.

Product Owner

January 2022 - November 2022

Appswave Information Technology, Amman, Jordan

• Responsible for communicating with stakeholders across the board.
• Serving as a liaison between product and development.
• Maximizing the value of the products.
• Managing and prioritizing the product backlog.
• Managing the repositories in Bitbucket.

AI Engineer and Co-founder

February 2022 - July 2022

Kenda AI

• Co-founded Kenda AI and contributed to building AI-powered solutions focused on Arabic language learning and handwriting recognition using Computer Vision. Supported the development and deployment of AI models, ensuring smooth integration into production systems.

Data Science Intern

September 2020 - December 2020

Xina AI, Amman, Jordan

• Mastered the fundamentals of Artificial Intelligence.
• Used the modern Deep Learning framework "PyTorch" to build multi-layer neural networks, and analyze real data.
• Learned how to build Convolutional Networks and use them to classify images
• Learned the basics of NLP (Natural Language Processing) and how to build Recurrent Networks and use them to perform sentiment analysis.
• Learned how to annotate data for use in ASR (Automatic Speech Recognition), NER (Named Entity Recognition), POS (Part of Speech Tagging), and more
• Built NLU (Natural Language Understanding) and Rule-Based chatbots.

Embedded Systems Intern

May 2019 - July 2019

Nano Lab - German Jordanian University, Madaba, Jordan

• I have worked for two-months at Nano-Lab in GJU (German Jordanian University) in the programming of Arm microcontroller using C-embedded language for the spinning system device that is used to check the type of blood.
• That device is a semi-automated blood grouping apparatus where the blood sample is loaded into a microfluidic disc with three chambers containing anti-A, anti-B, and anti- D antibodies separately. It moves the blood sample through microfluidic channels via centrifugal force into the inspection chambers where a reaction between the pre-filled antibodies and samples antigens (if exists) would occur. Overover, a mounted camera captures the agglutination reaction and saves the results.

TRAiNER - Arduino Course

May 2019 - July 2019

Innovation for Creativity Development Association, Amman, Jordan

• I have worked on training the electronics and microcontrollers practical lectures for the students 13-19 years with Albaqa's association for Humanitarian helping funded by UNICEF. At the of training, we used Arduino microcotroller to create a practical applications..

Applied Researcher

February 2019 - February 2020

Phi Science Institute, Amman, Jordan

• Phi Applied and Innovative Research program (PAIR) is a program that integrates ap- plied scientific research with innovation and entrepreneurship..
• I was selected as one of the 10% innovators among all candidates for this program to work with diverse majors for applied projects that fit the market's need. It is consists of four phases: Problem Definition phase, Ideation and Evaluation phase, Business and Marketing phase and Prototype phase. Also, we worked as a group of multidisciplinary bachelor students, engineers, medical majors and others.

AraCLIP: Cross-Lingual Learning for Effective Arabic Image Retrieval

2024

Muhammad Al-Barham , Imad Afyouni, Khalid Almubarak, Ashraf Elnagar, Ayad Turky, Ibrahim Hashem

Paper Huggingface

                    
@inproceedings{al-barham-etal-2024-araclip,
title = "{A}ra{CLIP}: Cross-Lingual Learning for Effective {A}rabic Image Retrieval",
author = "Al-Barham, Muhammad  and
Afyouni, Imad  and
Almubarak, Khalid  and
Elnagar, Ashraf  and
Turky, Ayad  and
Hashem, Ibrahim",
editor = "Habash, Nizar  and
Bouamor, Houda  and
Eskander, Ramy  and
Tomeh, Nadi  and
Abu Farha, Ibrahim  and
Abdelali, Ahmed  and
Touileb, Samia  and
Hamed, Injy  and
Onaizan, Yaser  and
Alhafni, Bashar  and
Antoun, Wissam  and
Khalifa, Salam  and
Haddad, Hatem  and
Zitouni, Imed  and
AlKhamissi, Badr  and
Almatham, Rawan  and
Mrini, Khalil",
booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.arabicnlp-1.9",
pages = "102--110",
abstract = "This paper introduces Arabic Contrastive Language-Image Pre-training (AraCLIP), a model designed for Arabic image retrieval tasks, building upon the Contrastive Language-Image Pre-training (CLIP) architecture. AraCLIP leverages Knowledge Distillation to transfer cross-modal knowledge from English to Arabic, enhancing its ability to understand Arabic text and retrieve relevant images. Unlike existing multilingual models, AraCLIP is uniquely positioned to understand the intricacies of the Arabic language, including specific terms, cultural nuances, and contextual constructs. By leveraging the CLIP architecture as our foundation, we introduce a novel approach that seamlessly integrates textual and visual modalities, enabling AraCLIP to effectively retrieve images based on Arabic textual queries. We offer an online demonstration allowing users to input Arabic prompts and compare AraCLIP{'}s performance with state-of-the-art multilingual models. We conduct comprehensive experiments to evaluate AraCLIP{'}s performance across diverse datasets, including Arabic XTD-11, and Arabic Flicker 8k. Our results showcase AraCLIP{'}s superiority in image retrieval accuracy, demonstrating its effectiveness in handling Arabic queries. AraCLIP represents a significant advancement in cross-lingual image retrieval, offering promising applications in Arabic language processing and beyond.",
}
                  
          

Arabic Sign Language Alphabet Classification via Transfer Learning

2023

Muhammad Al-Barham ,Osama Ahmad Alomari, Ashraf Elnagar

Conference Link

                    
@inproceedings{al2023arabic,
title={Arabic Sign Language Alphabet Classification via Transfer Learning},
author={Al-Barham, Muhammad and Alomari, Osama Ahmad and Elnagar, Ashraf},
booktitle={International Conference on Emerging Trends and Applications in Artificial Intelligence},
pages={226--237},
year={2023},
organization={Springer}
}
                  
          

Articles Classification Using Different Word Embeddings

2023

M Moneb Khaled, Muhammad Al-Barham, Osama Ahmad Alomari, Ashraf Elnagar

Conference Link

                    
@inproceedings{khaled2023arabic,
title={Arabic News Articles Classification Using Different Word Embeddings},
author={Khaled, M Moneb and Al-Barham, Muhammad and Alomari, Osama Ahmad and Elnagar, Ashraf},
booktitle={International Conference on Emerging Trends and Applications in Artificial Intelligence},
pages={125--136},
year={2023},
organization={Springer}
}
                  
          

Marine Predatory Algorithm for Feature Selection in Speech Emotion Recognition

2023

Osama Ahmad Alomari ,Muhammad Al-Barham, Ashraf Elnagar.

Conference Link

            
@inproceedings{alomari2023marine,
title={Marine Predatory Algorithm for Feature Selection in Speech Emotion Recognition},
author={Alomari, Osama Ahmad and Al-Barham, Muhammad and Elnagar, Ashraf},
booktitle={International Conference on Emerging Trends and Applications in Artificial Intelligence},
pages={279--291},
year={2023},
organization={Springer}
}
            
          

RGB Arabic Alphabets Sign Language Dataset

2023

Muhammad Al-Barham ,Adham Alsharkawi, Musa Al-Yaman, Mohammad Al-Fetyani, Ashraf Elnagar, Ahmad Abu SaAleek, Mohammad Al-Odat.

Paper Kaggle Dataset Huggingface Dataset Object Detection (1) Object Detection (2)

                
@article{al2023rgb,
title={RGB Arabic Alphabets Sign Language Dataset},
author={Al-Barham, Muhammad and Alsharkawi, Adham and Al-Yaman, Musa and Al-Fetyani, Mohammad and Elnagar, Ashraf and SaAleek, Ahmad Abu and Al-Odat, Mohammad},
journal={arXiv preprint arXiv:2301.11932},
year={2023}
}
                
              

Massive Arabic Speech Corpus

2023

Mohammad Al-Fetyani, Muhammad Al-Barham , Gheith Abandah, Adham Alsharkawi, Maha Dawas.

Paper IEEE Dataport Huggingface Dataset Code Language model ASR Model Twitter Dataset

                
@inproceedings{al2023masc,
title={MASC: Massive Arabic Speech Corpus},
author={Al-Fetyani, Mohammad and Al-Barham, Muhammad and Abandah, Gheith and Alsharkawi, Adham and Dawas, Maha},
booktitle={2022 IEEE Spoken Language Technology Workshop (SLT)},
pages={1006--1013},
year={2023},
organization={IEEE}
}
                
              

Arabic Sign Language Recognition Using Deep Learning Models

2022

Muhammad Al-Barham , Ahmad Abu Sa'Aleek, Mohammad Al-Odat, Ghada Hamad, Musa Al-Yaman, Ashraf Elnagar

                    
@inproceedings{al2022arabic,
title={Arabic Sign Language Recognition Using Deep Learning Models},
author={Al-Barham, Muhammad and Sa'Aleek, Ahmad Abu and Al-Odat, Mohammad and Hamad, Ghada and Al-Yaman, Musa and Elnagar, Ashraf},
booktitle={2022 13th International Conference on Information and Communication Systems (ICICS)},
pages={226--231},
year={2022},
organization={IEEE}
}