Soumeya Belabbas | Artificial Intelligence | Best Researcher Award

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Soumeya Belabbas| Artificial Intelligence| Best Researcher Award

Soumeya Belabbas,University of Sciences and Technology Houari Boumediene,United state.

Soumeya has taught telecommunications systems and networks to fourth-year engineering students at the Higher National School of Information and Communication Technologies and Post in Algiers. Her teaching module includes mobile networks.

Publication Profile

Google Scholar

Education 

Soumeya Belabbas is currently pursuing her PhD in Telecommunication and Information Processing at the University of Sciences and Technology Houari Boumediene in Algiers, Algeria, a journey she embarked on in December 2017. Her doctoral research focuses on multi-variable acoustic modeling for assessing pathological speech understanding, showcasing her deep commitment to advancing the field of telecommunications and speech processing. In addition to her ongoing doctoral studies, Soumeya holds a Master’s degree in Telecommunications, Networks, and Multimedia, which she earned in June 2017 from the same university. This advanced degree equipped her with a robust foundation in telecommunications, further honed through her bachelor’s degree in Telecommunications and Electronics, completed in June 2015. Her academic journey began with a Baccalaureate Diploma in Experimental Sciences, awarded in July 2012, which laid the groundwork for her subsequent specialization and research. Soumeya has also actively participated in professional development activities, such as a workshop on FPGA systems in April 2024, reflecting her continuous pursuit of knowledge and expertise in her field.

Soumeya Belabbas has cultivated a solid foundation in both research and teaching throughout her academic career. As a part-time teacher at the Higher National School of Information and Communication Technologies and Post in Algiers, Algeria, she has been responsible for educating fourth-year engineering students in telecommunications systems and networks, with a specific focus on mobile networks. Her teaching role, undertaken in June 2024, has allowed her to share her extensive knowledge and experience with the next generation of engineers. Alongside her teaching duties, Soumeya has made significant contributions to the field of telecommunications and information processing through her research. She has published multiple articles and presented her findings at international conferences, showcasing her expertise in improving mobile speech recognition systems and pathological speech classification. Her role as a researcher and educator demonstrates her commitment to advancing the field and fostering academic growth.

Soumeya Belabbas’s research is centered on the cutting-edge domains of telecommunications and speech processing, with a particular emphasis on addressing challenges in automatic speech recognition and classification. Her work is dedicated to developing innovative solutions for individuals with voice disorders, aiming to enhance the robustness and accuracy of speech recognition systems. Her current PhD research project, titled “Multi-variable Acoustic Modeling for Assessing Pathological Speech Understanding,” exemplifies her focus on improving pathological speech classification systems. This project involves a comprehensive three-stage framework that incorporates advanced techniques such as speech enhancement, multi-stream approaches, and deep machine learning algorithms like convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks. Additionally, Soumeya’s interests extend to signal processing, image processing, and audio speech processing, leveraging machine learning and deep learning methodologies to tackle complex problems in these areas. Her dedication to these research areas is reflected in her numerous publications and presentations, which contribute to the advancement of knowledge and technology in telecommunications and speech processing.

Skills

Soumeya Belabbas possesses a diverse and robust skill set that spans various aspects of telecommunications, information processing, and programming. She is proficient in multiple programming languages, including C/C++, Python, Perl, MATLAB, VHDL, PHP, HTML5, and CSS3, which enable her to develop sophisticated algorithms and models for speech processing and recognition. Her technical expertise extends to the use of HTK tools, essential for hidden Markov model training and evaluation in speech recognition systems. Soumeya is also skilled in managing and operating different operating systems, including Windows Server and Linux (Ubuntu), ensuring that she can work in a versatile computing environment. Her strong foundation in signal and audio speech processing is complemented by her experience with machine learning and deep learning techniques, which she applies to enhance the performance of speech recognition systems. Additionally, Soumeya is well-versed in the use of various software tools and platforms essential for research and development in her field, making her a well-rounded and highly capable researcher and education .

Publications 📚📝

MohammadSadeq Mottaqi | Machine Learning | Best Researcher Award

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MohammadSadeq Mottaqi | Machine Learning | Best Researcher Award

Dr. MohammadSadeq Mottaqi, City University of New York, United States.

Dr. MohammadSadeq Mottaqi is a Ph.D. candidate in Biochemistry at the City University of New York, with a background in Biotechnology from the University of Tehran. Proficient in Python and R, Mohammad excels in bioinformatics tools like PyMol and Gromacs. His research spans machine learning-based drug action prediction, molecular dynamics simulations, and protein-ligand interactions. He has published on topics such as SARS-CoV-2 infection responses and cyanobacterial genera identification. Mohammad’s technical prowess extends to data analysis, visualization, and high-performance computing, reflecting his commitment to advancing biotechnological and computational research. 🧬🔬💻

Publication Profile

Scopus

orcid

Education 🎓

Ph.D. in Biochemistry
City University of New York, New York, NY
Aug 2022 – Present

Bachelor of Science in Biotechnology
University of Tehran, Tehran, Iran
Sep 2017 – March 2022

Experience 💼

Graduate Assistant
City University of New York, New York, NY
Aug 2022 – Present

  • Assisting in bioinformatics and computational biochemistry research.

Research Experience

  • Conducted molecular dynamics simulations of mutated HER2 proteins.
  • Investigated protein-ligand interactions using Python and PyMOL.
  • Developed machine learning models for predicting drug actions.

 

Professional Development

Mr.Mohammad Sadeq Mottaqi is currently immersed in his Ph.D. journey in Biochemistry at the City University of New York, focusing on computational biochemistry. His research expertise includes pioneering machine learning approaches for predicting drug actions and analyzing transcriptomics data to advance personalized medicine. Mohammad’s impactful contributions extend to molecular dynamics simulations of protein interactions and investigating the molecular mechanisms of SARS-CoV-2 mutations. With a foundation in biotechnology from the University of Tehran, he blends technical skills in Python, R, and bioinformatics tools like Gromacs and PyMol. Mohammad is dedicated to pushing the boundaries of bioinformatics, bridging theoretical insights with practical applications in healthcare and environmental management.🌱

Research Focus

Mr. MohammadSadeq Mottaqi is a biochemist and computational scientist specializing in bioinformatics and machine learning applications in drug discovery and molecular biology. His research includes predictive modeling for drug actions, molecular dynamics simulations, and protein-ligand interactions. Mohammad has contributed significantly to understanding SARS-CoV-2 through machine learning approaches and automated identification of toxigenic cyanobacterial genera for water quality control. His work, cited extensively, showcases his expertise in integrating computational tools and biotechnological research, making impactful advancements in these fields. 🌐🔬💻📊

Publications 📚📝

  • Automated identification of toxigenic cyanobacterial genera for water quality control purposes, Journal of Environmental Management, 2024 🌿
  • Contribution of machine learning approaches in response to SARS-CoV-2 infection, Informatics in Medicine Unlocked, 2021 🦠
  • Mutations in SARS-CoV-2; Consequences in structure, function, and pathogenicity of the virus, Microbial Pathogenesis, 2021 🧬