Siliang Ma | Computer Science | Best Researcher Award

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Dr. Siliang Ma | Computer Science | Best Researcher Award

Senior Algorithm Engineer at School of Computer Science and Engineering, South China University of Technology, China

Dr. Siliang Ma, a Ph.D. candidate at South China University of Technology, is an accomplished researcher specializing in computer science with a focus on image processing and machine learning. With an excellent academic record, including a bachelor’s degree from South China Agricultural University (GPA: 3.99/5), Dr. Ma has made significant contributions to cutting-edge research. His works, published in esteemed journals such as Acta Automatica Sinica and Image and Vision Computing, address topics like calligraphy character recognition, multilingual scene text spotting, and efficient bounding box regression through novel loss functions like MPDIoU and FPDIoU. A skilled programmer proficient in Python, Java, and C#, he has developed robust image processing algorithms and software applications. Dr. Ma also contributes as a reviewer for leading conferences like ICRA and ICASSP, reflecting his commitment to advancing the research community. His innovative and impactful work positions him as a rising talent in computational science.

Professional ProfileĀ 

Education

Dr. Siliang Ma has a strong educational background in computer science and engineering. He is currently pursuing a Ph.D. at the South China University of Technology, where he has maintained an excellent GPA of 86.33/100. His doctoral research focuses on cutting-edge topics in image processing, machine learning, and computational algorithms, demonstrating both theoretical depth and practical relevance. Prior to this, Dr. Ma earned his bachelor’s degree from South China Agricultural University, graduating with a remarkable GPA of 3.99/5. His undergraduate studies in mathematics and informatics laid a solid foundation for his advanced research pursuits, equipping him with the analytical and technical skills essential for solving complex computational problems. Through rigorous academic training and dedication, Dr. Ma has excelled in his education, which is further reflected in his extensive publications in high-impact journals and his active engagement in academic conferences and peer reviews.

Professional Experience

Dr. Siliang Ma has gained valuable professional experience through diverse roles in research and industry, complementing his academic achievements. He interned as a Data Analyst at the China Construction Bank Guangdong Branch Technology Center, where he conducted financial data analysis using PostgreSQL, mastering database operations and complex linked table queries. As a Quality Engineer at the China Mobile Guangdong Branch Business Support Center, he developed a JavaWeb-based minimum feature set for user registration, login, and management, and implemented automated quality testing workflows using Jenkins. These roles allowed Dr. Ma to hone his skills in software development, data analysis, and quality assurance, showcasing his ability to translate theoretical knowledge into practical applications. Additionally, his expertise in programming and image processing has led to impactful contributions in academia, particularly in algorithm development. This blend of industrial and research experience positions Dr. Ma as a versatile professional in computer science and engineering.

Research Interest

Dr. Siliang Ma’s research interests lie at the intersection of computer vision, machine learning, and image processing. He is particularly focused on developing innovative algorithms and techniques for efficient and accurate object detection, scene text recognition, and character recognition. His work explores advanced loss functions, such as MPDIoU and FPDIoU, to optimize bounding box regression for both traditional and rotated object detection. Additionally, Dr. Ma has a keen interest in multilingual scene text spotting, where he leverages character-level features and benchmarks to improve the accuracy of text recognition across diverse languages. His research extends to robust graph learning and hypergraph-enhanced self-supervised models for social recommendation systems, showcasing his ability to address complex, real-world challenges. Through his work, Dr. Ma aims to bridge theoretical advancements with practical applications, contributing to the broader fields of artificial intelligence, data analysis, and computational optimization.

Award and Honor

Dr. Siliang Ma has been recognized for his academic and research excellence through various accolades and contributions. As a Ph.D. candidate at South China University of Technology, his consistent high performance, reflected in his impressive GPA, underscores his dedication to academic rigor. Although specific awards or honors are not explicitly listed in his profile, his role as a reviewer for prestigious conferences such as ICRA and ICASSP highlights his esteemed position within the research community. Dr. Ma’s impactful publications in top-tier journals and conferences, including Acta Automatica Sinica and Image and Vision Computing, further demonstrate the high regard in which his work is held. His innovative contributions to image processing and machine learning have earned him recognition as a rising talent in his field. These achievements reflect Dr. Ma’s commitment to advancing computational science and his growing influence in academic and professional circles.

Conclusion

Siliang Ma is a strong candidate for the Best Researcher Award due to his impressive academic record, significant publications, and technical expertise. His contributions to advanced image processing algorithms and innovative loss functions for object detection demonstrate technical ingenuity and research excellence. To further strengthen his profile, he could expand his research impact through interdisciplinary work, mentorship roles, and greater industry engagement.

Publications Top Noted

  • Title: FPDIoU Loss: A loss function for efficient bounding box regression of rotated object detection
    Authors: Siliang Ma, Yong Xu
    Year: 2024
    Citation: Ma, S., & Xu, Y. (2024). FPDIoU Loss: A loss function for efficient bounding box regression of rotated object detection. Image and Vision Computing. https://doi.org/10.1016/j.imavis.2024.105381
  • Title: Rethinking Multilingual Scene Text Spotting: A Novel Benchmark and a Character-Level Feature Based Approach
    Authors: Siliang Ma, Yong Xu
    Year: 2024
    Citation: Ma, S., & Xu, Y. (2024). Rethinking Multilingual Scene Text Spotting: A Novel Benchmark and a Character-Level Feature Based Approach. American Journal of Computer Science and Technology. https://doi.org/10.11648/j.ajcst.20240703.12

Salim Chehida | Software engineering | Best Researcher Award

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šŸŒŸDr. Salim Chehida, Software engineering, Best Researcher Award šŸ†

  • Ā Doctorate at VERIMAG – UniversitĆ© Grenoble alpes, France

Salim Chehida is a distinguished researcher and development expert in software engineering, with extensive experience in academia and industry. Holding a PhD in Computer Science from the University of Oran 1 in Algeria in collaboration with the University of Grenoble Alpes in France, he has been actively involved in numerous innovative projects and has made significant contributions to the field. His expertise includes software engineering, deep learning, formal modeling, and security, among others.

Author Metrics:

Scopus Profile

ORCID Profile

Google Scholar Profile

Salim Chehida’s contributions to the field of software engineering and related areas are notable, evidenced by his publication record and citations. He has a strong presence in academic databases such as DBLP, Google Scholar, and ResearchGate, where his work is widely referenced and acknowledged by the research community. Salim Chehida has authored or co-authored 24 documents, with a total of 48 citations.

  • Citations: 48
  • Documents: 24
  • h-index: 4

Education:

Salim Chehida pursued his academic journey with dedication and excellence. He obtained his PhD in Computer Science specializing in Software Engineering and Systems Security from the University of Oran 1 in Algeria, in collaboration with the University of Grenoble Alpes in France. Prior to that, he completed his Magister Degree in Information Systems Engineering and his Engineer Degree in Computer Science from the University of Mostaganem in Algeria.

Research Focus:

Salim Chehida’s research interests primarily revolve around software engineering, deep learning, and formal methods. His work encompasses various aspects such as software design, development, security, verification, and validation, particularly focusing on cyber-physical systems (CPS) and Internet of Things (IoT) systems. He is also involved in projects related to deep learning applications, including image classification, segmentation, and object detection.

Professional Journey:

Salim Chehida has a rich professional journey that spans both academia and industry. He started his career as a Software Engineer at the Finance Direction in Algeria, where he was responsible for designing and developing software systems. Later, he transitioned into academia, serving as a Scientific Researcher and Assistant Professor at the University of Mostaganem in Algeria. Currently, he holds the position of R&D Expert at the University of Grenoble Alpes in France, working at the VERIMAG and LIG laboratories, INRIA.

Honors & Awards:

Salim Chehida’s contributions to the field have been recognized with several honors and awards. Notably, he has received the Best Researcher Award in software engineering, acknowledging his outstanding achievements and impact on the research community.

Publications Noted & Contributions:

Salim Chehida has made significant contributions to the field through his publications, which are noted for their relevance, quality, and impact. His research addresses key challenges in software engineering, deep learning, and formal methods, providing valuable insights and solutions to real-world problems.

Learning and analysis of sensors behavior in IoT systems using statistical model checking

Published in the Software Quality Journal in June 2022.

Contributors: Salim Chehida, Abdelhakim Baouya, Saddek Bensalem, Marius Bozga.

DOI: 10.1007/s11219-021-09559-w

Asset-Driven Approach for Security Risk Assessment in IoT Systems

Presented at the Risks and Security of Internet and Systems conference in 2021.

DOI: 10.1007/978-3-030-68887-5_9

Applied Statistical Model Checking for a Sensor Behavior Analysis

Included as a book chapter in Communications in Computer and Information Science in 2020.

DOI: 10.1007/978-3-030-58793-2_32

Formal Modeling and Verification of Blockchain Consensus Protocol for IoT Systems

Presented as a book chapter in Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques in September 2020.

DOI: 10.3233/FAIA200578

Exploration of Impactful Countermeasures on IoT Attacks

Presented at the 2020 9th Mediterranean Conference on Embedded Computing (MECO) in June 2020.

DOI: 10.1109/meco49872.2020.9134200

Research Timeline:

Salim Chehida’s research timeline showcases his involvement in various projects and initiatives over the years. From his early contributions to projects like ANR MODMED to his recent endeavors in FOCETA and MODWIN UGA, his work has evolved to address diverse challenges in software engineering, cyber-physical systems, and deep learning.

Collaborations and Projects:

Salim Chehida has been actively involved in collaborative projects both nationally and internationally. His collaborations extend across academia, industry, and research institutions, contributing to the development of innovative solutions and advancements in software engineering, deep learning, and related fields. Notable projects include ANR MODMED, EU BRAIN-IoT, EU CPS4EU, and EU FOCETA, among others.