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 Profile 

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

Sayyed Ahmed | Computer Science | Best Scholar Award

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Mr. Sayyed Ahmed | Computer Science | Best Scholar Award

Assistant Professor of  Aligarh Muslim University, India

Dr. Sayyed Usman Ahmed is a dedicated academic and researcher in the field of computer engineering, specializing in artificial intelligence and legal reasoning. He has been recognized for his contributions with awards such as the Best Paper Award (2022-23) and the Visvesvaraya Part-Time PhD Fellowship (2018-19). His teaching and research continue to inspire and shape the next generation of engineers and technologists.

Publication profile

Education

Dr. Sayyed Usman Ahmed holds a Ph.D. in Computer Engineering from Aligarh Muslim University (AMU), India, where he conducted research on “Decision Intelligence in Augmentation of Legal Reasoning” under the supervision of Prof. Nesar Ahmad. His thesis was submitted on March 6, 2024. He earned his M.Tech in Computer Engineering from Rajasthan Technical University (2012-2014) with a thesis on evaluating the efficiency and effectiveness of code reading techniques, supervised by Dr. Rajendra Purohit. He completed his B.Tech in Computer Engineering from AMU (2003-2007), with a project on fingerprint detection systems under the guidance of Prof. M. Sarosh Umar and Prof. Syed Atiqur Rahman.

Experience

Dr. Ahmed has extensive experience in academia and industry. He is currently an Assistant Professor at AMU, teaching courses in software engineering, data structures, information security, and programming labs. He has also served as a Deputy Head of the Information Technology department at Jodhpur Institute of Engineering and Technology, where he contributed significantly to teaching, course development, and departmental administration. In the industry, he has worked as an Application Software Engineer at Computer Science Corporation, focusing on software maintenance, bug fixes, and enhancements. Additionally, he has served in various capacities at the Computer Centre of AMU, including roles as a Programmer and Technical Consultant.

Research focus

Dr. Ahmed’s research interests encompass artificial intelligence, machine learning, natural language processing, and decision intelligence. He has published extensively in journals and conferences, focusing on areas such as sentiment analysis, depression detection from social media posts, rumor-free social networks, and news article summarization. His recent research includes a framework for legal case brief generation using natural language processing and smart contract generation through NLP and blockchain.

Publication top notes

1. Ahmad, T., Ahamad, M., Ahmed, S. U., Ahmad, N. (2022) Short question-answers
assessment using lexical and semantic similarity based features, Journal of Discrete
Mathematical Sciences and Cryptography, 25:7, 2057-2067, DOI:
10.1080/09720529.2022.2133245 [ESCI & Scopus]

2. Ahmed, S. U., Ahmad, T., Ahmad, N. (2022). Sentiment Analysis Techniques for
Depression Detection from Micro-Blogging Social Media Post. NueroQuantology
DOI: 10.14704/NQ.2022.20.12.NQ77265 [Scopus]

3. Ahmad, T., Ahmed, S. U., Ali, S. O., & Khan, R. (2020). Beginning with exploring the
way for rumor free social networks. Journal of Statistics and Management Systems, 23(2),
231-238. https://doi.org/10.1080/09720510.2020.1724623 [Web of Science]

4. Ahmad, T., Ahmed, S. U., Ahmad, N., Aziz, A., Mukul, L. (2020). News Article
Summarization: Analysis and Experiments on Basic Extractive Algorithms. International
Journal of Grid and Distributed Computing, 13(2), 2366 – 2379. [Web of Science]

5. Ahmed, S. U. (2018). Monitoring Unscheduled Leaves using IVR. Global Journal of
Computer Science and Technology, 18(1), 7–9. [Peer-reviewed]

6. Ahmed, S. U., & Purohit, R. (2014). Evaluating Efficiency and Effectiveness of Code
Reading Technique with an Emphasis on Enhancing Software Quality. International
Journal of Computer Applications, 2, 32-36. [Peer-reviewed]

7. Ahmed, S. U., Azmi, M. A., Badgujar, C., (2014). How to design and test safety critical
software systems. International Journal of Advances in Computer Science and Technology,
3(1), 19-22. [Peer-reviewed]

8. Ahmed, S. U., Sahare, S. A., & Ahmed, A. (2013). Automatic test case generation using
collaboration UML diagrams. World Journal of Science and Technology. 2, [Peerreviewed]

9. Ahmed, S. U., & Azmi, M. A. (2013). A Novel Model Based Testing (MBT) approach for
Automatic Test Case Generation. International Journal of Advanced Research in
Computer Science, 4(11), 81-83. [Peer-reviewed]

Journal Publications (Under Review)
1. Ahmed, S. U., Ahmed, N., Ahmad, T. (2023) A Rhetorical Role Relatedness (RRR)
framework for Legal Case Brief Generation Natural Language Processing Journal
(Elsevier, Submitted)

Meiyan Liang | Computer Science | Best Researcher Award

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🌟Assoc Prof Dr. Meiyan Liang, Computer Science, Best Researcher Award 🏆

  •  Associate Professor at Shanxi University, China

Meiyan Liang, PhD, is an accomplished researcher in the field of Instrument Science and Technology, with a focus on Deep Learning and Medical Image Processing. Currently affiliated with the School of Physics and Electronic Engineering at Shanxi University in China, Dr. Liang completed her PhD at the Opto-Electronic College, Beijing Institute of Technology. She has made significant contributions to the development of innovative technologies for the identification and classification of various medical conditions, particularly in cancer diagnosis. Her work spans both theoretical and experimental domains, with a particular emphasis on leveraging neural networks and terahertz imaging techniques. Dr. Liang’s expertise is recognized through numerous awards, patents, and a prolific publication record in prestigious journals.

Author Metrics

Scopus Profile

ORCID Profile

Dr. Liang’s research output is not only extensive but also impactful, as evidenced by her author metrics. She has consistently published in high-impact journals, demonstrating the significance of her work within the scientific community. Additionally, Dr. Liang’s patents highlight her innovative approach to problem-solving and technology development.

  • Citations: 138 citations across 136 documents
  • Documents: Authored 25 documents
  • h-index: 5

Education

Dr. Meiyan Liang obtained her PhD in Instrument Science and Technology from the Opto-Electronic College at Beijing Institute of Technology. Her doctoral research focused on the application of deep learning methodologies in medical image processing, particularly for cancer diagnosis.

Research Focus

Dr. Liang’s research primarily centers around two main areas: Deep Learning and Medical Image Processing. Within these domains, she specializes in utilizing neural networks for the interpretation and analysis of medical images, with a particular emphasis on cancer detection and classification. Her work also involves the integration of advanced imaging techniques, such as terahertz imaging, to develop novel diagnostic tools.

Professional Journey

Following her doctoral studies, Dr. Liang embarked on a professional journey that has seen her become an esteemed researcher in the field of medical imaging. She has held positions at various academic institutions, including her current role at Shanxi University. Throughout her career, Dr. Liang has secured research funding, published extensively, and obtained several patents for her innovative contributions to the field.

Honors & Awards

Dr. Liang’s outstanding contributions to her field have been recognized through numerous honors and awards. Notable accolades include being awarded the “Sanjin talent” by the government of Shanxi Province and receiving the “China Instrument & Control Society Scholarship” from the Chinese instrumentation society.

Research Timeline

Dr. Liang’s research timeline showcases her progression as a researcher and the evolution of her research interests. Starting from her doctoral studies, she has continued to expand her expertise and contribute to advancements in medical imaging technology. Her research timeline reflects a commitment to excellence and a dedication to addressing critical challenges in healthcare through innovative research.

Publications Noted & Contributions

Dr. Liang has made significant contributions to the academic community through her prolific publication record. Her research findings have been published in prestigious journals such as the IEEE Journal of Biomedical and Health Informatics, Computer Methods and Programs in Biomedicine, and The Visual Computer. These publications cover a wide range of topics, including interpretable inference, whole-slide image prediction, and pathology image restoration.

Title: Interpretable Inference and Classification of Tissue Types in Histological Colorectal Cancer Slides Based on Ensembles Adaptive Boosting Prototype Tree

  • Authors: Liang, M., Wang, R., Liang, J., Zhang, T., Zhang, C.
  • Journal: IEEE Journal of Biomedical and Health Informatics, 2023, 27(12), pp. 6006–6017
  • Abstract: This paper presents a method for interpretable inference and classification of tissue types in histological colorectal cancer slides using ensembles adaptive boosting prototype tree.

Title: Multi-scale self-attention generative adversarial network for pathology image restoration

  • Authors: Liang, M., Zhang, Q., Wang, G., Liu, H., Zhang, C.
  • Journal: Visual Computer, 2023, 39(9), pp. 4305–4321
  • Abstract: This paper introduces a multi-scale self-attention generative adversarial network for pathology image restoration.
  • Citations: 1

Title: Interpretable classification of pathology whole-slide images using attention based context-aware graph convolutional neural network

  • Authors: Liang, M., Chen, Q., Li, B., Jiang, X., Zhang, C.
  • Journal: Computer Methods and Programs in Biomedicine, 2023, 229, 107268
  • Abstract: This paper proposes an interpretable classification method for pathology whole-slide images using an attention-based context-aware graph convolutional neural network.
  • Citations: 6

Title: A novel strategy regarding geometric product for liquids discrimination based on THz reflection spectroscopy

  • Authors: Liu, H., Liu, X., Zhang, Z., Liang, M., Zhang, C.
  • Journal: Spectrochimica Acta – Part A: Molecular and Biomolecular Spectroscopy, 2022, 274, 121104
  • Abstract: This paper proposes a novel strategy for liquids discrimination based on THz reflection spectroscopy using the geometric product.
  • Citations: 1

Title: THz ISAR imaging using GPU-accelerated phase compensated back projection algorithm

  • Authors: Liang, M.-Y., Ren, Z.-Y., Li, G.-H., Zhang, C.-L., Fathy, A.E.
  • Journal: Hongwai Yu Haomibo Xuebao/Journal of Infrared and Millimeter Waves, 2022, 41(2), pp. 448–456
  • Abstract: This paper presents THz ISAR imaging using a GPU-accelerated phase-compensated back projection algorithm.
  • Citations: 3