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

Nunzio Alberto Borghese | Computer Science | Best Researcher Award

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Prof. Nunzio Alberto Borghese | Computer Science | Best Researcher Award

Full professor at Università degli Studi di MIlano, Italy

Professor N. Alberto Borghese is a renowned researcher in computational intelligence and its application to real-world problems. He graduated magna cum laude in Electrical Engineering from Politecnico di Milan and has held significant academic positions, including Full Professor at the University of Milan. His research focuses on innovative methods such as multi-scale hierarchical neural networks, adaptive clustering, and statistical data processing, with particular emphasis on limited processing time. He has made notable contributions to e-Health and robotics, integrating AI, service robots, virtual communities, and smart objects to improve healthcare and welfare systems. With over 90 journal papers, 140+ conference papers, and 16 international patents, he has a strong academic and industrial impact. He has led several high-profile projects funded by the European Commission and Italian government, including REWIRE, MOVECARE, and AIRCA. His work continues to advance the intersection of AI, robotics, and healthcare, addressing critical societal needs.

Professional Profile 

Education

Professor N. Alberto Borghese received his education in Electrical Engineering, graduating magna cum laude in 1986 from Politecnico di Milan, one of Italy’s leading institutions. This strong academic foundation laid the groundwork for his extensive research career. His academic journey furthered through his role as a tenured researcher at the National Research Council (CNR) from 1987 to 2000, where he began developing his expertise in computational intelligence. This led to his appointment as an Associate and later Full Professor at the Department of Computer Science, University of Milan (UNIMI). At UNIMI, he also directs the Laboratory of Applied Intelligent Systems, where he has mentored students and led cutting-edge research projects. Professor Borghese’s education and professional development have been marked by continuous innovation, research leadership, and a commitment to applying his knowledge to real-world challenges, particularly in e-Health, robotics, and AI.

Professional Experience

Professor N. Alberto Borghese has had a distinguished professional career, beginning as a tenured researcher at the National Research Council (CNR) from 1987 to 2000. During this time, he built a strong foundation in computational intelligence. He then transitioned to the University of Milan (UNIMI), where he became an Associate Professor and later a Full Professor in the Department of Computer Science. At UNIMI, he also directs the Laboratory of Applied Intelligent Systems, where he leads innovative research projects focused on AI, robotics, and e-Health. Throughout his career, he has contributed to over 90 journal papers, more than 140 conference papers, and holds 16 international patents. Professor Borghese has led several major research projects funded by the European Commission, including REWIRE, MOVECARE, and FITREHAB, and has been involved in multiple Italian government-funded initiatives. His work bridges academia and industry, addressing pressing societal needs in healthcare and welfare through technological advancements.

Research Interest

Professor N. Alberto Borghese’s research interests lie primarily in the field of computational intelligence, focusing on the development and application of advanced algorithms to solve real-world problems. He specializes in multi-scale hierarchical neural networks, adaptive clustering, and statistical data processing, with an emphasis on optimizing solutions for limited processing time. His work extends to the integration of Artificial Intelligence (AI) and robotics, particularly in the domains of e-Health and e-Welfare. Professor Borghese has pioneered the use of service robots, virtual communities, and smart objects, creating innovative platforms that enhance healthcare and welfare systems. His research also explores the intersection of AI with healthcare technologies such as exer-games, aiming to improve accessibility and promote well-being. Additionally, he has a strong focus on interdisciplinary collaboration, leading several European and Italian research projects that combine AI, robotics, and human-centered design to address societal challenges in health, aging, and rehabilitation.

Award and Honor

Professor N. Alberto Borghese has received numerous awards and honors throughout his distinguished academic and research career. His recognition stems from his innovative contributions to computational intelligence, AI, and robotics, particularly in the fields of e-Health and e-Welfare. With over 90 journal papers and 140+ conference papers, his research has garnered widespread acclaim, reflected in his h-index of 42. He has also been honored for his extensive intellectual property contributions, holding 16 international patents. His leadership in research has been recognized through his involvement in high-profile projects funded by the European Commission and Italian government, such as REWIRE (FP7), MOVECARE (H2020), and AIRCA (2023-2025). These honors not only underline his academic excellence but also highlight his impact on advancing technology in healthcare and welfare systems. His continued success in securing major funding and his role in shaping interdisciplinary research make him a highly respected figure in his field.

Conclusion

Based on his exceptional academic qualifications, pioneering research in computational intelligence and e-Health, leadership in high-profile projects, and impressive publication and patent record, N. Alberto Borghese is a highly suitable candidate for the Best Researcher Award. Addressing minor improvements in public engagement and cross-disciplinary impact could further strengthen his candidacy. Nonetheless, his proven expertise and contributions make him a deserving nominee.

Publications Top Noted

  • Kinematic determinants of human locomotion
    • Authors: N. Alberto Borghese, L. Bianchi, F. Lacquaniti
    • Year: 1996
    • Citations: 553
  • Different brain correlates for watching real and virtual hand actions
    • Authors: D. Perani, F. Fazio, N. A. Borghese, M. Tettamanti, S. Ferrari, J. Decety, …
    • Year: 2001
    • Citations: 402
  • Autocalibration of MEMS accelerometers
    • Authors: I. Frosio, F. Pedersini, N. A. Borghese
    • Year: 2008
    • Citations: 261
  • Time-varying mechanical behavior of multijointed arm in man
    • Authors: F. Lacquaniti, M. Carrozzo, N. A. Borghese
    • Year: 1993
    • Citations: 202
  • Internal models of limb geometry in the control of hand compliance
    • Authors: F. Lacquaniti, N. A. Borghese, M. Carrozzo
    • Year: 1992
    • Citations: 197
  • Reading the reading brain: a new meta-analysis of functional imaging data on reading
    • Authors: I. Cattinelli, N. A. Borghese, M. Gallucci, E. Paulesu
    • Year: 2013
    • Citations: 188
  • A functional-anatomical model for lipreading
    • Authors: E. Paulesu, D. Perani, V. Blasi, G. Silani, N. A. Borghese, U. De Giovanni, …
    • Year: 2003
    • Citations: 163
  • The role of vision in tuning anticipatory motor responses of the limbs
    • Authors: F. Lacquaniti
    • Year: 1993
    • Citations: 151
  • Exergaming and rehabilitation: A methodology for the design of effective and safe therapeutic exergames
    • Authors: M. Pirovano, E. Surer, R. Mainetti, P. L. Lanzi, N. A. Borghese
    • Year: 2016
    • Citations: 148
  • Self-adaptive games for rehabilitation at home
    • Authors: M. Pirovano, R. Mainetti, G. Baud-Bovy, P. L. Lanzi, N. A. Borghese
    • Year: 2012
    • Citations: 146
  • Transient reversal of the stretch reflex in human arm muscles
    • Authors: F. Lacquaniti, N. A. Borghese, M. Carrozzo
    • Year: 1991
    • Citations: 144
  • Computational intelligence and game design for effective at-home stroke rehabilitation
    • Authors: N. A. Borghese, M. Pirovano, P. L. Lanzi, S. Wüest, E. D. de Bruin
    • Year: 2013
    • Citations: 139
  • Automatic detection of powdery mildew on grapevine leaves by image analysis: Optimal view-angle range to increase the sensitivity
    • Authors: R. Oberti, M. Marchi, P. Tirelli, A. Calcante, M. Iriti, A. N. Borghese
    • Year: 2014
    • Citations: 128
  • Usability and effects of an exergame-based balance training program
    • Authors: S. Wüest, N. A. Borghese, M. Pirovano, R. Mainetti, R. van de Langenberg, …
    • Year: 2014
    • Citations: 121
  • Pattern recognition in 3D automatic human motion analysis
    • Authors: G. Ferrigno, N. A. Borghese, A. Pedotti
    • Year: 1990
    • Citations: 121

Bechoo Lal | Computer Science | Best Researcher Award

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Dr. Bechoo Lal | Computer Science | Best Researcher Award

Associate Professor of KLEF- KL University Vijayawada Campus Andhra Pradesh, India

Dr. Bechoolal 🌟 is an esteemed Associate Professor in Computer Science/Data Science with a passion for inspiring students through a deep understanding of technology and research. With a solid academic foundation that includes a PGP in Data Science from Purdue University and multiple PhDs in Information Systems and Computer Science 🎓, he brings a wealth of expertise to his teaching and research. Dr. Bechoolal has extensive experience in various institutions, from KLEF KL Deemed University to Western College 🏫, and has made significant contributions through his numerous research publications and certifications 🏅. His interests span Machine Learning, Data Science, and programming languages, and he actively engages in projects that explore digital transformation and its societal impacts 💻🔍. Fluent in English and Hindi 🇬🇧🇮🇳, he continues to advance knowledge and inspire the next generation of tech professionals.

Publication profile

Education

Dr. Bechoolal 🎓 is a distinguished academic with a rich educational background in Computer Science and Data Science. He earned a PGP in Data Science from Purdue University 🌟, where he specialized in data regression models and predictive data modeling. Dr. Bechoolal holds multiple PhDs—one in Information Systems from the University of Mumbai and another in Computer Science from SJJT University 🧠. His foundational studies include a Master of Technology in Computer Science from AAI-Deemed University, a Master of Computer Applications from Banaras Hindu University, and an undergraduate degree in Statistics from MG. Kashi Vidyapeeth University 📚. His continuous quest for knowledge is also reflected in his various certifications, including Machine Learning from Stanford University and an IBM Data Science Professional Certificate 🏅.

Academic Qualification

  • 📜 PGP in Data Science (2020-2021) from Purdue University, USA – Specializing in data regression models, predictive data modeling, and accuracy analyzing using machine learning.
  • 📜 PhD in Information System (2015-2019) from the University of Mumbai, India – Research Area: Data Science.
  • 📜 PhD in Computer Science (2011-2015) from SJJT University, India – Research Area: Machine Learning.
  • 📜 Master of Technology (M. Tech) in Computer Science and Engineering (2004-2006) from AAI-Deemed University, Allahabad, India.
  • 📜 Master of Computer Application (MCA) (1995-1998) from Institute of Science, Banaras Hindu University (BHU), India.
  • 📜 Graduation (Statistics-Hons) (1990-1993) from the Department of Mathematics and Statistics, MG Kashi Vidyapeeth University, India.

Data Science Certifications and Training

  • 🎓 Machine Learning, Stanford University, USA (2020)
  • 🎓 IBM Data Science Professional Certificate (2020)
  • 🎓 Data Science and Big Data Analytics (2019), ICT Academy, Govt. of India
  • 🎓 Security Fundamentals, Microsoft Technology Associate (2017)
  • 🎓 Intelligent Multimedia Data Warehouse and Mining (2009), University of Mumbai
  • 🎓 Python Programming (2017), University of Mumbai, India

 

Teaching Interest 

  • 📘 Data Science/Machine Learning
  • 📘 Database 📘 C/C++/Python Programming Languages
  • 📘 Software Engineering

Research Interest

  • 🔍 Machine Learning
  • 🔍 Data Science

Computer Science/Data Science Skills

💻 Machine Learning, Data Visualization, Big Data Analytics

📊 Predictive Modelling: Supervised Learning (Linear and Logistic Regression, Decision Tree, Support Vector Machine (SVM), Naïve Bayes Classifiers), Unsupervised Learning (K-Means clustering, principal components analysis (PCA))

💻 Programming Languages: Python (NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn), SPSS, R-Programming

💻 Operating Systems/Platforms: UNIX/LINUX, WINDOWS, MS-DOS

💻 C/C++, CORE JAVA Programming Languages

💻 DBMS/RDBMS: Oracle, SQL, MySQL, NoSQL

Publication top notes

  • Improving migration forecasting for transitory foreign tourists using an Ensemble DNN-LSTM model
    Authors: Nanjappa, Y., Kumar Nassa, V., Varshney, G., Pandey, S., V Turukmane, A.
    Journal: Entertainment Computing
    Year: 2024
    Citations: 0 📅
  • Using social networking evidence to examine the impact of environmental factors on social followings: An innovative Machine learning method
    Authors: Murthy, S.V.N., Ramesh, P.S., Padmaja, P., Reddy, G.J., Chinthamu, N.
    Journal: Entertainment Computing
    Year: 2024
    Citations: 0 📅
  • Real-Time Convolutional Neural Networks for Emotion and Gender Classification
    Authors: Singh, J., Singh, A., Singh, K.K., Samudre, N., Raperia, H.
    Conference: Procedia Computer Science
    Year: 2024
    Citations: 0 📅
  • Identification of Brain Diseases using Image Classification: A Deep Learning Approach
    Authors: Singh, J., Singh, A., Singh, K.K., Turukmane, A.V., Kumar, A.
    Conference: Procedia Computer Science
    Year: 2024
    Citations: 0 📅
  • Fake News Detection Using Transfer Learning
    Authors: Singh, J., Sahu, D.P., Gupta, T., Lal, B., Turukmane, A.V.
    Conference: Communications in Computer and Information Science
    Year: 2024
    Citations: 0 📅
  • Reliability Evaluation of a Wireless Sensor Network in Terms of Network Delay and Transmission Probability for IoT Applications
    Authors: Mishra, P., Dash, R.K., Panda, D.K., Lal, B., Sujata Gupta, N.
    Journal: Contemporary Mathematics (Singapore)
    Year: 2024
    Citations: 0 📅
  • TRANSFER LEARNING METHOD FOR HANDLING THE INTRUSION DETECTION SYSTEM WITH ZERO ATTACKS USING MACHINE LEARNING AND DEEP LEARNING
    Authors: Upender, T., Lal, B., Nagaraju, R.
    Conference: ACM International Conference Proceeding Series
    Year: 2023
    Citations: 0 📅
  • Monitoring and Sensing of Real-Time Data with Deep Learning Through Micro- and Macro-analysis in Hardware Support Packages
    Authors: Lal, B., Chinthamu, N., Harichandana, B., Sharmaa, A., Kumar, A.R.
    Journal: SN Computer Science
    Year: 2023
    Citations: 0 📅
  • An Efficient QRS Detection and Pre-processing by Wavelet Transform Technique for Classifying Cardiac Arrhythmia
    Authors: Lal, B., Gopagoni, D.R., Barik, B., Kumar, R.D., Lakshmi, T.R.V.
    Journal: International Journal of Intelligent Systems and Applications in Engineering
    Year: 2023
    Citations: 0 📅
  • IOT-BASED Cyber Security Identification Model Through Machine Learning Technique
    Authors: Lal, B., Ravichandran, S., Kavin, R., Bordoloi, D., Ganesh Kumar, R.
    Journal: Measurement: Sensors
    Year: 2023
    Citations: 3 📅📈