Dr. Zhiyong Zhang | Data Science | Best Researcher Award

Dr. Zhiyong Zhang, Tianjin university of traditional Chinese medicine, China

Dr. Zhiyong Zhang is a Ph.D. candidate at Tianjin University of Traditional Chinese Medicine, China. His research focuses on the integration of modern analytical techniques and artificial intelligence to enhance quality evaluation and control in Traditional Chinese Medicine (TCM). With expertise in LC/GC-MS, ICP-MS, NIR, and LIBS, he has contributed to over 20 peer-reviewed publications, including 12 SCI-indexed papers as first or co-first author. His work advances real-time, data-driven approaches for TCM manufacturing and herbal product authentication.

Profile

Scopus

Summary:

Dr. Zhiyong Zhang is a rising star in the field of Traditional Chinese Medicine research, demonstrating excellence in combining classical medical knowledge with state-of-the-art analytical and computational techniques. His research is not only scientifically rigorous but also highly relevant to real-world applications in medicine production, safety, and quality assurance. With a growing list of impactful publications and innovative methodologies, he exemplifies the type of interdisciplinary thinker the Research for Best Researcher Award aims to recognize.

🎓 Education

Zhiyong Zhang, born in September 1997 in Huaibei, Anhui, China, is currently a Ph.D. candidate in Traditional Chinese Medicine at the Tianjin University of Traditional Chinese Medicine, under the supervision of Researcher Wenlong Li. He began his doctoral studies in September 2022. Prior to this, he participated in a joint master’s training program at the First Institute of Oceanography, Ministry of Natural Resources, from September 2021 to June 2022, where he worked with Researcher Junhui Chen. Zhang completed his Master of Science in Traditional Chinese Medicine at the Tianjin University of Traditional Chinese Medicine from September 2020 to June 2021. His academic journey began with a Bachelor of Science in Traditional Chinese Medicine Pharmaceutical at the same university, completed between 2015 and 2019.

💼Experience

Dr. Zhiyong Zhang has extensive academic and research experience in the field of Traditional Chinese Medicine (TCM). He is currently pursuing his Ph.D. at Tianjin University of Traditional Chinese Medicine, where he focuses on quality assessment and control of TCM using advanced analytical technologies and artificial intelligence. He previously participated in a joint master’s training program at the First Institute of Oceanography, Ministry of Natural Resources, where he gained interdisciplinary experience in TCM analysis and data integration. He also completed both his Master’s and Bachelor’s degrees in Traditional Chinese Medicine and Pharmaceutical Sciences at Tianjin University of Traditional Chinese Medicine. Over the years, Dr. Zhang has developed strong expertise in applying chemometrics, metabolomics, and spectroscopy-based techniques to improve the reliability and efficiency of TCM research and manufacturing.

🔬Research Focus

Zhiyong Zhang’s research centers on advancing the quality evaluation and control of Traditional Chinese Medicine (TCM) through modern analytical and computational techniques. His interests include machine learning-based quality analysis of TCM using multimodal data, rapid on-site quality control of Chinese herbal materials and processed products, and the development of multidimensional quality evaluation systems integrating metabolomics, elementalomics, and chemometrics. He also focuses on the application of Process Analytical Technology (PAT) for real-time monitoring of critical quality attributes in TCM manufacturing.

🛠️Skills

He is proficient in a wide range of analytical techniques, including Liquid/Gas Chromatography-Mass Spectrometry (LC/GC-MS), Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Near-Infrared Spectroscopy (NIR), and Laser-Induced Breakdown Spectroscopy (LIBS). His expertise spans both laboratory-based and field-deployable technologies, enabling high-throughput and real-time analysis of complex herbal matrices.

🏆Awards

Zhiyong Zhang has made significant contributions to the field with over 20 peer-reviewed publications, including 12 SCI-indexed papers where he served as the first author or co-first author. He has also filed three patent applications related to his research innovations in TCM quality control and evaluation technologies.

📚 Publications

1. Journal of Food Composition and Analysis (2025)

Title: Investigating the impact of geographical origin and harvesting season on the quality of Hypericum perforatum L. using LC-MS, GC-MS, and ICP-MS technologies in conjunction with random forest model
Authors: Zhang Zhiyong, Qian Jiahe, Fang Guangpu, Chen Jingchao, Li Wenlong

2. Phytochemical Analysis (2025)

Title: A Machine Learning-Based Approach for the Prediction of Anticoagulant Activity of Hypericum perforatum L. and Evaluation of Compound Activity
Authors: Zhang Zhiyong, Nie Wennan, Zhang Yijing, Zhang Shule, Li Wenlong

Conclusion:

Dr. Zhiyong Zhang is highly suitable for the Research for Best Researcher Award. His achievements to date already place him among the most promising young researchers in the field of TCM. With continued support and opportunities to expand his global and practical influence, he is poised to make even greater contributions to science and society.

Ms. Aqsa Zafar Abbasi | Data Science | Women Researcher Award

Ms. Aqsa Zafar Abbasi, Institute of Space Technology, Pakistan

Ms. Aqsa Zafar Abbasi is a PhD candidate in Mathematics at the Institute of Space Technology, Pakistan, specializing in group theory, cryptography, and mathematical modeling. Her research focuses on the parametric exploration of homomorphisms of modular groups for finite generalized triangle groups, with applications in image encryption, substitution box design, and neural network-based cryptographic security. With six years of teaching experience, she has served as a visiting lecturer at multiple institutions, teaching courses in differential equations, discrete mathematics, graph theory, and engineering mathematics. She has published extensively in high-impact journals, contributing to research in cryptography, fuzzy algebra, and numerical simulations. Aqsa is proficient in MATLAB, Python, R, and computational intelligence techniques, making her a dedicated researcher in the fields of mathematics and data security.

Profile

Summary:

Ms. Aqsa Zafar Abbasi is an exceptional researcher in mathematical cryptography, data security, and computational mathematics. Her significant contributions to encryption techniques, strong publication record, and interdisciplinary expertise make her a strong candidate for the Research for Women Researcher Award. While she already has an impressive academic and research background, expanding collaborations, engaging in industry applications, and securing research funding could further elevate her contributions.

🎓 Education

Aqsa Zafar Abbasi is currently pursuing a PhD in Mathematics at the Institute of Space Technology, Islamabad, Pakistan, with an expected completion date in February 2025. Her research focuses on the parametric exploration of homomorphisms of modular groups for finite generalized triangle groups, with applications in cryptography and neural network-based encryption. She holds an MS in Mathematics from the same institution, completed in 2021, where she explored algebraic characteristics of bipolar-valued fuzzy subgroups and their applications in decision-making and S-box analysis. She also earned an MSc in Mathematics from PMAS Arid Agriculture University, Rawalpindi, with an excellent academic record.

💼Experience

With six years of teaching experience, Aqsa has served as a visiting lecturer at prestigious institutions, including Pir Mehr Ali Shah Arid Agriculture University, National Defence University, Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology (SZABIST), and the Institute of Space Technology, Islamabad. She has taught differential equations, discrete mathematics, graph theory, real analysis, linear algebra, and engineering mathematics to students in mathematics, software engineering, and computer science programs.

🔬Research Focus

Aqsa’s research expertise includes group theory, cryptography, algebraic geometry, numerical solutions of PDEs, and machine learning applications in mathematical modeling. She specializes in highly secure substitution box construction for encryption, particularly for image encryption and watermarking, using parameterized homomorphisms of modular groups. Additionally, she integrates neural networks with encryption schemes to enhance data security. Her research also extends to bipolar-valued fuzzy set theory, focusing on its applications in decision-making and cryptographic analysis.

Awards

Aqsa has gained recognition for her contributions to cryptography, algebraic structures, and numerical analysis, with publications in renowned scientific journals. Her research on mathematical modeling and encryption techniques has positioned her as a dedicated scholar in her field.

Skills

Aqsa possesses a diverse skill set, including mathematical modeling, computational intelligence, programming (C++, Python, MATLAB, R, Wolfram Mathematica), research writing, editing, and proofreading. She is also skilled in seminar organization, event management, leadership, and teamwork, making her a well-rounded academic professional.

Publications

Publication: A digital audio data protection method using parametric action of generalised triangle group on GF(28)
Authors: A.Z. Abbasi (Aqsa Zafar Abbasi), A. Rafiq (Ayesha Rafiq), B.M. Alshammari (Badr Mesned)
Journal: Ain Shams Engineering Journal
Year: 2025

Conclusion:

Ms. Aqsa Zafar Abbasi is a highly qualified and deserving candidate for the Research for Women Researcher Award. Her expertise in mathematical cryptography, interdisciplinary research, and teaching makes her a valuable asset to the academic community. With continued research, international collaborations, and industry engagement, she has the potential to make even greater contributions to her field.

Zhiyong Zhang | Data Science | Best Researcher Award