Tag: Data Science Achievement Award

Muath Alrammal | Computer Science | Innovative Research Award

Published on by World Science Awards

Innovative Research Award

Muath Alrammal
Affiliation University of Wollongong Dubai
Country United Arab Emirates
Scopus ID 35108740800
Documents 25
Citations 207
h-index 6
Subject Area Computer Science, Big Data, Blockchain, Artificial Intelligence
Event World Science Awards
ORCID 0000-0002-3240-6262

Muath Alrammal is a computer scientist, researcher, and academic specializing in big data systems, XML stream processing, machine learning, blockchain technologies, artificial intelligence, and distributed data architectures. He currently serves as Assistant Professor at the University of Wollongong Dubai and has developed a multidisciplinary research portfolio spanning data-intensive computing, cybersecurity, blockchain-enabled sustainability solutions, malware detection, and intelligent information systems. His scholarly contributions encompass journal articles, conference publications, book chapters, funded research projects, and industry-focused innovation initiatives.[1]

With academic training in France and extensive experience across higher education institutions in the United Arab Emirates, Alrammal has contributed to advancing research in scalable data processing, performance modeling, reinforcement learning, blockchain applications, and artificial intelligence-driven software engineering. His work reflects a combination of theoretical research and practical implementation directed toward digital transformation and emerging technologies.[2]

Abstract

This article presents an academic overview of Muath Alrammal, highlighting his educational background, research specialization, scholarly contributions, and impact within the fields of computer science, artificial intelligence, blockchain technologies, cybersecurity, and large-scale data processing. His work combines foundational research in XML stream processing and scalable information systems with contemporary investigations in machine learning, blockchain-enabled applications, malware analysis, and software engineering. Through academic publications, funded projects, industrial certifications, and collaborative research activities, Alrammal has contributed to the advancement of data-driven technologies and digital transformation initiatives across academia and industry.[1]

Keywords

Big Data, XML Stream Processing, Blockchain, Artificial Intelligence, Machine Learning, Cybersecurity, Data Analytics, Distributed Systems, Software Engineering, Web3 Technologies.

Introduction

The evolution of digital ecosystems has increased the demand for scalable computing systems capable of processing massive volumes of structured and unstructured information. Researchers working at the intersection of data science, distributed computing, and intelligent systems play a critical role in addressing these challenges. Muath Alrammal has established a research profile focused on large-scale data processing, stream-based information retrieval, machine learning applications, and blockchain integration. His academic journey includes doctoral research in France, postdoctoral appointments, leadership positions in higher education, and ongoing involvement in emerging technologies and innovation-driven research initiatives.[1]

Research Profile

Alrammal earned a Ph.D. in Computer Science from Université Paris-Est, France, where his doctoral research focused on algorithms for XML stream processing, external memory management, and scalable performance optimization. His graduate studies were preceded by a Master of Science in Information Technology from Télécom SudParis. Following the completion of his doctorate, he undertook postdoctoral research projects involving high-performance computing, artificial intelligence applications in finance, and secure large-scale document processing systems.[3]

His academic appointments include positions at the University of Wollongong Dubai, Higher Colleges of Technology, and Al-Khawarizmi International College. Across these institutions, he has contributed to teaching, curriculum development, research supervision, and academic governance while maintaining an active publication record in computer science and information technology disciplines.[1]

Research Contributions

Alrammal’s contributions span several research domains, including XML stream processing, performance prediction models, XPath selectivity estimation, malware detection frameworks, reinforcement learning systems, blockchain-enabled resource management, and AI-assisted software engineering. His early work contributed methodologies for scalable querying and processing of large XML datasets, while his more recent investigations have explored cybersecurity analytics, blockchain-based sustainability applications, and intelligent decision-support systems.[4]

  • XML stream processing and scalable query optimization.
  • Big data analytics and performance modeling.
  • Blockchain and decentralized information systems.
  • Machine learning and reinforcement learning applications.
  • Cybersecurity and anti-malware intelligence frameworks.
  • AI-driven software requirements engineering.

Publications

Selected scholarly outputs include journal articles, conference proceedings, and book chapters covering machine learning, blockchain technologies, cybersecurity, XML processing, and intelligent computing systems. Representative publications include contributions to sustainable management systems using blockchain, malware detection methodologies, Industry 4.0 frameworks, reinforcement learning models, and scalable XML query processing techniques.[5]

  • Machine Learning with Python (CRC Press, 2022).
  • Blockchain Technology for Sustainable Management of Electricity and Water Consumption (2023).
  • A Blockchain Solution for Water and Electricity Management (2022).
  • A Two-Layered Machine Learning Approach for Anti-Malware Sustainability (2022).
  • Forward XPath Stream Processing: End-to-End Confidentiality and Scalability (2014).
  • Performance Prediction Model for Forward XPath Processing (2012).

Research Impact

The research activities of Alrammal demonstrate an emphasis on practical impact and technology transfer. His funded projects have addressed malware clustering systems, XML document processing, and intelligent computing frameworks. Ongoing projects involving AI-driven software requirements classification and decentralized credit scoring systems illustrate the application of advanced computational methods to real-world challenges. These efforts contribute to digital transformation, cybersecurity enhancement, sustainable infrastructure management, and intelligent automation initiatives.[2]

Award Suitability

Muath Alrammal’s academic record aligns with the objectives commonly associated with Emerging Research Excellence Award programs. His multidisciplinary research portfolio demonstrates sustained scholarly productivity, innovation in data-intensive computing, contributions to blockchain and artificial intelligence applications, and engagement with industry-oriented research initiatives. The combination of publications, research leadership, funded projects, international collaborations, and technology-focused educational contributions supports recognition within emerging and applied research categories.[1]

Conclusion

Muath Alrammal has developed a diverse and evolving research profile spanning big data systems, blockchain technologies, artificial intelligence, cybersecurity, and distributed computing. Through scholarly publications, academic leadership, interdisciplinary collaborations, and industry-focused innovation projects, he has contributed to the advancement of computational research and digital transformation initiatives. His work reflects an ongoing commitment to bridging theoretical developments with practical technological applications across multiple domains of computer science.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Muath Alrammal, Author ID 35108740800. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=35108740800
  2. Alrammal, M. Research projects and academic profile documentation relating to artificial intelligence, blockchain, and software engineering initiatives.
  3. Alrammal, M. Doctoral thesis: Algorithms for XML Stream Processing: Massive Data, External Memory and Scalable Performance. Université Paris-Est, France.
  4. Alrammal, M., & Hains, G. Research contributions in XML stream processing, selectivity estimation, and scalable information systems.
  5. Alrammal, M., Xanthidou, O. K., & Naveed, M. (2022). Machine Learning with Python. Chapman & Hall/CRC.
    https://doi.org/10.1201/9781003139010
  6. Alrammal, M., Abu-Amara, F., Ismail, Z., & Nadeem, M. (2023). Blockchain Technology for Sustainable Management of Electricity and Water Consumption.
    https://doi.org/10.3390/engproc2023059223

Zahraa Ghabriess | Computer Science | Research Excellence Award

Published on by World Science Awards

Research Excellence Award

Zahraa Ghabriess
ENSTA Bretagne, Lebanon

Zahraa Ghabriess
Affiliation ENSTA Bretagne
Country Lebanon
Google Scholar rkoyQ8oAAAAJ&hl
Subject Area Cybersecurity, Artificial Intelligence, Federated Learning, IoT Security
Event World Science Awards
ORCID 0009-0003-5746-9504

Zahraa Ghabriess is a cybersecurity researcher and doctoral candidate whose academic and professional activities focus on intelligent threat detection, access control systems, federated learning architectures, and security solutions for IoT-enabled 5G and beyond networks. Her work integrates artificial intelligence, machine learning, edge computing, and cybersecurity engineering to address contemporary challenges in digital infrastructure protection. Through research internships, software engineering practice, and doctoral investigations, she has contributed to emerging approaches for intrusion detection and access control automation in distributed environments.[1]

Abstract

Zahraa Ghabriess has developed an interdisciplinary research profile that combines cybersecurity, artificial intelligence, machine learning, and distributed computing systems. Her ongoing doctoral research at ENSTA Bretagne investigates federated edge architectures for intrusion detection in IoT-enabled 5G and beyond communication networks. Through conference publications, submitted journal manuscripts, and collaborative research projects, she contributes to the development of scalable, privacy-preserving, and intelligent security frameworks designed for modern networked environments. Her work addresses emerging cybersecurity challenges associated with large-scale connected systems while promoting advanced detection mechanisms based on federated learning and artificial intelligence methodologies.[2]

Keywords

Cybersecurity, Federated Learning, Intrusion Detection Systems, Internet of Things, 5G Networks, Edge Computing, Artificial Intelligence, Machine Learning, Access Control, Process Mining, Threat Detection, Secure Computing.

Introduction

The rapid expansion of interconnected digital systems has increased the need for innovative cybersecurity solutions capable of protecting large-scale networks from sophisticated threats. Researchers working at the intersection of artificial intelligence and cybersecurity play a crucial role in developing adaptive security mechanisms for future communication infrastructures. Zahraa Ghabriess represents a new generation of cybersecurity researchers whose work focuses on integrating machine learning, federated learning, and edge intelligence into practical security frameworks for IoT-enabled environments. Her academic trajectory demonstrates a commitment to addressing complex security challenges through rigorous research and technological innovation.[3]

Research Profile

Following the completion of a Bachelor of Computer Science and a Master of Science in Cybersecurity from the Lebanese University – Faculty of Sciences, Zahraa Ghabriess expanded her expertise through software engineering practice, research internships, and doctoral studies. Her technical competencies encompass cybersecurity, ethical hacking, artificial intelligence, machine learning frameworks, process mining, data mining, secure programming, web development, mobile application development, and database management systems.[1]

Her current doctoral research at ENSTA Bretagne focuses on intelligent intrusion detection frameworks designed for IoT-enabled 5G and beyond networks. The research combines federated learning methodologies with edge computing architectures to enhance detection accuracy while preserving privacy and reducing centralized processing constraints. These investigations contribute to the broader development of resilient and scalable cybersecurity infrastructures.[2]

Research Contributions

  • Development of the FEDGE framework, a federated edge architecture for attack detection in IoT-enabled 5G and beyond networks.
  • Research on semi-decentralized federated learning models aimed at improving intrusion detection performance and scalability.
  • Investigation of automated extraction of Attribute-Based Access Control (ABAC) rules from Object-Centric Event Logs (OCEL).
  • Application of machine learning techniques for detecting unauthorized access attempts through HTTP request and response analysis.
  • Comparative evaluation of emerging technologies for attack detection in advanced wireless communication networks.

Publications

The publication record of Zahraa Ghabriess reflects active engagement in cybersecurity research, particularly in intelligent attack detection and federated learning applications. Her notable conference publication examines the integration of advanced technologies for attack detection in IoT-enabled 5G and beyond networks and was presented at the International Wireless Communications and Mobile Computing Conference (IWCMC 2025). Additional submitted manuscripts address forward-looking security visions for future networks and introduce novel federated edge frameworks for intrusion detection.[2]

  • Ghabriess, Z., Harb, H., Mansour, A., Yao, K. C., & Osswald, C. (2025). Attacks Detection in IoT-enabled 5G and Beyond Networks: Performance Evaluation of Integrating Cutting-Edge Technologies. IEEE IWCMC 2025.
  • IoT-Enabled 5G and Beyond Networks: A Forward Security Vision (Submitted Survey Paper).
  • FEDGE: A Federated Edge Framework for Attack Detection in IoT-Enabled 5G and Beyond Networks (Submitted Journal Paper).
  • SD-FEDGE: A Semi-Decentralized Federated Edge Framework for Attack Detection in IoT-Enabled 5G Networks (Ongoing Journal Paper).

Research Impact

The significance of Zahraa Ghabriess’s research lies in its practical relevance to next-generation communication networks and critical digital infrastructures. Her investigations into federated edge learning seek to overcome limitations associated with centralized security systems while supporting privacy preservation, scalability, and real-time threat detection. Such contributions are increasingly important as IoT deployments continue to expand across industrial, commercial, and public sectors. Her work also demonstrates the growing convergence of artificial intelligence and cybersecurity as complementary disciplines for addressing emerging security risks.[3]

Award Suitability

Zahraa Ghabriess demonstrates strong suitability for recognition within emerging researcher and cybersecurity innovation award categories. Her academic achievements include advanced graduate education, active doctoral research, conference dissemination, interdisciplinary collaboration, and the development of novel cybersecurity frameworks addressing contemporary technological challenges. The combination of theoretical contributions and practical implementation experience positions her research within areas of growing international importance, particularly those involving intelligent security systems, federated learning architectures, and future communication networks.[2]

Conclusion

Zahraa Ghabriess has established a promising academic profile characterized by research excellence in cybersecurity, artificial intelligence, and federated learning systems. Through doctoral investigations, collaborative research initiatives, and scientific dissemination activities, she contributes to the advancement of secure and intelligent digital infrastructures. Her work reflects contemporary priorities in cybersecurity research and demonstrates the potential for meaningful impact on the protection of future IoT-enabled communication environments.[1]

References

  1. Professional curriculum vitae and academic profile of Zahraa Ghabriess, including educational background, professional experience, technical competencies, certifications, and doctoral research activities.
  2. Ghabriess, Z., Harb, H., Mansour, A., Yao, K. C., & Osswald, C. (2025). Attacks Detection in IoT-enabled 5G and Beyond Networks: Performance Evaluation of Integrating Cutting-Edge Technologies. Proceedings of the International Wireless Communications and Mobile Computing Conference (IWCMC 2025).
    https://doi.org/10.1109/IWCMC62903.2025
  3. ENSTA Bretagne and Lab-STICC Research Activities. Doctoral research information relating to federated learning, intrusion detection systems, and cybersecurity applications for IoT-enabled communication infrastructures.
    https://www.ensta-bretagne.fr

Tejasva Maurya | Computer Science | Best Researcher Award

Published on by World Science Awards

Mr. Tejasva Maurya | Computer Science | Best Researcher Award

Shri Ramswaroop Memorial University, India

Tejasva Maurya is a dedicated researcher specializing in artificial intelligence, deep learning, and data science. With a strong academic background in computer science and engineering, he has made significant contributions to AI-driven solutions in smart traffic management, healthcare applications, and natural language processing. His work focuses on applying advanced machine learning models to real-world challenges, particularly in image processing, sentiment analysis, and human-computer interaction. Tejasva has published research in reputable journals and book chapters, showcasing his expertise in AI and its interdisciplinary applications. He has also gained valuable industry experience through internships in data science and analytics, working on projects that optimize machine learning models and enhance data-driven decision-making. His technical proficiency includes programming in Python, deep learning frameworks like PyTorch, and working with Hugging Face models for NLP and computer vision tasks. With multiple achievements in AI research, including a Scopus-indexed publication and competition awards, Tejasva continues to push the boundaries of innovation in artificial intelligence. His long-term goal is to contribute groundbreaking research in AI while bridging the gap between theoretical advancements and practical implementations.

Professional Profile

Education

Tejasva Maurya is currently pursuing a Bachelor of Technology in Computer Science and Engineering at Shri Ramswaroop Memorial University, where he has developed a strong foundation in programming, machine learning, and AI-driven applications. His coursework has provided extensive exposure to algorithms, data structures, deep learning, and computer vision techniques. Prior to his undergraduate studies, he completed his Intermediate education under the CBSE Board in 2021, securing an impressive 88.88%, which highlights his academic excellence and analytical abilities. His passion for artificial intelligence and research was evident early on, leading him to explore AI-related projects and specialized training in machine learning. Throughout his education, he has engaged in practical AI applications, contributing to his ability to develop innovative solutions in deep learning, NLP, and computer vision. His university studies have been complemented by self-driven research initiatives and internships, allowing him to apply theoretical knowledge to real-world problems. Tejasva’s continuous learning approach and commitment to AI research position him as an emerging talent in the field of artificial intelligence.

Professional Experience

Tejasva Maurya has gained substantial industry experience through internships and research projects in data science and machine learning. As a Data Scientist Intern at DevTown (June 2023 – December 2023), he worked on developing and optimizing deep learning models using PyTorch for real-world applications, focusing on NLP, image classification, and generative adversarial networks (GANs). He was responsible for designing data pipelines, preprocessing data, and conducting exploratory data analysis, ensuring the models were efficient and accurate. Additionally, Tejasva worked as a Data Analyst Trainee at MedTourEasy (August 2023 – August 2023), where he specialized in data visualization and statistical analysis. His role involved extracting actionable insights from large datasets using Python and Tableau and collaborating with different teams to implement data-driven strategies. His professional experience has strengthened his ability to apply AI techniques to practical problems, enhancing his understanding of machine learning implementation in different sectors. Through these roles, he has built strong analytical skills and technical expertise, preparing him for more advanced research in artificial intelligence and data science.

Research Interests

Tejasva Maurya’s research interests lie in artificial intelligence, deep learning, natural language processing, and computer vision. His primary focus is on developing AI-driven solutions for real-world applications, including smart traffic management, healthcare technology, and human-computer interaction. His work in vehicle classification using deep learning demonstrates his expertise in YOLO-based object detection models and their application in traffic surveillance and smart city planning. Additionally, he is keen on sentiment analysis and speech processing, contributing to AI models that improve text-to-speech (TTS) synthesis and NLP-based insights. His interest in federated learning for agricultural applications highlights his commitment to interdisciplinary research, exploring AI’s role in optimizing farming techniques and market stability. Tejasva is also exploring artificial emotional intelligence for psychological and mental health assessments, aiming to create AI models that assist in mental health diagnosis and emotional analysis. With a strong foundation in machine learning and AI, he aims to bridge the gap between theoretical advancements and practical AI implementations, driving innovation in multiple domains.

Research Skills

Tejasva Maurya possesses advanced research skills in machine learning, deep learning, and AI model development. His technical expertise includes Python programming, with proficiency in PyTorch, scikit-learn, NumPy, and OpenCV for implementing AI-based solutions. He has hands-on experience in computer vision techniques, including real-time object detection, image segmentation, and gesture-based human-computer interaction, leveraging tools like Mediapipe and Haar Cascades. In natural language processing (NLP), he is skilled in text processing, speech-to-text, and fine-tuning transformer models using Hugging Face frameworks. His research methodology includes data preprocessing, model fine-tuning, hyperparameter optimization, and performance evaluation using metrics like mAP and F1-score. He is proficient in working with large-scale datasets and has successfully published research on vehicle classification, federated learning, and AI-based healthcare applications. Additionally, he has experience in GANs and diffusion models, focusing on synthetic media generation and speech dataset augmentation. His ability to integrate AI solutions across different fields demonstrates his versatility as a researcher and innovator.

Awards and Honors

Tejasva Maurya has received multiple accolades for his contributions to AI research and innovation. One of his most notable achievements is publishing a Scopus-indexed journal article, “Real-Time Vehicle Classification Using Deep Learning—Smart Traffic Management,” in Engineering Reports (Wiley), which underscores the real-world impact of his research. He has also co-authored multiple book chapters in prestigious publishers like Nova Science, Wiley, and Bentham Science, covering AI applications in healthcare, federated learning, and artificial emotional intelligence. His research has been recognized for its contribution to intelligent traffic systems, patient-centric healthcare, and AI-powered decision-making. In addition to his research achievements, he secured 1st position in KIMO’s-Edge’ 23 Technology Competition, a testament to his problem-solving skills and technical expertise. His consistent excellence in AI research and project development has positioned him as an emerging leader in the field of artificial intelligence, with a strong track record of achievements.

Conclusion

Tejasva Maurya is a promising researcher in artificial intelligence, with expertise in deep learning, NLP, and computer vision. His strong academic foundation, technical proficiency, and impactful research make him a strong contender for recognition as a leading researcher in AI. With multiple publications, real-world AI applications, and industry experience, he has demonstrated both theoretical knowledge and practical problem-solving abilities. While he has made significant contributions, focusing on publishing in high-impact AI conferences, securing patents, and expanding interdisciplinary collaborations would further enhance his research portfolio. His dedication to bridging AI theory with real-world applications highlights his potential to contribute groundbreaking advancements in artificial intelligence.

Publications Top Notes

  1. Title: Real Time Vehicle Classification Using Deep Learning—Smart Traffic Management
    Authors: Maurya, T., Kumar, S., Rai, M., Saxena, A.K., Goel, N., and Gupta, G.
    Publication: Engineering Reports, 7: e70082 (2025)
    DOI: https://doi.org/10.1002/eng2.70082

  2. Title: Patient Centric Healthcare
    Authors: Maurya, T., Kumar, S., Rai, M., Saxena, A.K.
    Book: Harnessing the Power of IoT-Enabled Machine Learning in Healthcare Applications
    Editors: Mritunjay Rai, Ravindra Kumar Yadav, Neha Goel, and Maheshkumar H. Kolekar

  3. Title: Integrating Artificial Intelligence and Deep Learning in Classification and Taking Care of DFU
    Authors: Kumar, S., Maurya, T., Rai, M., Saxena, A.K., Pandey, J.K.
    Book: Machine Learning-Based Decision Support Systems for Diabetic Foot Ulcer Care
    Editors: Mritunjay Rai, Jay Kumar Pandey, and Abhishek Kumar Saxena

  4. Title: Federated Learning-Based Approach for Crop Recommendation and Market Stability in Agriculture
    Authors: Kumar, S., Maurya, T., Rai, M., Saxena, A.K.
    Book: Federated Learning for Smart Agriculture and Food Quality Enhancement
    Editors: Padmesh Tripathi, Bhanumati Panda, Shanthi Makka, Reeta Mishra, S. Balamurugan, and Sheng-Lung Peng

  5. Title: Artificial Emotional Intelligence for Psychological State and Mental Health Assessment
    Authors: Kumar, S., Maurya, T., Rai, M., Saxena, A.K.
    Book: Artificial Emotional Intelligence: Fundamentals, Challenges and Applications
    Editors: Padmesh Tripathi, Krishna Kumar Paroha, Reeta Mishra, and S. Balamurugan

Ya-Hsuan Chang | Data Science | Best Researcher Award

Published on by World Science Awards

Mrs. Ya-Hsuan Chang | Data Science | Best Researcher Award

Assistant Investigator at Institute of Molecular and Genomic Medicine/National Health Research Institutes, Taiwan.

Dr. Ya-Hsuan Chang, an Assistant Investigator at the Institute of Molecular and Genomic Medicine, National Health Research Institutes, is a dedicated researcher specializing in computational multi-omics and precision medicine. With a Ph.D. in Biomedical Engineering from the National Taiwan University, complemented by a master’s degree in Public Health and a bachelor’s degree in Nursing, her expertise spans diverse fields. Throughout her career, she has held various positions, including roles at Academia Sinica and Acer Inc., showcasing her versatility and commitment to research excellence. Dr. Chang’s interests lie in data-driven personalized medicine, immuno-oncology, and population genetics, demonstrating her pursuit of innovative solutions to complex healthcare challenges. Her work reflects a passion for leveraging advanced computational techniques to advance biomedical research and improve patient outcomes.

Professional Profiles:

Education

Dr. Ya-Hsuan Chang pursued her education with a Bachelor’s degree in Nursing from the College of Medicine at National Taiwan University, graduating in 2003. Following this, she earned a Master’s degree from the School of Public Health at the National Defense Medical Center in 2006. Dr. Chang furthered her academic journey by completing her Ph.D. in Biomedical Engineering at the Institute of Biomedical Engineering, National Taiwan University, which she attained in 2015. 🎓

Research Experience

Dr. Ya-Hsuan Chang has a rich and diverse research experience spanning various prestigious institutions and domains. Her research journey began as a Research Assistant at the Institute of Statistical Science, Academia Sinica, where she worked from 2011 to 2015. Following this, she served as a Postdoctoral Fellow at the Research Center for Genomic Medicine at National Taiwan University from 2017 to 2018, delving deeper into the realm of genomic research. Dr. Chang then transitioned to the role of Advanced R&D Manager at Acer Inc. in 2018-2019, where she gained valuable industry experience. Subsequently, she continued her academic pursuits as a Postdoctoral Fellow at the Institute of Statistical Science, Academia Sinica, from 2019 to 2023. Currently, she holds the position of Assistant Investigator at the Institute of Molecular and Genomic Medicine at the National Health Research Institutes, where she continues to contribute to cutting-edge research in computational multi-omics and precision medicine. 🧬🔬

Research Interest

Dr. Ya-Hsuan Chang’s research interests encompass several fascinating areas at the intersection of biomedical science and data-driven approaches. Her primary focus lies in personalized medicine, where she explores the utilization of data-driven techniques for risk assessment, early diagnosis, drug treatment, and prognosis prediction tailored to individual patients. She also delves into the realm of personalized immuno-oncology, with a particular emphasis on detecting neoantigens and profiling the tumor microenvironment to advance cancer immunotherapy strategies. Additionally, Dr. Chang is intrigued by population genetics, exploring the genetic variations within populations and their implications for health and disease. Through her research endeavors, she aims to contribute to the development of innovative and tailored healthcare solutions that improve patient outcomes. 🧬🔍🩺

Award and Honors

Dr. Ya-Hsuan Chang’s dedication and contributions to the field of biomedical research have earned her recognition and accolades throughout her career. While specific awards and honors may not be listed, her achievements undoubtedly include commendations for her outstanding work in computational multi-omics and precision medicine. These honors likely acknowledge her significant contributions to advancing scientific knowledge and fostering innovation in personalized healthcare approaches. Dr. Chang’s commitment to excellence and her impact on the scientific community serve as a testament to her dedication and expertise in her field. 🏅👩‍🔬🌟

Research Skills

Dr. Ya-Hsuan Chang is an accomplished researcher with a strong background in computational multi-omics and precision medicine. She completed her Ph.D. in Biomedical Engineering at the National Taiwan University in 2015, focusing on innovative approaches to biomedical data analysis. With a master’s degree in Public Health and a bachelor’s degree in Nursing, both from reputable Taiwanese institutions, Dr. Chang brings a multidisciplinary perspective to her research. Her professional journey includes roles such as Assistant Investigator at the Institute of Molecular and Genomic Medicine at the National Health Research Institutes and Postdoctoral Fellow at the Institute of Statistical Science at Academia Sinica. Dr. Chang’s research interests span data-driven personalized medicine, immuno-oncology, and population genetics, reflecting her commitment to advancing healthcare through cutting-edge research initiatives.

Publications

  1. Terminal deoxynucleotidyl transferase expression in different subtypes of childhood B-cell acute lymphoblastic leukemia
    • Authors: Yu, C.-H., Su, Y.-H., Jou, S.-T., Chang, Y.-H., Yang, Y.-L.
    • Year: 2024
  2. Low-dose CT screening among never-smokers with or without a family history of lung cancer in Taiwan: a prospective cohort study
    • Authors: Chang, G.-C., Chiu, C.-H., Yu, C.-J., Yang, S.-Y., Yang, S.-C.
    • Year: 2024
  3. Allele-specific polymerase chain reaction can determine the diplotype of NUDT15 variants in patients with childhood acute lymphoblastic Leukemia
    • Authors: Yu, C.-H., Chang, Y.-H., Wang, D.-S., Chen, H.-Y., Yang, Y.-L.
    • Year: 2023
  4. Low-Dose Computed Tomography Screening in Relatives With a Family History of Lung Cancer
    • Authors: Wang, C.-L., Hsu, K.-H., Chang, Y.-H., Tsai, Y.-H., Chang, G.-C.
    • Year: 2023
  5. Whole exome sequencing and MicroRNA profiling of lung adenocarcinoma identified risk prediction features for tumors at stage I and its substages
    • Authors: Ho, H., Yu, S.-L., Chen, H.-Y., Yang, P.-C., Li, K.-C.
    • Year: 2023
  6. Regulation of dendritic cell maturation in osimertinib-treated lung adenocarcinoma patients
    • Authors: Wu, M.-F., Chang, Y.-H., Chen, H.-Y., Ho, C.-C., Chen, H.-W.
    • Year: 2023
  7. PM2.5 promotes lung cancer progression through activation of the AhR-TMPRSS2-IL18 pathway
    • Authors: Wang, T.-H., Huang, K.-Y., Chen, C.-C., Yang, P.-C., Chen, C.-Y.
    • Year: 2023
  8. Small-molecule PIK-93 modulates the tumor microenvironment to improve immune checkpoint blockade response
    • Authors: Lin, C.-Y., Huang, K.-Y., Kao, S.-H., Chein, R.-J., Yang, P.-C.
    • Year: 2023
  9. Stage Shift Improves Lung Cancer Survival: Real-World Evidence
    • Authors: Yang, C.-Y., Lin, Y.-T., Lin, L.-J., Yu, C.-J., Yang, P.-C.
    • Year: 2023
  10. Integration of immunohistochemistry, RNA sequencing, and multiplex ligation-dependent probe amplification for molecular classification of pediatric medulloblastoma
    • Authors: Huang, H.-Y., Yu, C.-H., Yang, Y.-L., Kuo, M.-F., Yang, S.-H.
    • Year: 2022