Dr. Zeinab Shahbazi | Computer Science | Best Researcher Award

Published on by World Top Scientists

Dr. Zeinab Shahbazi | Computer Science | Best Researcher Award

Senior Lecturer at Kristianstad University, Sweden

Dr. Zeinab Shahbazi is an accomplished researcher specializing in Reinforcement Learning, Deep Learning, Natural Language Processing, Blockchain, and Knowledge Discovery. With a Ph.D. in Computer Engineering from Jeju National University, South Korea, she has over eight years of research experience in AI and data-driven technologies. Dr. Shahbazi has held postdoctoral positions in Spain and Sweden and is currently a Senior Lecturer in AI at Kristianstad University. Her research focuses on enhancing state-of-the-art architectures and developing innovative solutions in software-based intelligent systems. She has been recognized with several academic awards, including a Presidential Award and Best Paper Presentation honors. Fluent in multiple languages and technically skilled in programming and data systems, she actively contributes as a reviewer for high-impact journals. Her international collaborations and funded research projects reflect her commitment to advancing AI applications. Dr. Shahbazi is a dedicated and forward-thinking researcher making significant contributions to the field of computer science.

Professional Profile 

Google Scholar

Education

Dr. Zeinab Shahbazi holds a Ph.D. in Computer Engineering from Jeju National University, South Korea, where she completed her dissertation on cryptocurrency price prediction using blockchain frameworks, graduating with an impressive CGPA of 4.32/4.5. She also earned a Master’s degree in Computer Engineering from Chonbuk National University, Korea, with a thesis on deep learning techniques for paragraph focus analysis. Her foundational education includes a Bachelor’s degree in Computer Engineering from Pooyesh University in Iran. Throughout her academic journey, she received several scholarships and honors, reflecting her consistent academic excellence. Her education has been firmly rooted in AI, software systems, and intelligent technologies, providing her with a robust theoretical and practical grounding. This strong academic background has played a pivotal role in shaping her as a multidisciplinary researcher with global exposure, capable of addressing complex problems in AI and data science with both depth and innovation.

Professional Experience

Dr. Zeinab Shahbazi has accumulated diverse international professional experience in research and academia. She is currently a Senior Lecturer in Artificial Intelligence at Kristianstad University, Sweden. Prior to this, she held postdoctoral researcher positions at Halmstad University in Sweden and at the BCN-AIM Lab at the University of Barcelona in Spain. Her work has consistently focused on applied AI, reinforcement learning, and blockchain-based systems. Dr. Shahbazi has also led and participated in international research collaborations, notably securing a Vinnova-funded international staff exchange project with a partner institution in South Korea. Her career path showcases her ability to transition between theoretical research and practical implementations, including experience in advanced programming, system architecture, and AI model development. These roles have enabled her to contribute to both the academic and industrial applications of intelligent technologies, while also strengthening her leadership and mentoring capabilities in multidisciplinary, multicultural environments.

Research Interest

Dr. Zeinab Shahbazi’s research interests are deeply rooted in intelligent computing systems, with a focus on Reinforcement Learning, Deep Learning, Natural Language Processing (NLP), Blockchain, Knowledge Discovery, and their integration within modern technological ecosystems such as IoT, edge computing, and big data platforms. Her core research ambition lies in improving existing AI models and architectures, addressing their limitations, and introducing novel components to enhance performance and applicability. She has made notable contributions to the software aspects of AI, particularly through her work on knowledge-driven systems and blockchain-based data prediction. Dr. Shahbazi combines theoretical advancements with practical implementations, bridging the gap between academic research and real-world applications. Her multidisciplinary focus reflects a keen interest in innovation, system integration, and cross-domain problem-solving. This makes her work highly relevant to both academic audiences and industry stakeholders interested in deploying intelligent, data-driven systems for practical and scalable use.

Award and Honor

Dr. Zeinab Shahbazi has received multiple awards and honors in recognition of her academic excellence and research contributions. During her Ph.D. at Jeju National University, she was awarded the prestigious Presidential Award for distinguished research publications. She also received a university research grant in 2021 for her outstanding output during 2019–2020. Earlier in her academic career, she was a recipient of the BK government scholarship and multiple semester-based scholarships during her Master’s studies at Chonbuk National University. Her early academic promise was also recognized with a government-funded scholarship during her undergraduate studies in Iran. Additionally, she won the Best Paper and Presentation Award at the ITEC Conference in 2019, further solidifying her reputation in the research community. These honors demonstrate a consistent trajectory of excellence, reflecting both the quality and impact of her research work, as well as her ability to compete and stand out in international academic environments.

Conclusion

Dr. Zeinab Shahbazi exemplifies a dynamic and impactful researcher in the field of computer science, particularly in AI, machine learning, and data-driven systems. Her strong educational background, diverse international research experience, and cross-disciplinary expertise make her a well-rounded academic and innovator. Her ability to secure research funding, collaborate internationally, and publish high-quality work underlines her potential for long-term academic leadership. Recognized through various awards and honors, she has demonstrated excellence not only in individual performance but also in contributing to the broader scientific community through peer review and collaboration. Fluent in multiple languages and culturally adaptive, Dr. Shahbazi brings global perspective and technical depth to every role she undertakes. With a forward-thinking mindset and a commitment to advancing the state of AI, she stands as a strong candidate for high-level recognitions such as the Best Researcher Award and is poised to continue making meaningful contributions to academia and beyond.

Publications Top Notes

  • Title: Integration of blockchain, IoT and machine learning for multistage quality control and enhancing security in smart manufacturing
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2021
    Citations: 187

  • Title: A procedure for tracing supply chains for perishable food based on blockchain, machine learning and fuzzy logic
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2020
    Citations: 140

  • Title: Towards a secure thermal-energy aware routing protocol in wireless body area network based on blockchain technology
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2020
    Citations: 123

  • Title: Smart manufacturing real-time analysis based on blockchain and machine learning approaches
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2021
    Citations: 72

  • Title: Toward improving the prediction accuracy of product recommendation system using extreme gradient boosting and encoding approaches
    Authors: Z. Shahbazi, D. Hazra, S. Park, Y.C. Byun
    Year: 2020
    Citations: 68

  • Title: Improving transactional data system based on an edge computing–blockchain–machine learning integrated framework
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2021
    Citations: 64

  • Title: Product recommendation based on content-based filtering using XGBoost classifier
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2019
    Citations: 64

  • Title: Agent-based recommendation in E-learning environment using knowledge discovery and machine learning approaches
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2022
    Citations: 63

  • Title: Fake media detection based on natural language processing and blockchain approaches
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2021
    Citations: 63

  • Title: Improving the cryptocurrency price prediction performance based on reinforcement learning
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2021
    Citations: 60

  • Title: Machine learning-based analysis of cryptocurrency market financial risk management
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2022
    Citations: 58

  • Title: Lithium-ion battery estimation in online framework using extreme gradient boosting machine learning approach
    Authors: S. Jafari, Z. Shahbazi, Y.C. Byun, S.J. Lee
    Year: 2022
    Citations: 58

  • Title: Blockchain-based event detection and trust verification using natural language processing and machine learning
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2021
    Citations: 51

  • Title: Knowledge discovery on cryptocurrency exchange rate prediction using machine learning pipelines
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2022
    Citations: 42

  • Title: Lithium-ion battery health prediction on hybrid vehicles using machine learning approach
    Authors: S. Jafari, Z. Shahbazi, Y.C. Byun
    Year: 2022
    Citations: 36

Shishir Tewari | Computer Science | Technology and Innovation Leadership Award

Published on by World Top Scientists

Mr. Shishir Tewari | Computer Science | Technology and Innovation Leadership Award

Senior Manager, Data Engineering at Procore Technologies, United States

Shishir Tewari is a seasoned technology leader with over 19 years of experience driving innovation in data engineering, data warehousing, and analytics across top-tier organizations such as Google, Amazon, Morgan Stanley, and Microsoft. He currently leads strategic data initiatives at Procore Technologies, where he has spearheaded the development of AI/ML-driven platforms, cloud migrations, and real-time analytics systems. Known for his expertise in building scalable, high-performance data solutions, Shishir has successfully led global engineering teams and transformed complex data ecosystems on AWS, GCP, and Databricks. His technical vision, operational excellence, and commitment to data quality and governance have consistently delivered measurable business value. Shishir’s continuous pursuit of innovation and deep cross-functional leadership make him a standout contributor in the technology landscape. With a strong foundation in data science, cloud architecture, and team mentorship, he exemplifies the qualities of a forward-thinking, impact-driven technology leader worthy of recognition.

Professional Profile 

Google Scholar

Education

Shishir Tewari holds a Bachelor of Technology in Information Technology from U.P.T.U., India, graduating in 2006. Demonstrating a commitment to lifelong learning and innovation, he further enhanced his credentials with a specialization in Data Science and Analytics from Rutgers University, New Jersey, in 2018–2019. This advanced academic training equipped him with modern analytical techniques, machine learning algorithms, and statistical modeling—skills that have been instrumental in his professional success. His educational background lays a strong foundation for his technical leadership, blending theoretical knowledge with real-world application. The combination of engineering fundamentals and data science expertise positions Shishir as a well-rounded technology leader who can bridge the gap between innovation and implementation in enterprise environments.

Professional Experience

Shishir Tewari brings over 19 years of robust experience across global technology firms, including Google, Amazon, Morgan Stanley, Microsoft, and currently, Procore Technologies. His career spans technical leadership, large-scale data architecture, and cloud-native platform innovation. At Google, he led a global team optimizing financial data pipelines and infrastructure. At Amazon, he designed high-performance advertising data systems, enabling substantial revenue impact. At Procore, he has driven major initiatives including AI/ML-powered data platforms and cloud migrations. His ability to manage large engineering teams, align data strategy with business goals, and optimize performance at scale reflects his leadership maturity. Shishir’s diverse experience across industries—finance, tech, construction, and advertising—gives him a unique, cross-sector perspective on data-driven transformation.

Research Interest

Shishir Tewari’s research interests lie at the intersection of big data engineering, AI/ML-driven analytics, and cloud computing. He is particularly passionate about optimizing large-scale data systems for performance, governance, and real-time decision-making. With practical expertise in cloud platforms like AWS, GCP, and Databricks, his focus is on leveraging modern data stacks and open-source technologies to power next-generation analytics and automation. He is also interested in the application of machine learning for master data management, anomaly detection, and predictive modeling within business intelligence ecosystems. While not rooted in academic publishing, his work consistently applies research principles to solve real-world business problems, delivering measurable impact. Future interests include exploring the integration of generative AI with enterprise data platforms and advancing data democratization through self-service analytics tools.

Award and Honor

While specific awards and honors are not listed in his profile, Shishir Tewari’s consistent elevation to senior technical and leadership roles in globally respected organizations serves as a testament to his excellence and recognition within the industry. Being entrusted with mission-critical projects at Google, Amazon, and Morgan Stanley speaks to his reliability, vision, and execution skills. His role in leading high-visibility initiatives such as financial data certification, AI/ML-driven analytics platforms, and major cloud migrations reflects the high degree of trust and credibility he commands. He has likely received internal accolades for his contributions to performance optimization, cost reduction, and innovation. A nomination for a Technology and Innovation Leadership Award would further formalize and honor his significant contributions to data-driven transformation and technological advancement in enterprise settings.

Conclusion

Shishir Tewari exemplifies the qualities of a forward-thinking technology leader, with deep expertise in data engineering, cloud architecture, and strategic innovation. His two-decade-long career reflects a commitment to excellence, from hands-on development to executive-level leadership. With advanced training in data science, he brings both theoretical rigor and practical vision to his work. His impactful roles at top-tier organizations demonstrate his ability to lead cross-functional teams, optimize large-scale systems, and implement transformative technologies. Passionate about leveraging AI/ML and cloud platforms to drive business value, Shishir’s professional journey is marked by continuous learning and measurable outcomes. He stands out as a prime candidate for recognition through a Technology and Innovation Leadership Award, not only for his technical contributions but also for his ability to inspire, mentor, and lead organizations into the future of data-driven innovation.

Publications Top Notes

  1. Title: AI Powered Data Governance – Ensuring Data Quality and Compliance in the Era of Big Data
    Authors: S. Tewari
    Year: 2025

  2. Title: Operationalizing Explainable AI in Business Intelligence: A Blueprint for Transparent Enterprise Analytics
    Authors: A. Chitnis, S. Tewari
    Year: 2024

  3. Title: AI and Multi-Cloud Compliance: Safeguarding Data Sovereignty
    Authors: S. Tewari, A. Chitnis
    Year: 2024

  4. Title: Scalable Metadata Management in Data Lakes Using Machine Learning
    Authors: S. Tewari
    Year: 2023
    Citation: (Update needed)

  5. Title: AI-Powered Financial Forecasting: Enhancing Accuracy with Machine Learning in Enterprise System
    Authors: S. Tewari
    Year: 2023)

  6. Title: Detecting Data Drift and Ensuring Observability with Machine Learning Automation
    Authors: A. Chitnis, S. Tewari
    Year: 2022

  7. Title: Anomaly Detection in Large Scale Data Platforms with Machine Learning
    Authors: S. Tewari
    Year: 2022

  8. Title: Leveraging Graph Based Machine Learning to Analyze Complex Enterprise Data Relationships
    Authors: S. Tewari, A. Chitnis
    Year: 2021

Feixiang Li | Computer Science | Best Researcher Award

Published on by World Top Scientists

Dr. Feixiang Li | Computer Science | Best Researcher Award

Senior Eengineer at The 15th Research Institute of China Electronics Technology Corporation, China

Feixiang Li is a dedicated researcher and senior engineer specializing in Mobile Edge Computing, Software Defined Networks (SDN), and Evolutionary Algorithms. He earned his Ph.D. from Beijing University of Technology in 2020 and currently holds a senior engineering position at the 15th Research Institute of China Electronics Technology Corporation. His research has resulted in numerous publications in prestigious, high-impact journals such as IEEE Transactions on Industrial Informatics and IEEE Transactions on Mobile Computing. Feixiang has demonstrated a strong ability to bridge theoretical research with practical applications, contributing to fields essential to next-generation communication technologies. His work showcases consistent academic productivity, interdisciplinary problem-solving, and real-world relevance. While there is potential to expand international collaborations and increase innovation leadership, his achievements to date mark him as a significant contributor to his field. Feixiang Li stands out as a promising candidate for honors recognizing research excellence in computer science and telecommunications.

Professional Profile

ORCID Profile

Education

Feixiang Li completed his Ph.D. in Computer Science at Beijing University of Technology in June 2020. His doctoral research focused on Mobile Edge Computing and Software Defined Networks, integrating theoretical frameworks with real-world computing challenges. His academic training equipped him with a solid foundation in advanced algorithms, optimization methods, and emerging communication technologies. During his Ph.D., he began publishing in high-impact journals and enaging in collaborative research with leading scholars in the field. His academic journey reflects a strong emphasis on applied research and innovative problem-solving in the domain of network systems and intelligent computing. This solid educational background has not only shaped his technical expertise but also laid the groundwork for his ongoing contributions to both academia and industry. The combination of deep theoretical knowledge and practical insights defines his educational experience and positions him well for leading-edge research in evolving digital infrastructure and smart network systems.

Professional Experience

After earning his Ph.D., Feixiang Li joined the 15th Research Institute of China Electronics Technology Corporation as an engineer in July 2020. His role focused on the development and optimization of network systems, with a particular emphasis on edge computing solutions and SDN architecture. In November 2022, he was promoted to Senior Engineer, reflecting recognition of his technical leadership and innovative contributions. In this capacity, Feixiang has worked on high-impact projects with national relevance, integrating academic research into practical implementations that advance China’s communication technology infrastructure. His experience bridges the gap between academic innovation and industrial application, allowing him to contribute meaningfully to both spheres. With a strong command of evolving digital technologies and hands-on experience in designing and deploying scalable systems, he continues to push the boundaries of what is technically possible in his field. His professional trajectory demonstrates steady growth, leadership, and a commitment to innovation-driven development.

Research Interest

Feixiang Li’s research interests lie at the intersection of Mobile Edge Computing, Software Defined Networks (SDN), and Evolutionary Algorithms. His work addresses critical challenges in network optimization, resource allocation, and intelligent control in dense communication environments. He has published influential studies on computation offloading using game theory and auction-based models, reflecting his expertise in both algorithm design and systems thinking. Feixiang is particularly interested in how emerging network paradigms like SDN and edge computing can be made more efficient and adaptive through intelligent algorithms. His research not only contributes theoretical models but also delivers practical tools for enhancing communication systems in the era of IoT and 5G. These interests position him at the forefront of digital infrastructure innovation. As networks become more complex and data-driven, his work provides scalable, intelligent solutions to meet future demands, reinforcing his role as a vital contributor to the advancement of modern communication technologies.

Award and Honor

While specific individual awards and honors are not listed in the provided resume, Feixiang Li’s track record reflects recognition through peer-reviewed publications in high-impact journals such as IEEE Transactions on Industrial Informatics and IEEE Transactions on Mobile Computing. These publications, often co-authored with respected researchers, indicate scholarly recognition and professional credibility in the field of computer science and communication networks. His promotion to Senior Engineer at a prominent national research institute also suggests internal recognition of his expertise and leadership. Participation in major international conferences and acceptance of his work in top-tier venues serve as further evidence of his standing within the academic and professional community. Although formal awards are not explicitly mentioned, the quality, volume, and impact of his research contributions suggest a strong foundation for future honors and positions him as a valuable candidate for accolades such as the Best Researcher Award or other academic distinctions in technology and engineering.

Conclusion

Feixiang Li is a highly capable researcher and engineer whose work bridges academic innovation and real-world technology implementation. With a strong educational foundation, extensive experience in a national research institute, and a focused research agenda in high-impact areas like edge computing and SDNs, he stands out as a leader in his field. His publications in top-tier journals and his growing professional responsibilities underscore his commitment to advancing the state of digital communication systems. While there is room to expand his global research network and innovation leadership, his current achievements reflect a robust trajectory of growth, influence, and potential. Feixiang’s career exemplifies the integration of theoretical rigor with applied engineering, making him a strong candidate for awards and honors that recognize excellence in research and technological innovation. As he continues to evolve professionally and academically, he is well-positioned to make further meaningful contributions to the future of intelligent networked systems.

Publications Top Notes

  1. Title: A Deep Reinforcement Learning-Based Topology Optimisation Method for Distributed Trial Networks

  2. Title: A Transformer-GRU-Based Edge Computing Method for Vessel Trajectory Prediction

  3. Title: Constant-Time Discrete Gaussian Sampling for Edge Computing Based on DPWGAN

  4. Title: Semi-supervised Remote Sensing Image Classification for Edge Computing via Contrastive Learning

  1. Title: Collaborative Computation Offloading and Resource Management in Space–Air–Ground Integrated Networking

  2. Title: Intelligent Computation Offloading Mechanism with Content Cache in Mobile Edge Computing

  3. Title: Auction Design for Edge Computation Offloading in SDN-Based Ultra Dense Networks

    • Authors: Feixiang Li, Haipeng Yao, Jun Du, Chunxiao Jiang, Zhu Han, Yunjie Liu

    • Journal: IEEE Transactions on Mobile Computing

    • Year: 2022

    • DOI: 10.1109/TMC.2020.3026319

  4. Title: Stackelberg Game-Based Computation Offloading in Social and Cognitive IIoT

    • Authors: Feixiang Li, Haipeng Yao, Jun Du, Chunxiao Jiang, Yi Qian

    • Journal: IEEE Transactions on Industrial Informatics

    • Year: 2020

    • DOI: 10.1109/TII.2019.2961662

  5. Title: Multi-Controller Resource Management for Software-Defined Wireless Networks

    • Authors: Feixiang Li, Xiaobin Xu, Haipeng Yao, Jingjing Wang, Chunxiao Jiang, Song Guo

    • Journal: IEEE Communications Letters

    • Year: 2019

    • DOI: 10.1109/LCOMM.2019.2891527

    • Citations: 18 (Scopus)

  6. Title: Bat Algorithm with Principal Component Analysis

    • Authors: Zhihua Cui, Feixiang Li, Wensheng Zhang

    • Journal: International Journal of Machine Learning and Cybernetics

    • Year: 2019

    • DOI: 10.1007/s13042-018-0888-4