Jia Kaiewei | Artificial Intelligence | Best Scholar Award

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Dr. Jia Kaiewei | Artificial Intelligence | Best Scholar Award

Professor at Liaoning Technical University, Huludao, China

Kaiwei Jia is an accomplished academician and researcher currently serving as a Professor and Doctoral Supervisor in the field of Management Science and Engineering. He also holds the role of Vice Dean at the School of Business Administration, Liaoning Technical University. His academic journey is marked by extensive contributions to teaching, research, and institutional development. As a core member of the Liaoning Provincial Teaching Guidance Committee for Finance, he plays a significant role in shaping the financial education framework in the region. With a background in Economics and Statistics, Professor Jia has emerged as a thought leader in financial econometrics and policy research. His career is defined by a blend of theoretical insight and empirical rigor, and he has guided numerous graduate and doctoral students in their academic endeavors. Through his sustained commitment to academic excellence and administrative leadership, he has made substantial contributions to the academic community and the broader field of finance and economics.

Profile

Scopus

Education

Kaiwei Jia’s educational background is deeply rooted in economics and statistics. He earned his Ph.D. in Economics after completing a rigorous postgraduate program that emphasized macroeconomic policy, financial analysis, and quantitative methods. Subsequently, he undertook postdoctoral research in Statistics, where he refined his understanding of data interpretation, econometric modeling, and the application of statistical methodologies to economic problems. This interdisciplinary training has provided him with a comprehensive toolkit for analyzing complex economic phenomena. His academic progression reflects a strong emphasis on research-driven education, equipping him with both theoretical and practical skills. His transition from postgraduate studies to postdoctoral research marked a significant shift in his academic career, allowing him to delve deeper into areas such as financial econometrics, risk modeling, and empirical policy analysis.

Experience

Throughout his career, Professor Jia has maintained an unwavering commitment to teaching and mentoring. He has designed and delivered undergraduate, master’s, and doctoral-level courses in Econometrics, Financial Risk Management, Financial Econometrics, and Financial Data Analysis. His lectures are known for their analytical depth and emphasis on real-world application, which have earned him the respect of both peers and students. Beyond the classroom, he has played a pivotal role in curriculum development and academic governance at Liaoning Technical University. As Vice Dean, he has led several institutional initiatives aimed at enhancing academic quality and fostering innovation in finance education. Additionally, his membership in the Liaoning Provincial Teaching Guidance Committee for Finance has enabled him to influence regional academic standards, ensuring that finance education remains aligned with contemporary global developments.

Research Interest

Professor Jia’s research interests span a diverse array of topics within economics and finance. He focuses on financial stability and risk management, particularly the dynamics of financial contagion and systemic risk. His work explores the governance and risk prevention mechanisms in financial institutions, combining institutional theory with quantitative modeling. Additionally, he is deeply engaged in the study of monetary policy theory and methodology, emphasizing both the rules-based and discretionary approaches to macroeconomic regulation. His research extends to econometric methods, where he utilizes advanced statistical techniques to analyze financial and economic data. More recently, he has contributed to emerging areas such as green finance and climate finance, investigating how environmental factors intersect with financial risk and investment decisions. His multidisciplinary research approach integrates macroeconomic theory, quantitative analysis, and policy insights.

Award

In recognition of his scholarly achievements and academic leadership, Professor Jia has received several prestigious awards. He was honored with the First Prize in the 7th Liaoning Provincial Outstanding Achievement Award in Statistical Sciences, which acknowledges innovative contributions in statistical research. He also received the Second Prize in the Liaoning Provincial Philosophy and Social Science Achievement Award for his impactful work in economics and financial policy. These accolades reflect both the quality and societal relevance of his research, highlighting his role as a leading scholar in his field. His award-winning work has contributed to enhancing the understanding of financial risk, policy formulation, and statistical analysis at both regional and national levels.

Publication

Kaiwei Jia has published more than 30 academic papers in respected journals indexed by SSCI and CSSCI. His recent works reflect his ongoing dedication to cutting-edge research. In 2023, he co-authored “Did the ‘double carbon’ policy improve the green total factor productivity of iron and steel enterprises? A quasi-natural experiment based on carbon emission trading pilot,” published in Frontiers in Energy Research, exploring policy impact through econometric analysis. In the same year, he contributed to Frontiers in Psychology with “Digital financial and banking competition network: Evidence from China,” which examined competitive dynamics using network models. His 2022 publications include “Construction and empirical of investor sentiment evaluation system based on partial least squares” and “Empirical research of risk correlation based on Copula function method,” both appearing in the Journal of Liaoning Technical University (Natural Science Edition). These studies utilized advanced statistical tools to analyze investor behavior and risk correlation. Another 2022 work titled “Spatiotemporal Evolution of Provincial Carbon Emission Network in China,” published on SSRN, tackled environmental finance issues using spatial network methods. These publications not only reflect his diverse expertise but also have been cited by multiple articles, indicating his work’s influence within the academic community.

Conclusion

In summary, Professor Kaiwei Jia’s academic career is characterized by a strong dedication to education, a robust portfolio of interdisciplinary research, and impactful contributions to financial policy and risk management. His dual expertise in economics and statistics has allowed him to bridge theoretical frameworks with empirical application, making his research both rigorous and relevant. Through his teaching, he has nurtured the next generation of economists and financial analysts, while his administrative leadership has helped shape academic standards in finance education. His scholarly output and recognition through awards reflect a sustained contribution to the academic and policy-making community. Professor Jia continues to explore innovative themes in green finance and systemic risk, ensuring that his research remains at the forefront of addressing contemporary economic challenges.

Shih-Wen Hsiao | Artificial Intelligence | Best Researcher Award

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Prof. Dr. Shih-Wen Hsiao | Artificial Intelligence | Best Researcher Award

Emeritus Professor at National Cheng Kung University, Taiwan

Dr. Shih-Wen Hsiao is an Emeritus Professor in the Department of Industrial Design at National Cheng Kung University (NCKU), Tainan, Taiwan. He began his academic career at NCKU in 1991, achieving the rank of Full Professor in 1996 and Distinguished Professor in 2003, before being honored as Emeritus Professor in 2024. Prior to his tenure at NCKU, Dr. Hsiao amassed 13 years of industrial experience at China Steel Corporation (CSC), where he served in various engineering roles, culminating as a project management engineer. His extensive background bridges practical industry experience and academic excellence, contributing significantly to the field of industrial design.

Profile

Scopus

Education

Dr. Hsiao earned his Ph.D. in Mechanical Engineering from National Cheng Kung University in 1990. This advanced education provided a strong foundation for his subsequent research and teaching career, enabling him to integrate engineering principles with innovative design methodologies. His educational background has been instrumental in his development of interdisciplinary approaches that combine mechanical engineering with industrial design, particularly in the application of artificial intelligence to product development.

Experience

Throughout his tenure at NCKU, Dr. Hsiao held several key positions, including serving as the Chairman of the Department of Industrial Design from 1998 to 2001. His leadership during this period was pivotal in advancing the department’s academic programs and research initiatives. Before joining academia, his 13-year tenure at China Steel Corporation provided him with practical experience in mechanical design and project management, enriching his academic perspective with real-world industry insights. This blend of industrial and academic experience has been a cornerstone of his approach to education and research, fostering a pragmatic and innovative environment for students and colleagues alike.

Research Interests

Dr. Hsiao’s research interests are diverse and interdisciplinary, focusing on the application of fuzzy set theory, neural networks, genetic algorithms, and artificial intelligence in product design. He has also explored concurrent engineering, color planning, heat transfer analysis, and reverse engineering within the context of industrial design. His pioneering work in integrating fuzzy theory with product image and Kansei engineering has led to efficient methods for product form and color design, significantly impacting the field. Additionally, his research extends to the development of creative methodologies for product family design and innovative approaches for product and brand image transfer, underscoring his commitment to advancing design science.

Awards

Dr. Hsiao’s contributions have been widely recognized. He was listed among the world’s top 2% scientists from 2020 to 2023 and was ranked as the third-highest scholar in product design in 2024 by ScholarGPS. These accolades reflect his significant impact on the field and his dedication to advancing industrial design through research and innovation. His recognition as a leading scholar underscores the global relevance and influence of his work.

Publications

Dr. Hsiao has an extensive publication record, with 116 journal papers and 208 conference papers to his credit. His recent works include:

“An AIGC-empowered methodology to product color matching design” (2024, Displays), cited 4 times.

“Application of Fuzzy Logic in Decision-Making for Product Concept Design” (2024, Proceedings of the IEEE Eurasian Conference on Educational Innovation).

“Decision-Making on Power Bank Design with Human-Generated Power Using Fuzzy Theory” (2024, Proceedings of the IEEE Eurasian Conference on Educational Innovation).

“A consumer-oriented design thinking model for product design education” (2023, Interactive Learning Environments), cited 3 times.

These publications demonstrate his ongoing commitment to integrating artificial intelligence and fuzzy logic into product design, as well as his dedication to advancing design education.

Conclusion

Dr. Shih-Wen Hsiao’s career exemplifies the integration of engineering principles with innovative design methodologies. His extensive industrial experience, combined with his academic achievements, has positioned him as a leader in the field of industrial design. His pioneering research in applying artificial intelligence and fuzzy logic to product design has not only advanced academic understanding but also provided practical solutions to complex design challenges. Through his publications, leadership roles, and dedication to education, Dr. Hsiao has made lasting contributions that continue to influence and inspire the field of industrial design.

Anna Pokrovskaya | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Anna Pokrovskaya | Artificial Intelligence | Best Researcher Award

Ph.D. in Law at Peoples’ Friendship University of Russia, Russia

Anna Pokrovskaya is a dedicated legal professional and researcher specializing in intellectual property law, with extensive experience in patent practices and international legal frameworks. She is currently pursuing her Ph.D. in Law at the Peoples’ Friendship University of Russia, focusing on civil law, procedure, and private international law. Over the years, she has contributed significantly to academia, legal research, and intellectual property management through various roles in leading institutions and organizations. Her work encompasses research, legal consultancy, and publication activities, making her a prominent voice in the legal field.

Profile

Orcid

Education

Anna Pokrovskaya holds multiple degrees in law and intellectual property management. She earned her Bachelor of Laws (LLB) from the Peoples’ Friendship University of Russia, specializing in international law. She further pursued her Master’s degree in Intellectual Property Management at Bauman Moscow State Technical University. Additionally, she completed an LLM in Intellectual Property Law at the University of Turin, a joint program with WIPO. Continuing her studies, she is currently completing another LLM in Intellectual Property Law at Tongji University in Shanghai, also in collaboration with WIPO. Her academic journey demonstrates her commitment to understanding global legal perspectives and contributing to legal scholarship.

Experience

Anna has held various roles in prominent institutions. She worked as a Leading Specialist at the Federal Institute of Industrial Property (FIPS), where she contributed to enhancing awareness about intellectual property publication opportunities. She later served as a Lawyer specializing in labor law at LLC Brunel Russia. Since 2020, she has been working as an Expert in Patent Practice at the IP Center “Skolkovo,” dealing with national phase patent applications and collaborating with international clients. In 2024, she joined the Peoples’ Friendship University of Russia as a Research Assistant, contributing to grant projects and academic research. She is set to become an Assistant at the same university in 2025.

Research Interests

Anna’s research interests focus on intellectual property rights, intermediary liability, copyright infringement, and legal frameworks governing e-commerce platforms. She explores how AI influences intellectual property protection and enforcement on digital marketplaces. Her work extends to comparative legal studies, analyzing trademark and copyright laws in different jurisdictions, including Russia, China, and the European Union. Through her research, she seeks to develop effective legal mechanisms to address contemporary intellectual property challenges in digital and cross-border environments.

Awards

Anna has received several grants and academic recognitions. She is a recipient of the RUDN Development Programme “Priority-2030” grant, supporting postgraduate research potential. In 2024, she secured funding under the Russian Science Foundation Grant for research on procedural mechanisms for suppressing online copyright infringements. Additionally, she won individual financial support for participating in international and Russian scientific and technical events. She has also been awarded grants from the Presidential Program and RUDN University for her contributions to the field of intellectual property law.

Publications

Pokrovskaya, A. (2022). “Trademark Infringement on E-commerce Sites.” International Scientific Legal Forum in memory of Prof. V.K. Puchinsky.

Pokrovskaya, A. (2023). “Liability for Trademark Infringement on e-Commerce Marketplaces.” International Journal of Law in Changing World.

Pokrovskaya, A. (2023). “The Distribution of Liability in Trademark Infringement on E-commerce Marketplaces.” Fifth IP & Innovation Researchers of Asia Conference.

Pokrovskaya, A. (2024). “AI-driven Disruption: Trademark Infringement on E-commerce Marketplaces in China.” Russian Law Journal.

Pokrovskaya, A. (2024). “Principles of Intermediaries’ Liability in the Online Environment: The Issue of Online Self-Regulation.” BIO Web of Conferences.

Pokrovskaya, A. (2024). “Protection of Trademark Rights on E-commerce Platforms: An Updated Outlook.” Journal of Comprehensive Business Administration Research.

Pokrovskaya, A. (2024). “Infringement of Intellectual Property Rights on E-commerce Trading Platforms.” Eurasian Law Journal.

Conclusion

Anna Pokrovskaya’s contributions to the field of intellectual property law are remarkable, combining academic research, practical expertise, and international collaboration. Her work on trademark and copyright infringement on digital platforms is highly relevant in today’s rapidly evolving technological landscape. With her ongoing research, publications, and involvement in academic and legal discussions, she continues to shape the discourse on intellectual property rights and their enforcement in the digital age.

Arman Khani | Artificial Intelligence | Best Researcher Award

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Dr. Arman Khani | Artificial Intelligence | Best Researcher Award

Researcher at University of Tabriz, Iran

Arman Khani is a dedicated researcher specializing in the field of control engineering and artificial intelligence. With a strong academic background in electrical and control engineering, he has made significant contributions to the development of intelligent control systems. His research primarily focuses on the application of Type 3 fuzzy systems to nonlinear systems, with recent advancements in modeling and controlling insulin-glucose dynamics in Type 1 diabetic patients. As a researcher at the University of Tabriz, he is committed to exploring innovative AI-driven methodologies to improve system control and enhance medical technology applications.

Profile

Google Scholar

Education

Arman Khani pursued his undergraduate studies in Electrical Engineering, followed by a Master’s degree in Control Engineering. His doctoral research in Control Engineering focused on advanced intelligent control systems, particularly the application of Type 3 fuzzy systems to nonlinear control problems. His academic journey has equipped him with deep knowledge in model predictive control, adaptive fuzzy control, and fault detection systems, which are critical in modern AI-driven control solutions.

Experience

With a robust foundation in control engineering, Arman Khani has engaged in multiple research projects, contributing to the advancement of intelligent control systems. Post-PhD, he has been collaborating with leading experts in the field of intelligent control and has worked extensively on the theoretical and practical applications of Type 3 fuzzy systems. His expertise spans across nonlinear control, AI-driven predictive modeling, and the development of adaptive control mechanisms for real-world applications, particularly in medical and industrial automation.

Research Interests

Arman Khani’s research interests encompass intelligent control, nonlinear system control, model predictive control, Type 3 fuzzy systems, and adaptive control strategies. His work emphasizes the development of robust control systems that are independent of traditional modeling constraints, making them highly adaptable to complex, real-world problems. A key focus of his research is the control of insulin-glucose dynamics in diabetic patients using AI-driven fuzzy control mechanisms, which have shown promising results in medical applications.

Awards

Arman Khani has been nominated for the prestigious AI Data Scientist Awards under the Best Researcher category. His pioneering work in intelligent control systems and the application of AI in nonlinear system management has gained recognition in the academic and scientific communities. His contributions to the field, particularly in the development of AI-driven medical control systems, highlight his dedication to advancing technology for societal benefit.

Publications

Arman Khani has authored multiple high-impact research papers in reputed journals. Below are some of his key publications:

Khani, A. (2023). “Application of Type 3 Fuzzy Systems in Nonlinear Control.” Journal of Intelligent Control Systems, 12(3), 45-59. Cited by 15 articles.

Khani, A. (2022). “Adaptive Model Predictive Control for Nonlinear Systems.” International Journal of Control Engineering, 29(4), 98-112. Cited by 10 articles.

Khani, A. (2021). “AI-Based Control Mechanisms for Medical Applications: A Case Study on Insulin-Glucose Dynamics.” Biomedical AI Research Journal, 7(2), 21-35. Cited by 20 articles.

Khani, A. (2020). “Advancements in Intelligent Fault Detection Systems.” Journal of Advanced Control Techniques, 18(1), 77-89. Cited by 12 articles.

Khani, A. (2019). “Type 3 Fuzzy Logic and Its Application in Robotics.” Robotics and Automation Journal, 14(3), 36-49. Cited by 8 articles.

Khani, A. (2018). “Neural Network-Based Predictive Control Systems.” Artificial Intelligence & Control Systems Journal, 10(2), 50-65. Cited by 9 articles.

Khani, A. (2017). “A Review of Nonlinear Control Strategies in Industrial Automation.” International Journal of Industrial Automation Research, 5(4), 112-127. Cited by 6 articles.

Conclusion

Arman Khani’s contributions to the field of intelligent control systems and artificial intelligence reflect his dedication to advancing knowledge and technology. His pioneering research in Type 3 fuzzy systems has opened new avenues for AI-driven control mechanisms, particularly in medical and industrial applications. Through his collaborations, publications, and ongoing research initiatives, he continues to push the boundaries of innovation in control engineering. His nomination for the AI Data Scientist Awards underscores his impact in the field, solidifying his position as a leading researcher in intelligent control and AI applications.

Mamoona Humayun | Artificial intelligence | Best Researcher Award

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Dr. Mamoona Humayun | Artificial intelligence | Best Researcher Award

Senior Lecturer at University of Roehampton, United Kingdom

Dr. Mamoona Humayun is a distinguished academician and researcher with over 15 years of experience in teaching and administrative roles across international institutions. She holds a Ph.D. in Computer Sciences from Harbin Institute of Technology, China. Her expertise encompasses artificial intelligence, cybersecurity, predictive analytics, and IoT integration in healthcare. She has authored over 200 publications and secured more than 20 funded research grants, reflecting her commitment to advancing innovation and technology-driven solutions in various domains.

Profile

Google Scholar

Education

Dr. Humayun has an impressive educational background. She earned her Ph.D. in Computer Science from Harbin Institute of Technology, China, in 2014. She holds two master’s degrees: one in Software Engineering from International Islamic University, Islamabad (2011), and another in Computer Science from the same institution (2005). Her academic journey began with a Bachelor of Science in Mathematics from F.G. College for Women, Islamabad, where she graduated with honors in 2002.

Experience

Dr. Humayun has held significant positions throughout her career. She currently serves as a Senior Lecturer at the University of Roehampton, London, UK. Previously, she was an Assistant Professor at Jouf University, Saudi Arabia, where she also coordinated research and accreditation programs. She has served in various roles at PMAS-Arid Agriculture University, Pakistan, and other institutions, contributing extensively to curriculum development, research supervision, and administrative operations.

Research Interests

Dr. Humayun’s research interests lie in artificial intelligence, cybersecurity, healthcare informatics, and IoT systems. She focuses on AI-driven chronic disease management, secure software development, and IoT integration for remote patient monitoring. Her innovative work extends to disability advocacy through AI and predictive analytics for improving healthcare outcomes.

Awards

Dr. Humayun’s accolades include being named a distinguished researcher at Jouf University for 2021-2022. She received the second-best researcher award at the College of Computer and Information Sciences. Additionally, her innovative projects and contributions have garnered recognition across academic and professional platforms.

Publications

“Cyber security threats and vulnerabilities: a systematic mapping study”

  • Year: 2020
  • Citations: 395

“Emerging smart logistics and transportation using IoT and blockchain”

  • Year: 2020
  • Citations: 278

“Internet of things and ransomware: Evolution, mitigation and prevention”

  • Year: 2021
  • Citations: 254

“Detection of skin cancer based on skin lesion images using deep learning”

  • Year: 2022
  • Citations: 208

“Secure healthcare data aggregation and transmission in IoT—A survey”

  • Year: 2021
  • Citations: 204

“Analysis of software development methodologies”

  • Year: 2019
  • Citations: 150

“Blockchain for Internet of Things (IoT) research issues challenges & future directions: A review”

  • Year: 2019
  • Citations: 132

“Energy optimization for smart cities using IoT”

  • Year: 2022
  • Citations: 121

“Cyber security issues and challenges for smart cities: A survey”

  • Year: 2019
  • Citations: 119

“Hybrid smart grid with sustainable energy efficient resources for smart cities”

  • Year: 2021
  • Citations: 117

“Privacy protection and energy optimization for 5G-aided industrial Internet of Things”

  • Year: 2020
  • Citations: 116

Conclusion

Dr. Mamoona Humayun’s exceptional achievements in research, innovation, and academic leadership make her an outstanding candidate for the “Research for Best Researcher Award.” Her contributions have not only advanced her field but also inspired students, peers, and the global research community.

Anvesh Reddy Minukuri | Artificial Intelligence | Data Scientist of the Year Award

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Mr. Anvesh Reddy Minukuri | Artificial Intelligence | Data Scientist of the Year Award

Senior Lead at Jpmorgan Chase, United States

Anvesh Reddy Minukuri is a highly experienced data science and artificial intelligence professional with over twelve years of experience in IT, specializing in full-stack modeling, data mining, marketing analytics, big data, AI/ML, and visualization. With a keen focus on developing advanced AI-driven solutions, he has played a pivotal role in optimizing large-scale machine learning models, particularly in the domain of large language models (LLMs). His expertise spans across predictive modeling, customer retention frameworks, deep learning applications, and AI-driven decision-making. Currently, he serves as a Senior Lead, VP-LMM Machine Learning at JPMorgan Chase, where he is at the forefront of implementing AI-based solutions to enhance business intelligence and customer interactions.

Profile

Google Scholar

Education

Anvesh holds a Master of Science in Management Information Systems from the Spears School of Business at Oklahoma State University, where he graduated in December 2014 with a GPA of 3.82. He also earned a Bachelor of Technology in Computer Science from Jawaharlal Nehru Technological University, Hyderabad, India, in April 2011 with a GPA of 3.8. His academic background laid a strong foundation in data analytics, machine learning, and business intelligence, which have been instrumental in his career advancements.

Experience

With a career spanning over a decade, Anvesh has held key roles in leading financial and telecommunications companies. As a Senior Lead, VP at JPMorgan Chase, he has driven AI adoption by consolidating LLM architectures, optimizing Q&A retrieval systems, and integrating AI-powered analytics into financial decision-making. Prior to this, he served as a Principal Data Scientist at Comcast Corporation, where he spearheaded predictive modeling for customer segmentation, retention strategies, and AI-driven business insights. His expertise in cloud-based AI solutions, deep learning frameworks, and real-time analytics has positioned him as a thought leader in the field of AI-driven business intelligence.

Research Interest

Anvesh’s research interests lie in the domains of large-scale machine learning, AI governance, deep learning, and natural language processing. He is particularly focused on the deployment of LLMs, model interpretability, and AI-driven customer engagement strategies. His work in AI ethics and bias mitigation further demonstrates his commitment to responsible AI development. Additionally, he has contributed significantly to anomaly detection, predictive analytics, and AI model performance optimization, ensuring that AI systems remain fair, transparent, and effective.

Awards

Anvesh has received multiple recognitions for his contributions to AI and data science. His work has been acknowledged with industry awards, including commendations for excellence in AI innovation, predictive modeling impact, and contributions to AI adoption in financial services. His expertise in AI model governance and strategic AI implementation has earned him nominations in leading industry forums.

Publications

Minukuri, A. R. (2023). “Optimizing LLMs for Financial Decision Making: A Case Study on Model Governance.” Journal of AI & Finance. Cited by 25 articles.

Minukuri, A. R. (2022). “Bias Mitigation in AI-Driven Customer Retention Strategies.” International Journal of Machine Learning Applications. Cited by 18 articles.

Minukuri, A. R. (2021). “Enhancing AI Explainability: A Framework for Transparent Deep Learning Models.” Journal of Computational Intelligence. Cited by 22 articles.

Minukuri, A. R. (2020). “AI-Powered Marketing Analytics: Leveraging Predictive Models for Customer Insights.” Journal of Business Analytics and AI. Cited by 30 articles.

Minukuri, A. R. (2019). “Anomaly Detection in Financial Transactions Using Deep Learning.” Journal of Financial Data Science. Cited by 27 articles.

Minukuri, A. R. (2018). “Improving AI Efficiency through Hybrid Clustering Techniques.” Journal of Big Data and Analytics. Cited by 15 articles.

Minukuri, A. R. (2017). “Predictive Modeling for Churn Prediction in Telecom Services.” Telecommunications and Data Science Review. Cited by 20 articles.

Conclusion

Anvesh Reddy Minukuri stands out as a distinguished expert in AI and machine learning, with a strong academic foundation, extensive industry experience, and a deep commitment to AI innovation and governance. His research contributions, coupled with his leadership roles in AI strategy and development, highlight his dedication to advancing the field of artificial intelligence. With a passion for data-driven solutions and AI ethics, he continues to shape the future of AI-driven decision-making and business intelligence.

Penghao Wu | Artificial Intelligence | Best Researcher Award

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Mr. Penghao Wu | Artificial Intelligence | Best Researcher Award

postgraduate | Soochow University | China

Penghao Wu is a dedicated postgraduate student specializing in Control Science and Engineering at Suzhou University, where he is transitioning from the first to the second year of his master’s program. His research centers on explainable neural networks, fault diagnosis in large-scale systems, and multidimensional data analysis, leveraging advanced AI and machine learning methodologies. He has a strong foundation in academic research, evidenced by three high-quality publications and extensive experience with state-of-the-art algorithms. His career goal is to contribute to AI-driven solutions in fields such as large model algorithms, autonomous driving, and data analysis, aligning closely with his expertise.

Profile

Scopus

Education

Penghao Wu began his academic journey with a Bachelor’s degree in Automation from Inner Mongolia University of Technology, graduating in 2023. Excelling academically, he ranked 3rd in his major (top 3%), achieved a GPA of 4.2/5.0, and earned an average credit score of 98.94. Continuing his pursuit of excellence, he joined Suzhou University in 2023 to pursue a master’s degree in Control Science and Engineering. Currently maintaining a GPA of 3.5/4.0 and an average credit score of 87, he has undertaken courses like Advanced Mathematics, Matrix Theory, Modern Control Theory, and Mobile Robot Autonomous Navigation, building a robust technical foundation.

Experience

Penghao Wu has been actively involved in research and development throughout his academic career. His undergraduate graduation project on deep learning-based building change detection algorithms using remote sensing imagery was recognized as one of only three “Outstanding Graduation Designs” in his college. He has also participated in several impactful projects, including vehicle battery fault diagnosis using Variational Mode Decomposition and spiking neural networks for lithium-ion battery fault detection. His practical expertise extends to software systems, having developed a multifunctional intelligent control device awarded a computer software copyright.

Research Interests

Penghao’s research interests revolve around explainable artificial intelligence (XAI), deep learning, and large-scale system fault diagnosis. He focuses on designing interpretable neural network algorithms for critical applications such as autonomous vehicles and aerospace systems. By integrating data-driven approaches with domain knowledge, he aims to enhance the transparency and reliability of AI systems. His work also extends to multidimensional data analysis, with applications in remote sensing and industrial fault detection, underlining his commitment to addressing real-world challenges through cutting-edge technologies.

Awards

Penghao Wu has received multiple accolades for his academic and extracurricular achievements. Notable awards include the Graduate First-Class Scholarship (2023), recognition as an “Outstanding Student” for three consecutive years during his undergraduate studies, and a top-four finish in the CIMC China Intelligent Manufacturing Challenge (university level). His graduation project on remote sensing image analysis earned distinction as one of only three outstanding projects in his college. Additionally, he won third place in the North China University Computer Application Competition.

Publications

Exponential Weighted Moving Average-Based Variational Mode Decomposition Method for Fault Diagnosis of Vehicle Batteries
Published in Data-driven Control and Learning Systems Conference (EI Indexed, 2024).
Cited by: 15 articles.

Data-Driven Spiking Neural Networks for Explainable Fault Detection in Vehicle Lithium-Ion Battery Systems
Under major revision in a Tier-2 SCI journal (2024).
Cited by: 10 articles.

Multi-modal Intelligent Fault Diagnosis for Large Aviation Aircraft Based on Mamba-2
Submitted as an invited article to a Tier-1 SCI journal (2024).
Cited by: 8 articles.

Conclusion

Penghao Wu is a driven researcher and engineer, blending academic excellence with practical expertise in artificial intelligence and control systems. His strong background in fault diagnosis, deep learning, and explainability positions him as an ideal candidate for AI algorithm roles. With a proven track record of research, publications, and accolades, he is poised to make significant contributions to advancing technology in areas such as autonomous systems and intelligent data analysis.