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.

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.

Mohamed Abdalzaher | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Mohamed Abdalzaher | Artificial Intelligence | Best Researcher Award

Associate Professor at National Research Institute of Astronomy and Geophysics, Egypt

Mohamed Salah Abdalzaher is a distinguished researcher and academic with a strong focus on machine learning, deep learning, and seismology. He currently holds the position of Research Fellow at the Electrical Engineering Department of the American University of Sharjah (AUS) and is on leave from his role as Associate Professor in the Seismology Department at the National Research Institute of Astronomy and Geophysics (NRIAG) in Egypt. Abdalzaher’s work integrates advanced technologies such as machine learning and remote sensing with seismology, addressing issues related to earthquake prediction and disaster management.

Profile

Scopus

Education

Abdalzaher’s academic journey began with a Bachelor’s degree in Electronics and Communications Engineering from Obour High Institute of Engineering and Technology in 2008. He continued his studies with a Master’s degree from Ain Shams University, focusing on Electronics and Communications Engineering, before obtaining his PhD in Electronics and Communications Engineering from the Egypt-Japan University of Science and Technology in 2016. His postdoctoral research at Kyushu University, Japan, in 2019 contributed to his deepening expertise in machine learning applications and earthquake management technologies.

Experience

Abdalzaher’s professional experience spans both academia and research. As a Research Fellow at AUS, he is at the forefront of advancing machine learning applications in the field of electrical engineering. His role involves conducting cutting-edge research and supervising graduate students in their research projects. In addition, he serves as an Associate Professor at NRIAG, where he leads research efforts on seismic hazard assessments and Earthquake Engineering. He has supervised numerous PhD and MSc theses, contributing to the development of future experts in seismology and engineering.

Research Interest

Abdalzaher’s research interests are broad and multidisciplinary, covering topics such as machine learning, deep learning, cybersecurity, remote sensing, Internet of Things (IoT), and optimization techniques. His primary focus, however, is on the application of machine learning and artificial intelligence for earthquake prediction, seismic hazard assessment, and disaster management. He is also deeply engaged in using remote sensing technologies to monitor seismic activities and improve the accuracy of seismic event classification, with the aim of enhancing early warning systems and disaster response strategies.

Awards

Abdalzaher has received numerous awards and recognitions for his contributions to the fields of electrical engineering and seismology. His work on integrating machine learning with seismic monitoring systems has been widely recognized, contributing significantly to the advancement of earthquake early warning systems and seismic hazard prediction models. His publications, which include high-impact journal papers, reflect his contributions to the scientific community and his ongoing efforts to innovate in the fields of earthquake engineering and smart systems.

Publications

Sharshir, S.W., Joseph, A., Abdalzaher, M.S., et al. (2024). “Using multiple machine learning techniques to enhance the performance prediction of heat pump-driven solar desalination unit.” Desalination and Water Treatment.

Etman, A., Abdalzaher, M. S., et al. (2024). “A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor Networks.” IEEE ACCESS.

Habbak E. L., Abdalzaher, M. S., et al. (2024). “Enhancing the Classification of Seismic Events With Supervised Machine Learning and Feature Importance.” Scientific Report.

Abdalzaher, M. S., Soliman, M. S., & Fouda, M. M. (2024). “Using Deep Learning for Rapid Earthquake Parameter Estimation in Single-Station Single-Component Earthquake Early Warning System.” IEEE Transactions on Geoscience and Remote Sensing.

Krichen, M., Abdalzaher, M. S., et al. (2024). “Emerging technologies and supporting tools for earthquake disaster management: A perspective, challenges, and future directions.” Progress in Disaster Science.

Abdalzaher, M. S., Moustafa, S. R., & Yassien, M. (2024). “Development of smoothed seismicity models for seismic hazard assessment in the Red Sea region.” Natural Hazards.

Moustafa, S. S., Mohamed, G. E. A., Elhadidy, M. S., & Abdalzaher, M. S. (2023). “Machine learning regression implementation for high-frequency seismic wave attenuation estimation in the Aswan Reservoir area, Egypt.” Environmental Earth Sciences.

These publications have garnered attention from peers in the field, with many articles cited extensively, contributing to the evolution of seismic hazard assessment techniques and the integration of machine learning in the geophysical sciences.

Conclusion

Mohamed Salah Abdalzaher has established himself as a leading expert in the application of machine learning, deep learning, and remote sensing technologies to seismology and earthquake engineering. His work has greatly advanced seismic hazard assessments and earthquake early warning systems, utilizing innovative methods to enhance the accuracy of seismic predictions. Abdalzaher continues to push the boundaries of research, with a particular focus on optimizing and deploying machine learning algorithms for real-world disaster management applications. His academic and professional contributions make him a valuable asset to both the academic community and the broader scientific field.

Yao Zheng | Neural Networks | Best Researcher Award

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Prof. Yao Zheng | Neural Networks | Best Researcher Award

Professor | Zhejiang University | China

Yao Zheng is the Cheung Kong Chair Professor at the School of Aeronautics and Astronautics, Zhejiang University, China. With extensive academic and professional experience in computational mechanics and aerospace sciences, he has contributed significantly to these fields through pioneering research and leadership. His career has spanned academia and industry, including tenures at NASA and Siemens, reflecting his global expertise. His work combines engineering, mechanics, and computational science, underpinned by a commitment to innovation and education.

Profile

Scopus

Education

Yao Zheng earned his Ph.D. in Civil Engineering from the University of Wales Swansea (now Swansea University) in 1994, specializing in computational engineering. Before this, he obtained an M.Sc. in Solid Mechanics from Harbin Institute of Technology in 1986 and a B.Sc. in Mathematics from Hangzhou University in 1984. His educational background integrates mathematical precision with engineering application, forming the foundation for his interdisciplinary research.

Professional Experience

Yao Zheng’s professional journey began as a senior research assistant during his Ph.D. studies, which laid the groundwork for his future endeavors. He served as a Senior Research Scientist at NASA Glenn Research Center and later as a Senior Software Scientist at CD-adapco, contributing to cutting-edge aerospace and computational solutions. Since 2007, he has held a Chair Professorship at Zhejiang University, where he also served in leadership roles, including Vice Dean of the Faculty of Engineering. As Director of the Center for Engineering and Scientific Computation, he has driven innovation in computational methods and aerospace research.

Research Interests

Yao Zheng’s research focuses on computational mechanics, numerical simulation, and flight vehicle design. His work bridges aerospace science, mechanics, and computer science, advancing technologies in propulsion and structural analysis. With over 400 publications, he has contributed significantly to understanding complex systems, ensuring his research has practical and academic relevance.

Awards

Yao Zheng’s achievements are recognized by numerous prestigious awards. These include the ACM Gordon Bell Prize finalist in 2023, the Best Chinese Supercomputing Application Award in 2023, and the Qian Ling-Xi Achievement Award for Computational Mechanics in 2018. His contributions have been celebrated with the Natural Science Award of Zhejiang Province and multiple honors for technological progress and computational methods in engineering, reflecting his influence in the field.

Selected Publications

Zheng, Y. (2023). “High-Performance Computational Mechanics for Complex Aerospace Systems.” Aerospace Research Communications. [Cited by: 15 articles].

Zheng, Y., & Coauthors (2020). “Numerical Simulations of Hypersonic Flow Structures.” Engineering Applications of Computational Fluid Mechanics. [Cited by: 32 articles].

Zheng, Y. (2018). “Flight Vehicle Structural Optimization Using Computational Techniques.” Chinese Journal of Computational Mechanics. [Cited by: 20 articles].

Zheng, Y., & Wang, L. (2016). “Advances in Propulsion Technology via Numerical Modeling.” Communications in Computational Physics. [Cited by: 25 articles].

Zheng, Y. (2013). “Computational Approaches to Aerospace Design Challenges.” Journal of Aerospace Science and Technology. [Cited by: 40 articles].

Conclusion

Yao Zheng’s illustrious career demonstrates a commitment to excellence in aerospace engineering and computational mechanics. His leadership, research contributions, and global recognition highlight his status as a pioneer in the field. As a mentor and innovator, he continues to shape the future of aerospace science, inspiring the next generation of engineers and researchers.

Guangbo Yu | Artificial Intelligence | Best Researcher Award

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Mr. Guangbo Yu | Artificial Intelligence | Best Researcher Award

Mr .Guangbo  Yu, PhD Student, University of California, United States.

Mr. Guangbo Yu’s Curriculum Vitae, he demonstrates significant contributions in the field of biomedical engineering and artificial intelligence, with a focus on medical imaging and cancer treatment strategies. His academic background and hands-on research experience in AI applications for cancer immunotherapy and radiomics are commendable. Additionally, his role in designing AI systems at Tencent highlights his expertise in machine learning and model optimization.

Profile

google scholar

🎓 Education:

PhD in Biomedical Engineering (Expected 2027)

University of California, Irvine

Specialization: Radiological Science

Advisor: Prof. Zhuoli Zhang

Master’s in Computer Science

University of Southern California (2015–2017)

Bachelor’s in Software Engineering

University of Electronic Science and Technology of China (2011–2015)

🔬 Research Experience:

Graduate Assistant Researcher at UC Irvine (2022–Present)

Focused on using AI for medical imaging to develop predictive models for cancer immunotherapy treatments using MRI biomarkers. This work aims to improve evaluation methods for immunotherapy responses, especially in treating complex cancers.

💼 Professional Experience:

AI Engineer at Tencent QTrade (2020–2022)

Developed an AI-powered system to structure unstructured financial data, using advanced techniques like Named Entity Recognition (NER) with BERT and GAT.

Boosted model accuracy by 11% and expanded the user base to over 500,000 daily active users through strategic implementations with Flask, Gunicorn, and Jenkins CI/CD.

🔍 Research Interests:

Applying AI to enhance cancer immunotherapy strategies, specifically in areas requiring advanced imaging techniques to assess treatment effectiveness.

Citations:

Citations: 12 (all since 2019)

h-index: 2 (a minimum of two papers with at least two citations each)

i10-index: 0 (no papers with 10 or more citations)

📖 Publications and Presentations:

Qtrade AI at SemEval-2022 Task 11: A Unified Framework for Multilingual NER Task

W. Gan, Y. Lin, G. Yu, G. Chen, & Q. Ye. (2022). Association for Computational Linguistics.

Sorafenib Plus Memory-Like Natural Killer Cell Combination Therapy in Hepatocellular Carcinoma

A. Eresen, Y. Pang, Z. Zhang, Q. Hou, Z. Chen, G. Yu, Y. Wang, V. Yaghmai, … (2024). American Journal of Cancer Research, 14(1), 344.*

Dendritic Cell Vaccination Combined with Irreversible Electroporation for Treating Pancreatic Cancer—A Narrative Review

Z. Zhang, G. Yu, A. Eresen, Z. Chen, Z. Yu, V. Yaghmai, Z. Zhang. (2024). Annals of Translational Medicine.

MRI Radiomics to Monitor Therapeutic Outcome of Sorafenib Plus IHA Transcatheter NK Cell Combination Therapy in Hepatocellular Carcinoma

G. Yu, Z. Zhang, A. Eresen, Q. Hou, E. E. Garcia, Z. Yu, N. Abi-Jaoudeh, … (2024). Journal of Translational Medicine, 22(1), 76.*

Predicting and Monitoring Immune Checkpoint Inhibitor Therapy Using Artificial Intelligence in Pancreatic Cancer

G. Yu, Z. Zhang, A. Eresen, Q. Hou, F. Amirrad, S. Webster, S. Nauli, … (2024). International Journal of Molecular Sciences, 25(22), 12038.*

Sorafenib Plus Memory-Like Natural Killer Cell Immunochemotherapy Boosts Treatment Response in Liver Cancer

A. Eresen, Z. Zhang, G. Yu, Q. Hou, Z. Chen, Z. Yu, V. Yaghmai, Z. Zhang. (2024). BMC Cancer, 24(1), 1215.*

Transcatheter Intraarterial Delivery of Combination Therapy for Hepatocellular Carcinoma

Z. Zhang, A. Eresen, G. Yu, K. Liu, Q. Hou, V. Yaghmai. (2024). Journal of Vascular and Interventional Radiology, 35(3), S199.*

Evaluating Hepatocellular Carcinoma Combination Therapy of Sorafenib and Transcatheter Primed Natural Killer Cell Delivery Using MRI Radiomics Methods

G. Yu, A. Eresen, Z. Zhang, K. Liu, Q. Hou, V. Yaghmai. (2024). Journal of Vascular and Interventional Radiology, 35(3), S143–S144.*

Improving Therapeutic Response Against Hepatocellular Carcinoma with Cytokine-Activated Natural Killer Cells via Transcatheter Intraarterial Administration

A. Eresen, Z. Zhang, G. Yu, Q. Hou, N. Abi-Jaoudeh, V. Yaghmai. (2024). Journal of Vascular and Interventional Radiology, 35(3), S152.*

Investigation of Natural Killer Cell Delivery in Hepatocellular Carcinoma Treatment with Magnetic Resonance Imaging Radiomics

K. Liu, G. Yu, Z. Zhang, Q. Hou, V. Yaghmai, A. Eresen. (2024). Journal of Vascular and Interventional Radiology, 35(3), S92.*

MRI Monitoring of Combined Therapy with Transcatheter Arterial Delivery of NK Cells and Systemic Administration of Sorafenib for the Treatment of HCC

Z. Zhang, G. Yu, A. Eresen, Q. Hou, V. Yaghmai, Z. Zhang. (2024). American Journal of Cancer Research, 14(5), 2216.*