Xiongjun Zhao | Artificial Intelligence | Best Researcher Award

Published on by Global Academic Awards

Assist. Prof. Dr. Xiongjun Zhao | Artificial Intelligence | Best Researcher Award

Assist. Prof. Dr. Xiongjun Zhao, Hunan University, China

Assist. Prof. Dr. Xiongjun Zhao is a biomedical AI researcher currently serving as an Assistant Researcher at the School of Information Science and Engineering, Hunan University. He holds a Ph.D. in Computer Science and Technology and a bachelor’s degree in Software Engineering, both from Hunan University, where he was recognized as an Outstanding Graduate and Student Leader. His research focuses on biomedical big data analysis, multi-modal learning, and intelligent medical systems. Dr. Zhao has authored five significant papers, including a CCF-A ACM MM conference paper and three SCI-indexed journal articles. He has also secured four patents in medical AI technologies and leads multiple funded research projects backed by prominent Chinese institutions. An awardee of national honors in mathematics and programming competitions, he brings strong technical expertise in Python, C++, and deep learning frameworks. Dr. Zhao actively mentors students and contributes to international conferences such as IEEE BIBM and ACM MM.

Publication Profile

Orcid

Work Experience

Assist. Prof. Dr. Xiongjun Zhao currently holds the position of Assistant Researcher at the School of Information Science and Engineering, Hunan University, a role he began in July 2025. In this capacity, he is deeply involved in cutting-edge research and development focused on biomedical big data analysis, multi-modal learning, and intelligent medical systems. His responsibilities include leading and contributing to a range of scientific research projects, many of which involve national and provincial collaborations. Dr. Zhao plays a pivotal role in advancing academic innovation through his contributions to interdisciplinary projects and by leveraging artificial intelligence in healthcare-related research. In addition to his research activities, he actively mentors students, fostering a dynamic and innovative research environment in the laboratory. His work exemplifies a commitment to technological advancement and academic leadership, positioning him as a key contributor to the development of intelligent medical solutions through data-driven methodologies.

Educational Background

Assist. Prof. Dr. Xiongjun Zhao received his comprehensive academic training from Hunan University, a prestigious institution recognized under China’s Double First-Class Initiative. He pursued a Direct Ph.D. Program in Computer Science and Technology from September 2020 to June 2025, under the mentorship of Prof. Ying Jianguo, a distinguished Changjiang Scholar. His doctoral studies focused on advanced topics in biomedical data science and artificial intelligence. Prior to that, Dr. Zhao completed his Bachelor’s degree in Software Engineering at the same university between September 2016 and June 2020. During his undergraduate years, he was honored as an Outstanding Graduate and recognized for his leadership and community involvement with accolades such as Outstanding Student Leader and Outstanding Volunteer. His solid educational foundation, shaped by rigorous training and guided mentorship, has laid the groundwork for his successful academic and research career in the fields of machine learning and intelligent medical systems.

Research Experience

Assist. Prof. Dr. Xiongjun Zhao has a strong and focused research background in biomedical big data analysis, multi-modal learning, and intelligent medical models. His work integrates advanced machine learning techniques with complex medical datasets to develop deep learning frameworks—particularly Transformer-based models—for clinical diagnostics and decision support systems. His notable academic contributions include the publication of one top-tier conference paper and three SCI-indexed journal articles, along with the successful registration of four patents in the medical AI domain. Dr. Zhao has led and participated in several high-impact research projects, notably those funded by the Hunan Provincial Department of Health and the China Association for Science and Technology. His role as a core researcher in these projects highlights his leadership in interdisciplinary and applied medical research. He has also presented at major international conferences such as ACM MM and IEEE BIBM, reflecting his active engagement in the global scientific community.

Awards and Recognitions

Assist. Prof. Dr. Xiongjun Zhao has received several prestigious awards that reflect his academic excellence and problem-solving capabilities in both theoretical and applied domains. He was awarded the National Second Prize in the National University Student Mathematics Competition (ASC), showcasing his strong analytical and mathematical skills. In competitive programming, he earned the Regional Second Prize in the ACM-ICPC International Collegiate Programming Contest, a globally recognized event for algorithmic proficiency. His team received an Honorable Mention (Top 15%) in the Mathematical Contest in Modeling (MCM/ICPC), further emphasizing his strength in interdisciplinary problem-solving. Additionally, he secured the National Third Prize in the China Graduate Mathematical Modeling Competition and the Provincial Third Prize in the Hunan “Internet+” Innovation and Entrepreneurship Competition. These accolades collectively highlight Dr. Zhao’s commitment to academic rigor, computational excellence, and innovation, and they underscore his readiness to tackle real-world challenges through collaborative and research-driven approaches.

Research Focus

Assist. Prof. Dr. Xiongjun Zhao’s research is primarily centered on biomedical big data analysis, intelligent medical systems, and multi-modal machine learning for clinical decision-making. His work integrates advanced deep learning techniques—such as Transformers, Graph Neural Networks (GNNs), and multimodal prompt learning—into healthcare applications, particularly in medical image analysis, electronic health record (EHR) modeling, and medication guidance systems. His studies include developing continual learning models for EHRs (TransEHR), large language models for drug recommendations (ShennongMGS), and novel diagnostic approaches using multimodal X-ray interpretation and ECG analysis (ECGNN). He also explores multitask and multi-view learning frameworks to enhance predictive accuracy in various clinical settings. His research lies at the intersection of artificial intelligence, medical informatics, and computational biology, aiming to improve diagnostic accuracy, clinical workflow, and personalized medicine. Through high-impact publications and patents, Dr. Zhao contributes significantly to the advancement of AI-driven healthcare technologies and smart medical diagnostics.

Publication Top Notes

  • TransEHR: Alignment-Free Electronic Health Records Continual Learning Across Feature Spaces, Expert Systems with Applications, 2025, DOI: 10.1016/j.eswa.2025.129020

  • ShennongMGS: An LLM-based Chinese Medication Guidance System, ACM Transactions on Management Information Systems, 2025, DOI: 10.1145/3658451

  • Report-Concept Textual-Prompt Learning for Enhancing X-ray Diagnosis, ACM MM (Conference), 2024, DOI: 10.1145/3664647.3681568

  • ECGNN: Enhancing Abnormal Recognition in 12-Lead ECG with Graph Neural Network, IEEE BIBM, 2022, DOI: 10.1109/BIBM55620.2022.9995419

  • UniMed: Multimodal Multitask Learning for Medical Predictions, IEEE BIBM, 2022, DOI: 10.1109/BIBM55620.2022.9995044

  • A Knowledge-aware Machine Reading Comprehension Framework for Dialogue Symptom Diagnosis, IEEE BIBM, 2021, DOI: 10.1109/bibm52615.2021.9669717

  • Multi-View Weighted Feature Fusion Using CNN for Pneumonia Detection on Chest X-Rays, IEEE HEALTHCOM, 2021, DOI: 10.1109/healthcom49281.2021.9399029

Eugene Levner | Artificial Intelligence | Best Researcher Award

Published on by Global Academic Awards

Prof. Eugene Levner | Artificial Intelligence | Best Researcher Award

Professor at Holon Institute of Technology, Israel

Prof. Eugene Levner is a renowned expert in computational mathematics, operations research, and artificial intelligence, with a career spanning over five decades. He earned his Ph.D. from the Central Economic-Mathematical Institute of the USSR Academy of Sciences, focusing on graph models and scheduling problems. He has held prominent academic positions in Russia and Israel, including Holon Institute of Technology, Bar Ilan University, and The Hebrew University of Jerusalem. Prof. Levner has authored numerous influential publications in top-tier journals and received multiple Best Paper and Excellence in Teaching awards. His research spans scheduling theory, robotics, fuzzy logic, and digital medicine, with over 1,500 citations highlighting his global impact. He has been a guest lecturer at institutions across Europe, North America, and Asia and has served on editorial boards of leading journals. His work continues to influence the fields of algorithm design, risk management, and smart manufacturing systems.

Professional Profile

Google Scholar

Academic Background

Prof. Eugene Levner holds an exceptional academic background in computational mathematics and systems science. He earned his B.S. and M.S. degrees in Computational Mathematics from Moscow State Lomonosov University between 1963 and 1968, where he developed a strong foundation in algorithmic thinking and mathematical modeling. He went on to complete his Ph.D. in Computer and Systems Science at the Central Economic-Mathematical Institute of the USSR Academy of Sciences from 1969 to 1973. His doctoral research focused on the design of graph models and methods for solving scheduling problems, laying the groundwork for a lifelong career in optimization and operations research. Prof. Levner was mentored by distinguished scholars, including Prof. Boris T. Polyak and Prof. David B. Yudin, both influential figures in applied mathematics. His education equipped him with advanced skills in mathematical programming, which he later applied across multiple disciplines such as artificial intelligence, robotics, and digital medicine.

Professional Background

Prof. Eugene Levner has had a distinguished professional career marked by academic leadership and groundbreaking research in computer science, operations research, and artificial intelligence. Beginning as a researcher at the Institute of Automation and Remote Control in Moscow, he went on to serve at the Central Economic-Mathematical Institute of the USSR Academy of Sciences for over two decades. He later held academic positions at Moscow State University and The Hebrew University of Jerusalem. From 1994 to 2010, he was a professor at the Holon Institute of Technology in Israel, where he also received multiple excellence awards. He further contributed as a lecturer at Bar Ilan University and served as a full-time professor at Ashkelon Academic College. Prof. Levner has been a visiting lecturer at leading institutions across Europe, Asia, and North America. Currently, he serves as Emeritus Professor at the Holon Institute of Technology, continuing to mentor students and contribute to international research.

Awards and Honors

Prof. Eugene Levner has received numerous prestigious awards and honors in recognition of his outstanding contributions to research, teaching, and academic leadership. Early in his career, he was awarded the Silver Diploma by the USSR Institute of Control Problems in 1972 and received the Best Paper Award from the Moscow Government in 1981. His international recognition includes listings in Marquis’ Who’s Who in Science and Engineering and 2000 Outstanding Scientists of the 20th Century. He has earned multiple Best Paper Awards at international conferences in Russia, Mexico, and Israel, including INCOM-IFAC and MICAI. In addition to research excellence, he was honored with Excellence in Teaching and Research Awards at the Holon Institute of Technology between 2009 and 2021. He also received a special award from Shanghai Jiao Tong University in 2010 for his exceptional instruction in operations research. These accolades reflect his lasting global impact in applied mathematics and computer science.

Research Focus

Prof. Eugene Levner’s research spans several core areas in computational mathematics and applied computer science, with a primary focus on algorithm design, scheduling theory, and operations research. He has made significant contributions to the development of graph-based models and approximation algorithms for complex scheduling and optimization problems, particularly in manufacturing systems and robotics. His work integrates artificial intelligence techniques with digital medicine, risk management, and decision-making under uncertainty. Prof. Levner has also advanced research in fuzzy logic and its applications in intelligent systems and supply chain resilience. His recent studies explore adaptive scheduling, energy-efficient computing, and the ripple effects of environmental risks using entropy-based models. He has published extensively in high-impact journals, contributing to both theoretical foundations and real-world applications. Through multidisciplinary research and international collaborations, Prof. Levner continues to influence areas such as smart manufacturing, autonomous systems, and computational logistics, maintaining relevance in both academic and industrial research communities.

Publication Top Notes

Integer Programming and Flows in Networks
Year: 1974 | Cited by: 472

Fast Approximation Algorithm for Job Sequencing with Deadlines
Year: 1981 | Cited by: 121

Computational Complexity of Approximation Algorithms for Combinatorial Problems
Year: 1979 | Cited by: 124

An Improved Algorithm for Cyclic Flowshop Scheduling in a Robotic Cell
Year: 1997 | Cited by: 139

Cyclic Scheduling in Robotic Flowshops
Year: 2000 | Cited by: 280

Multiple-Part Cyclic Hoist Scheduling Using a Sieve Method
Year: 2002 | Cited by: 111

Adaptive Scheduling Server for Power-Aware Real-Time Tasks
Year: 2004 | Cited by: 130

Perishable Inventory Management with Dynamic Pricing Using Time–Temperature Indicators Linked to Automatic Detecting Devices
Year: 2014 | Cited by: 145

Complexity of Cyclic Scheduling Problems: A State-of-the-Art Survey
Year: 2010 | Cited by: 231

Entropy-Based Model for the Ripple Effect: Managing Environmental Risks in Supply Chains
Year: 2018 | Cited by: 110

Conclusion

Prof. Eugene Levner is a distinguished scholar with a lifelong dedication to advancing computational mathematics, operations research, and artificial intelligence. With a Ph.D. from the Central Economic-Mathematical Institute of the USSR Academy of Sciences and mentorship under world-renowned experts, his foundational work in graph models, scheduling, and optimization has had lasting global impact. He has published extensively in high-impact journals, with several highly cited papers influencing both theoretical and applied research. Prof. Levner has held senior academic positions in leading institutions across Russia and Israel and delivered invited lectures worldwide. His pioneering research in scheduling theory, robotics, fuzzy logic, and digital medicine, combined with multiple international awards and recognition for both teaching and research excellence, solidifies his reputation as a leader in his field. Through mentoring, interdisciplinary innovation, and global collaboration, Prof. Levner’s work continues to shape contemporary science and technology, making him an exceptional and highly deserving recipient of the “Best Researcher Award.”