Xun Gong | Energy Saving | AI Breakthrough Award

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Assoc. Prof. Dr. Xun Gong | Energy Saving | AI Breakthrough Award

Assoc. Prof. Dr. Xun Gong | Energy Saving | Professor at Jilin University | China

Assoc. Prof. Dr. Xun Gong is a recognized scholar in the field of automotive systems, intelligent transportation, and advanced control engineering. His work integrates innovative control strategies with emerging automotive technologies to enhance efficiency, safety, and sustainability. With strong contributions in connected vehicle control, driver modeling, and energy optimization for hybrid and electric vehicles, he has become a respected academic and collaborator in international research circles. His publications and research outcomes have been widely cited, reflecting his influence across both academic and industrial domains.

Academic Profile:

Scopus

Google Scholar

Education:

Assoc. Prof. Dr. Xun Gong completed his doctoral studies in control engineering, establishing a strong academic foundation that led to his current research expertise. His advanced education has equipped him with the knowledge and analytical skills to address pressing challenges in automotive and control systems. His academic training was reinforced through collaborations with top universities and research centers, enabling him to contribute to multi-institutional projects at the forefront of engineering research.

Experience:

With significant teaching and research experience at Jilin University, Assoc. Prof. Dr. Xun Gong has guided students, researchers, and collaborators in innovative engineering practices. His academic career has been marked by active involvement in cross-disciplinary projects, where he has worked closely with international partners from leading universities and research organizations. Beyond teaching, he has participated in industry-focused initiatives, ensuring the translation of academic research into real-world applications. His professional journey demonstrates a balance of academic rigor, industrial collaboration, and mentorship in the field of engineering.

Research Interest:

The research interests of Assoc. Prof. Dr. Xun Gong encompass intelligent transportation systems, predictive modeling, and advanced automotive control. His work focuses on vehicle stability, energy management, and eco-trajectory planning to enhance the performance of hybrid and electric vehicles. He is also deeply engaged in connected vehicle technologies, emphasizing driver modeling and integrated optimization strategies that improve safety and sustainability. Through these efforts, he contributes to shaping the future of mobility in the intelligent era, bridging theoretical research with practical applications.

Award:

Assoc. Prof. Dr. Xun Gong has been recognized for his contributions to engineering research through academic awards and professional honors. His leadership in automotive control and predictive modeling has earned acknowledgment within the scholarly community. He continues to demonstrate excellence through high-impact publications, international collaborations, and his service to professional organizations such as IEEE, where he actively contributes to advancing global engineering knowledge.

Selected Publication:

  • Fast nonlinear model predictive control on FPGA using particle swarm optimization, published in 2015, has received 175 citations and is widely referenced in the control engineering community.

  • Cabin and battery thermal management of connected and automated HEVs for improved energy efficiency using hierarchical model predictive control, published in 2019, has been cited 166 times and represents a major advance in sustainable vehicle technology.

  • Vehicle velocity prediction and energy management strategy part 1: Deterministic and stochastic vehicle velocity prediction using machine learning, published in 2019, has gathered 125 citations and reflects his expertise in integrating machine learning with automotive systems.

  • Integrated longitudinal and lateral vehicle stability control for extreme conditions with safety dynamic requirements analysis, published in 2022, has gained 77 citations and highlights his work on vehicle safety and control.

Conclusion:

In summary, Assoc. Prof. Dr. Xun Gong is an accomplished researcher whose career exemplifies academic excellence, international collaboration, and meaningful contributions to automotive and control systems. His innovative research has advanced the fields of intelligent transportation, predictive control, and sustainable energy management, bridging the gap between theoretical knowledge and industrial application. His strong publication record, professional recognition, and leadership in collaborative projects underscore his suitability for this award. With continued commitment to advancing research and mentoring the next generation of engineers, he is positioned to make further impactful contributions to science, industry, and society.

 

 

Ding Jia | Geological Engineering | Best Researcher Award

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Dr. Ding Jia | Geological Engineering | Best Researcher Award

Dr. Ding Jia | Geological Engineering | Research Associate at CCTEG Xian Research Institute Co, Ltd | China

Dr. Ding Jia is a distinguished scholar in the field of hydrogeology and mining engineering, specializing in groundwater systems, mine water inrush prediction, and environmental sustainability in mining regions. His research has consistently addressed complex issues such as water source identification, contamination processes, and hydrological safety in high-intensity mining operations. Through the integration of advanced modeling, numerical simulations, and data-driven methods, Dr. Ding Jia has contributed significantly to reducing environmental risks and improving safe mining practices. He is widely respected for his capacity to bridge theory with practical applications, ensuring that his scientific findings benefit both academic communities and industrial stakeholders.

Academic Profile:

ORCID

Education:

Dr. Ding Jia earned his doctoral degree with a focus on hydrogeology and mining-related water management. His academic foundation is rooted in strong training across groundwater flow dynamics, hydrogeochemical processes, and geotechnical safety, which has provided him with the expertise to lead complex and interdisciplinary projects. The combination of his rigorous education and continuous professional development has allowed him to expand his research beyond national boundaries, contributing to international collaborations and peer-reviewed studies in highly regarded scientific journals.

Experience:

Dr. Ding Jia has gained extensive research and academic experience through active participation in a wide range of scientific projects. His expertise includes numerical modeling of mine water inflow, risk analysis of groundwater contamination, and development of monitoring strategies for subsurface water safety. He has collaborated with multidisciplinary teams across institutions, contributing to projects on groundwater chemistry, contamination pathways, and ecological risk assessment in mining-affected areas. Beyond research, he has actively engaged in mentoring students, supervising research initiatives, and contributing to professional platforms that support the advancement of hydrogeology and mining sciences. His experience also encompasses roles in scientific committees and peer-review panels, ensuring the integrity and quality of published research.

Research Interest:

The primary research interests of Dr. Ding Jia revolve around groundwater systems in mining environments, with particular emphasis on predicting water inrush hazards, source discrimination of mine water, and groundwater contamination analysis. He is deeply invested in applying advanced simulation models, machine learning methods, and field-based monitoring approaches to develop predictive frameworks that improve mine safety. Another significant dimension of his research includes assessing the environmental and health risks associated with groundwater quality, with a focus on sustainable resource management. His ongoing studies also explore grouting technologies, groundwater evolution processes, and the use of integrated data analysis for enhanced risk mitigation strategies.

Award:

In recognition of his outstanding scholarly contributions, Dr. Ding Jia has been acknowledged through academic honors and professional commendations. His pioneering work in mine water risk assessment and groundwater protection has positioned him as a leading researcher in his field. These recognitions highlight his ability to contribute both to the scientific community and to practical advancements that improve industry safety standards and environmental sustainability. His dedication to impactful research has earned him a strong reputation in professional networks and has reinforced his standing as a deserving candidate for prestigious awards.

Selected Publication:

Comparative Study on Prediction Methods for Water Inflow in Regional High-Intensity Water Inrush Mine Clusters: A Case Study of Xiaozhuang Coal Mine, 2025, 12 citations

Research Progress on Identification of Mine Water Inrush Sources: A Visual Analysis Perspective, 2025, 8 citations

A Study of Interbedded Groundwater Contamination in a Mining Area and the Process of Grouting Composite Failure, 2023, 15 citations

Spatial Evolution Analysis of Groundwater Chemistry, Quality, and Fluoride Health Risk in Southern Hebei Plain, China, 2023, 20 citations

Conclusion:

Dr. Ding Jia exemplifies the qualities of a leading researcher whose work has advanced knowledge and practical solutions in the field of hydrogeology and mining engineering. His contributions to groundwater safety, environmental protection, and sustainable mining practices are evident through his impactful publications, collaborative research, and leadership roles in professional communities. With a proven track record of delivering high-quality research that addresses real-world challenges, he represents an ideal candidate for recognition in this award nomination. The depth and breadth of his expertise, combined with his continued commitment to advancing science and industry standards, make Dr. Ding Jia not only a deserving awardee but also a future leader with the potential to shape innovative solutions in hydrogeology and environmental sustainability.

 

 

Lixiang Xue | Cancer | Best Researcher Award

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Prof. Lixiang Xue | Cancer | Best Researcher Award

Prof. Lixiang Xue | Cancer | Director at Peking University Third Hospital | China

Prof. Xue Lixiang is a distinguished scholar in the field of biomedical sciences, recognized for his pioneering contributions to cancer biology, immunology, and molecular medicine. His research has significantly advanced the understanding of how molecular mechanisms such as microRNAs, chromatin dynamics, and immune modulation influence cancer development, progression, and treatment response. With a strong background in translational research, he has integrated experimental biology with clinical applications, aiming to create innovative solutions for some of the most pressing medical challenges. His scholarship is evidenced by extensive publications in high-impact journals, citations that reflect wide recognition of his work, and consistent involvement in collaborative projects that bridge basic science and clinical practice.

Academic Profile:

ORCID

Google Scholar

Education:

Prof. Xue Lixiang completed his doctoral studies in biomedical sciences with a concentration on molecular and cellular biology. His academic journey has been marked by rigorous training in epigenetics, cancer biology, and immunotherapy. During his education, he cultivated a multidisciplinary foundation, drawing on molecular biology, bioinformatics, and translational medicine. His strong academic record and advanced research training have enabled him to contribute to complex investigations that require both theoretical depth and practical application. His education provided the platform from which he has launched a career dedicated to innovative and impactful scientific contributions.

Experience:

Prof. Xue Lixiang has built a career of remarkable breadth and depth, holding academic and research roles that have allowed him to influence both scholarship and practice. His professional experience spans laboratory research, international collaborations, and leadership in research projects addressing cancer diagnostics, immunological therapies, and chronic disease management. He has contributed extensively to peer-reviewed literature, with his work indexed in leading databases such as Scopus and Web of Science. In addition, he has taken part in editorial duties, peer-review processes, and the mentorship of emerging scientists, strengthening academic communities and promoting high-quality research standards. Through his extensive experience, he has become a central figure in advancing the global dialogue on cancer biology and translational medicine.

Research Interest:

The research interests of Prof. Xue Lixiang encompass cancer biology, immunology, epigenetics, and molecular therapeutics. He has devoted substantial effort to exploring the regulatory functions of microRNAs in cancer progression and therapy, highlighting their potential as biomarkers and therapeutic targets. His work on tumor-associated macrophages, cellular senescence, and immune cell modulation has shed light on new therapeutic strategies for oncology. Additionally, he has contributed to understanding the role of gut microbiota in cardiovascular and metabolic diseases, illustrating his ability to bridge oncology with broader health issues. His research demonstrates a consistent commitment to addressing complex biomedical challenges by integrating molecular discoveries with clinical potential, advancing both scientific knowledge and patient care.

Award:

Prof. Xue Lixiang has been recognized for his significant contributions through various research awards and academic honors. These recognitions reflect the high regard in which his peers and the broader scientific community hold his work. His achievements have been acknowledged not only through citations and journal publications but also through invitations to participate in international collaborations, research panels, and academic forums. Such recognition is a testament to the impact and relevance of his contributions in shaping the future of biomedical sciences.

Selected Publications:

  • Circulating microRNAs as potential cancer biomarkers: the advantage and disadvantage (2018) – Citations: 564

  • Independence of repressive histone marks and chromatin compaction during senescent heterochromatic layer formation (2012) – Citations: 330

  • Tumor-associated macrophages: potential therapeutic strategies and future prospects in cancer (2021) – Citations: 293

  • MicroRNA-31 reduces inflammatory signaling and promotes regeneration in colon epithelium (2019) – Citations: 210

Conclusion:

In conclusion, Prof. Xue Lixiang is a visionary researcher whose scholarship has reshaped understanding in cancer biology, immunology, and epigenetics. His academic training, professional experience, and internationally recognized publications establish him as a leading authority in his field. Through sustained research excellence, he has advanced the development of innovative therapeutic approaches and opened new pathways for clinical application. The recognition of his work through citations, awards, and collaborations underscores his influence in both academic and clinical communities. Prof. Xue Lixiang is highly deserving of this award nomination, not only for his past achievements but also for his potential to drive future advancements in biomedical research and global health.

 

 

Xiaoshui Huang | Multimodal | Best Researcher Award

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Dr. Xiaoshui Huang | Multimodal | Best Researcher Award

Dr. Xiaoshui Huang | Multimodal | Assistant Professor at Shanghai Jiao Tong University | China

Dr. Xiaoshui Huang is a leading researcher in artificial intelligence, computer vision, and data-driven modeling, with an outstanding record of contributions in the fields of 3D vision, point cloud registration, and AI-driven healthcare. His scholarly achievements demonstrate a commitment to bridging the gap between theoretical research and applied solutions that address pressing challenges in healthcare, robotics, and intelligent systems. Widely recognized in the academic community, Dr. Xiaoshui Huang has published influential articles in high-impact journals and conferences, attracting thousands of citations and recognition from peers across the world. His career reflects a trajectory of innovation, global collaboration, and leadership in advancing the frontiers of artificial intelligence research.

Academic Profile:

ORCID

Scopus

Google Scholar

Education:

Dr. Xiaoshui Huang pursued his doctoral studies at the University of Technology Sydney, where he focused on computer vision and artificial intelligence. His Ph.D. work established a strong foundation for his career in machine learning and advanced data processing, particularly emphasizing multimodal data analysis, 3D vision, and healthcare applications. Prior to his doctoral research, he engaged in extensive training in computer science and information technology, gaining expertise in algorithm design, statistical modeling, and computational systems. His educational journey equipped him with the technical and analytical skills that have enabled him to lead cutting-edge research at the intersection of computer vision and intelligent systems.

Experience:

Dr. Xiaoshui Huang has accumulated rich academic and research experience through his involvement with leading global institutions and interdisciplinary projects. He has collaborated extensively with colleagues from the University of Technology Sydney, Shanghai Jiao Tong University, and Tsinghua University, building strong research networks across continents. His career is marked by impactful participation in international research projects involving 3D data analysis, AI for healthcare, and multimodal learning systems. In addition to research, he has actively contributed to academic communities by mentoring students, reviewing for prestigious journals, and organizing conference sessions. His experience highlights his dual commitment to advancing knowledge and fostering the next generation of researchers in artificial intelligence.

Research Interest:

The research interests of Dr. Xiaoshui Huang span across artificial intelligence, computer vision, and multimodal data processing, with a particular emphasis on point cloud registration, 3D reconstruction, and AI applications in healthcare. He is also deeply involved in generative models and interpretable machine learning frameworks that enable more transparent and reliable decision-making in critical areas such as medicine and robotics. His work contributes to advancing technologies in automated navigation, visual understanding, and clinical diagnostic support. Dr. Xiaoshui Huang’s research embodies a forward-looking vision of integrating computational intelligence with societal needs, ensuring that AI solutions are not only technically robust but also socially impactful.

Award:

Dr. Xiaoshui Huang has been consistently recognized for his scholarly achievements and contributions to the field of artificial intelligence. His work has received widespread acknowledgment through high citation metrics, collaborative invitations, and his involvement in competitive research projects. His research output, published in IEEE and other top-tier venues, reflects both quality and impact, positioning him as a deserving nominee for this award. His recognition goes beyond academic circles, as his work has applications in healthcare, robotics, and large-scale data analysis, contributing to advancements that directly benefit society.

Selected Publication:

  • A comprehensive survey on point cloud registration (2021) – 420 citations

  • Feature-metric registration: A fast semi-supervised approach for robust point cloud registration without correspondences (2020) – 361 citations

  • Attention-based transactional context embedding for next-item recommendation (2018) – 287 citations

  • Clip2point: Transfer clip to point cloud classification with image-depth pre-training (2023) – 200 citations

Conclusion:

In summary, Dr. Xiaoshui Huang is an accomplished researcher whose career has been defined by innovation, collaboration, and leadership in artificial intelligence and computer vision. His extensive body of work, strong citation impact, and role in advancing AI applications in healthcare and 3D vision establish him as a pioneer in the field. Beyond research, his contributions to mentorship, academic service, and global collaborations further amplify his influence within the scientific community. With a proven track record of achievements and a clear potential for future breakthroughs, Dr. Xiaoshui Huang stands out as a deserving candidate for recognition in this award nomination.