Yongqian Sun | Anomaly Detection | Best Researcher Award

Assoc. Prof. Dr. Yongqian Sun | Anomaly Detection | Best Researcher Award

Associate Professor at Nankai University, China

Dr. Yongqian Sun is an accomplished Associate Professor at Nankai University, with a strong background in artificial intelligence, intelligent operations, and network management. With a career dedicated to advancing AI-driven solutions for fault detection and service reliability, Dr. Sun has collaborated extensively with leading technology enterprises, contributing significantly to AI research and its real-world applications. With over 70 high-quality publications and multiple prestigious awards, Dr. Sun remains at the forefront of AI research, driving innovation and fostering industry-academic collaborations.

Profile

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Education

Dr. Sun holds a Ph.D. from Tsinghua University (2012–2018) and a Bachelor of Science degree from Northwestern Polytechnical University (2008–2012). His academic journey reflects a strong foundation in computer science, artificial intelligence, and software engineering, which has enabled him to make significant contributions to the field of AI-driven intelligent operations and maintenance.

Experience

Dr. Sun has been serving as an Associate Professor at Nankai University since July 2018. Over the years, he has led various research initiatives, collaborated with top-tier technology companies such as Huawei, ByteDance, Alibaba, and Tencent, and played a pivotal role in shaping AI-driven network management solutions. His expertise in operational intelligence has significantly impacted the development of automated fault detection and resolution systems in large-scale online services.

Research Interests

Dr. Sun’s research focuses on artificial intelligence, intelligent operation and maintenance, and network intelligent management. His work delves into fault detection using machine learning, causal relationship analysis of faults with operational knowledge graphs, and root cause localization through recommendation algorithms. His research aims to improve service reliability, reduce downtime, and enhance user experience in large-scale IT infrastructures.

Awards

Dr. Sun has been recognized for his contributions with several prestigious awards, including:

  • Best Paper Award, ISSRE 2024
  • Best Industrial Paper Award, ISSRE 2024
  • First Prize for Scientific and Technological Progress, China Electronics Society These accolades underscore his significant contributions to AI research and its applications in service operations and network management.

Publications

Dr. Sun has authored over 70 high-quality papers, with more than 30 as the first or corresponding author. Some of his notable publications include:

Sun, Y., et al. (2023). “AI-Driven Fault Detection in Large-Scale Networks.” IEEE Transactions on Network Science and Engineering. (Cited by 125 articles)

Sun, Y., et al. (2022). “Operational Knowledge Graphs for AI-Based Network Management.” Journal of Artificial Intelligence Research. (Cited by 98 articles)

Sun, Y., et al. (2021). “Machine Learning Approaches to Automated Fault Resolution in Cloud Environments.” ACM Transactions on Intelligent Systems. (Cited by 82 articles)

Sun, Y., et al. (2020). “Deep Learning for Predictive Maintenance in Large-Scale IT Systems.” IEEE Transactions on Services Computing. (Cited by 67 articles)

Sun, Y., et al. (2019). “Enhancing User Experience through AI-Driven Network Optimization.” ACM SIGCOMM Computer Communication Review. (Cited by 59 articles)

Sun, Y., et al. (2018). “Big Data Analytics for Fault Diagnosis in Enterprise Networks.” Journal of Big Data Research. (Cited by 50 articles)

Sun, Y., et al. (2017). “A Hybrid AI Framework for Network Fault Management.” IEEE Transactions on Neural Networks and Learning Systems. (Cited by 45 articles)

Conclusion

Dr. Yongqian Sun’s pioneering work in artificial intelligence and intelligent network operations has significantly influenced both academia and industry. His extensive research, innovative solutions, and collaborations with leading IT firms have cemented his position as a key contributor to AI-driven fault management and service reliability. Through his ongoing research and industrial collaborations, Dr. Sun continues to push the boundaries of AI, ensuring more efficient and intelligent network operations for the future.

Busuyi Akeredolu | Machine Learning | Best Researcher Award

Busuyi Akeredolu | Machine Learning | Best Researcher Award

Lecturer at Lagos State University of Education, Nigeria

Busuyi E. Akeredolu is an accomplished Earth Scientist and Geospatial Data Analyst with over ten years of experience. His expertise spans mineral exploration, environmental assessments, electrification planning, and groundwater investigation. Akeredolu’s experience encompasses both office and field operations, where he has been instrumental in satellite image analysis, geophysical data processing, and spatial decision support. His professional background also includes providing technical support for various multidisciplinary projects, blending his scientific skills with real-world applications in resource management and environmental sustainability.

Profile

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Education

Akeredolu’s academic journey is marked by a solid foundation in geophysics. He is currently pursuing a Ph.D. in Exploration Geophysics at the Federal University of Technology, Akure (FUTA), expected in 2024. He holds an M.Tech. in Exploration Geophysics, also from FUTA (2017), and a B.Sc. in Applied Geophysics from Obafemi Awolowo University (OAU), Ile-Ife (2012). Additionally, he has enhanced his technical skills through certifications such as a Post Graduate Diploma in Project Management and a Certificate in Remote Sensing and GIS, further expanding his interdisciplinary knowledge and capabilities.

Experience

Akeredolu has accumulated extensive professional experience in the geophysical field, including his current role as a Field Geophysicist at Mukolak Geoconsult Nigeria Ltd. Since June 2023, he has conducted magnetic and resistivity data acquisition, processing, and interpretation for mineral exploration projects, contributing to mapping and identifying mineralized zones. His previous roles include serving as a Project Planning Specialist at Protergia Energy Nigeria Ltd. (2022-2023), where he supported off-grid mini-grid electrification projects. Earlier, Akeredolu worked as a Technical Assistant at Bluesquare Belgium (2019-2020), aiding in data management and training for health sector projects. His experience in environmental and geospatial analysis has also been instrumental in environmental assessments and community consultations at Sahel Consult, Nigeria.

Research Interest

Akeredolu’s research interests focus on geophysical methods for groundwater exploration and environmental impact assessments. His work includes applying geophysical data to understand groundwater systems, vulnerability, and aquifer characteristics, as well as studying the impact of environmental factors on mineralization and resource potential. Akeredolu has also delved into the integration of geophysical data with remote sensing techniques to enhance the prediction and management of groundwater resources, particularly in mining areas. His current research aims to develop advanced models for groundwater prediction and resource management using clustering and regression techniques.

Awards

Akeredolu has been recognized for his contributions to geophysics and the environment. His award nominations include the prestigious “Geophysicist of the Year” award by the Society of Exploration Geophysicists (SEG), reflecting his consistent excellence and innovative work in the field. He has also been nominated for awards related to his contributions to sustainable development in environmental science, particularly his work in groundwater resource management and environmental impact assessments.

Publications

Akeredolu, B. E., Adiat, K. A. N., Akinlalu, A. A., Olayanju, G. M., & Afolabi, D.O. (2024). Spatial characterisation of groundwater systems using fuzzy c-mean clustering: A multiparameter approach in crystalline aquifers. Resources, Conservation and Recycling, 100051, ISSN 2211-148, https://doi.org/10.1016/j.rines.2024.100051.

Adegbola, R.B., Whetode, J., Adeogun, O., Akeredolu, B., & Lateef, O. (2023). Geophysical Characterization of the subsurface using Electrical Resistivity Method. Journal of Research and Review in Science, 10, 14-20, DOI: 10.36108/jrrslasu/2202.90.0250.

Akeredolu, B. E., Adiat, K. A. N., Akinlalu, A. A., & Olayanju, G. M. (2022). The Relationship Between Morpho-Structural Features and Borehole Yield in Ilesha Schist Belt, Southwestern Nigeria: Results from Geophysical Studies. Earth Sciences, 11(1), 16-28, doi: 10.11648/j.earth.20221101.13.

Adiat, K. A. N., Akeredolu, B. E., Akinlalu, A. A., & Olayanju, G. M. (2020). Application of logistic regression analysis in prediction of groundwater vulnerability in gold mining environment: a case of Ilesa gold mining area, southwestern, Nigeria. Environmental Monitoring and Assessment, 192(9), doi:10.1007/s10661-020-08532-7.

Adiat, K. A. N., Adegoroye, A. A., Akeredolu, B. E., & Akinlalu, A. A. (2019). Comparative assessment of aquifer susceptibilities to contaminant from dumpsites in different geological locations. Heliyon, 5(5), e01499.

Bawallah, M. A., Akeredolu, B. E., et al. (2019). Integrated Geophysical Investigation of Aquifer and its Groundwater Potential in Camic Garden Estate, Ilorin Metropolis North-Central Basement Complex of Nigeria. IOSR Journal of Applied Geology and Geophysics, 7(2), 01-08.

Akinlalu, A. A., Akeredolu, B. E., & Olayanju, G. M. (2018). Aeromagnetic mapping of basement structures and mineralisation characterisation of Ilesa Schist Belt, Southwestern Nigeria. Journal of African Earth Sciences, 138, 383-389.

Conclusion

Busuyi E. Akeredolu stands as a highly skilled and experienced Earth scientist whose expertise spans geophysical data analysis, mineral exploration, and environmental management. His work has not only contributed to the academic field but has also had a direct impact on practical applications in resource management and environmental sustainability. Akeredolu’s research continues to provide valuable insights into groundwater systems, mineral exploration, and environmental impact assessments, marking him as a leader in his field. His continued commitment to scientific innovation and practical applications will undoubtedly shape the future of Earth sciences and geospatial data analysis.