Zihan Deng | Artificial Intelligence | Best Researcher Award

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Dr. Zihan Deng | Artificial Intelligence | Best Researcher Award

Harbin Institute of Technology, China

Zihan Deng is a young and accomplished researcher in the field of imaging technology and computational tomography, with a strong foundation in deep learning and artificial intelligence. With a robust academic background and an array of interdisciplinary experiences, Deng has made significant contributions through high-impact publications, competitive grants, and patents. His expertise lies at the intersection of optical instrumentation and medical image analysis, and he continues to actively engage in scientific exploration with promising results.

Profile

Orcid

Education

Deng completed his undergraduate studies in Computer Science and Technology at Harbin Engineering University (2019–2023), ranking in the top 5% of his class. His academic curriculum included rigorous coursework in mathematics and computer science, scoring consistently above 90 in core subjects. He was subsequently recommended for direct admission into the graduate program at Harbin Institute of Technology, where he is currently pursuing his Master’s degree at the Institute of Ultra-Precision Optical Instrument Engineering under the mentorship of Professor Junning Cui and Academician Jiubin Tan. His research spans CT reconstruction, deep learning-based image enhancement, and X-ray detection technologies.

Experience

Deng has accumulated diverse experience through internships and collaborative projects. He served in leadership roles within student organizations and academic competitions, including receiving awards in national-level modeling and software contests. He undertook summer research at Tsinghua University’s IDG/McGovern Brain Research Institute and was later selected to join Germany’s PTB “Chief Engineer Class” as a visiting scholar. Professionally, he interned with Chengdu Shuzhilian Technology and Guangzhou CVTE, where he contributed to image processing and video enhancement projects. He has also played key roles in multimillion-yuan research collaborations with institutions like CGN Research Institute and GF High-End Semiconductor Imaging Systems.

Research Interest

Deng’s research interests revolve around imaging technology, deep learning, and CT reconstruction methods. He focuses on developing advanced algorithms for sparse-angle computed tomography, artifact reduction, and multi-view image correction using neural networks. His work integrates domain-specific knowledge from instrumentation science with state-of-the-art machine learning frameworks to improve image quality in both medical diagnostics and industrial inspection. He also investigates beam hardening correction and reconstruction under large field-of-view (FOV) conditions, addressing challenges in high-precision imaging systems.

Award

Over the course of his academic journey, Deng has received 11 scholarships and numerous accolades. These include five first-class and two second-class academic scholarships from Harbin Engineering University, the prestigious Xiaomi Scholarship, and the Outstanding Youth League Member Award. His undergraduate thesis on sparse-angle CT reconstruction was selected as an Excellent Graduation Project (top 2%). He has also won national-level awards in competitions such as the Mathematical Modeling Contest and the English Proficiency Championship.

Publication

Deng has authored or co-authored several influential papers in prestigious journals and conferences. His representative publications include:

  1. Deng Z., Wang Z., et al. (2024). “COO-DuDo: Computation Overhead Optimization Methods for Dual-Domain Sparse-View CT Reconstruction”, Expert Systems with Applications (JCR Q1, IF=7.5, in press) – cited in advanced CT algorithm research.

  2. Deng Z., Wang Z., Lin L., Wang S., Cui J. (2024). “Research on the Effectiveness of Multi-View Slice Correction Technology Based on Deep Learning in High-Pitch Spiral Scanning Reconstruction”, Journal of X-Ray Science and Technology (JCR Q2, IF=3.0) – applied in spiral CT systems.

  3. Wang Z.#, Deng Z.#, Liu F., et al. (2023). “OSNet & MNetO for Linear Computed Tomography in Multi-Scenarios”, IEEE Transactions on Instrumentation and Measurement (JCR Q1, IF=5.6) – widely cited in instrumentation imaging.

  4. Deng Z., Deng K., Wang Z., et al.. “Small Class Discussion-Based Teaching in Instrumentation Education”, The International Journal of Education – cited in engineering education reform discussions.

  5. Li Z., Li K., Deng Z., et al. (2024). “Assessment of Sheetlet Thickness in Human Left Ventricular Free Wall Using X-ray Phase-Contrast Microtomography”, Medical Image Analysis (JCR Q1, IF=10.9, accepted) – applied in cardiovascular research.

  6. Deng Z., Wang Z., Lin L., et al. (2025). “Computation Overhead Optimization Dual-Domain Network for Sparse-View CT Reconstruction”, ICASSP 2025 (CCF-B Conference) – in review, expected to support efficient CT image pipelines.

  7. Deng Z., Wang Z., Lin L., Wang S. “Hel-MUNet: Mamba-Unet with Helical Encoding for Clinical High Pitch Helical CT Reconstruction”, MICCAI 2025 (under review) – aligned with cutting-edge clinical imaging methods.

Conclusion

Zihan Deng exemplifies the next generation of research professionals driving innovation in imaging and artificial intelligence. Through a blend of strong theoretical foundation, hands-on project experience, and impactful publications, he has demonstrated exceptional capability in solving complex technical problems. With continued guidance under leading scholars and global exposure, Deng is well-positioned to become a prominent figure in the advancement of smart medical imaging and intelligent instrumentation.

Mathew Habyarimana | Artificial Intelligence | Best Academic Researcher Award

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Dr. Mathew Habyarimana | Artificial Intelligence | Best Academic Researcher Award

Research Scholar at Durban University of Technology, South Africa

Mathew Habyarimana, Ph.D., is an accomplished electrical engineer with expertise in electrical machines, power electronics, and renewable energy. He is a self-motivated researcher and educator committed to advancing knowledge and mentoring students in the field of electrical engineering. With a strong background in academia and industry, he has contributed significantly to the development of energy systems, power electronics applications, and machine optimization techniques. His career spans several years in research, lecturing, and engineering roles, with a focus on intelligent power systems and electrical energy optimization.

Profile

Scopus

Education

Dr. Habyarimana obtained his Ph.D. in Electrical Engineering from the University of KwaZulu-Natal, Durban, South Africa, in September 2022. His doctoral research, funded by the Eskom Power Plant Engineering Institute (EPPEI), focused on electrical machines and power system optimization. Prior to this, he completed his MSc. in Electrical Engineering at the same institution in 2016, specializing in power electronics. His undergraduate studies were conducted at the University of Rwanda, College of Science and Technology, where he earned a BSc. in Electrical Engineering with a focus on renewable energy. His strong educational foundation has shaped his expertise in energy conversion, machine performance improvement, and sustainable energy solutions.

Experience

Dr. Habyarimana has held various academic and research positions throughout his career. Currently, he is a Postdoctoral Research Fellow at Durban University of Technology, where he is engaged in high-impact research on electrical power systems. Previously, he served as a Postdoctoral Research Fellow at the University of Johannesburg from 2023 to 2024, authoring scientific papers and presenting his findings at international conferences.

His academic contributions also include lecturing positions at Durban University of Technology, where he taught courses such as Illumination and Digital Signal Processing in the Electrical and Electronic Engineering Department. As a Senior Lecturer, he developed curricula, designed assessment tools, and guided students through complex electrical engineering concepts.

Before transitioning into academia, Dr. Habyarimana worked as a Project Engineer at Rwanda Energy Group, contributing to rural electrification projects. Additionally, he served as a mathematics tutor and lab demonstrator at the University of KwaZulu-Natal, mentoring students in power electronics and electrical machines. His extensive experience bridges theoretical research and practical engineering applications.

Research Interests

Dr. Habyarimana’s research interests lie in electrical machines, power electronics, renewable energy, and intelligent power management systems. He is particularly focused on optimizing induction motors, mitigating in-rush currents, and integrating artificial intelligence into power systems for enhanced energy efficiency. His work aims to address challenges in energy sustainability, improve motor efficiency, and develop hybrid energy systems that balance renewable and conventional energy sources.

Awards

Dr. Habyarimana has received multiple accolades for his contributions to research and innovation. He was awarded the Best Commercialization Project by the UKZN Inqubate Intellectual Property initiative in 2014. In addition, he received a Certificate of Appreciation for judging at the Eskom Expo for Young Scientists in 2015. His academic excellence is further recognized through his University Teaching Assistant certification, highlighting his dedication to education and student mentorship.

Publications

M. Habyarimana, G. Sharma, P. N. Bokoro, and K. A. Ogudo, “Intelligent power source selection for solar energy optimization,” International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, 2024.

M. Habyarimana, G. Sharma, and P. N. Bokoro, “The Effect of Tuned Compensation Capacitors in the Induction Motors,” WSEAS Transactions on Power Systems, 2024.

Habyarimana, M., Dorrell, D. G., & Musumpuka, R., “Reduction of Starting Current in Large Induction Motors,” Energies, 2022.

Habyarimana, M., Musumpuka, R., & Dorrell, D. G., “Mitigating In-rush Currents for Induction Motor Loads,” IEEE Southern Power Electronics Conference, 2021.

Habyarimana, M., & Dorrell, D. G., “Methods to reduce the starting current of an induction motor,” IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, 2017.

Venugopal, C., Subramaniam, P. R., & Habyarimana, M., “A Fuzzy Based Power Switching Selection for Residential Application to Beat Peak Time Power Demand,” Intelligent Decision Support Systems for Sustainable Computing, 2017.

Habyarimana, M., & Venugopal, C., “Automated hybrid solar and mains system for peak time power demand,” International Conference on the Domestic Use of Energy, 2015.

Conclusion

Dr. Mathew Habyarimana is a distinguished electrical engineer and researcher whose work significantly impacts electrical power systems and renewable energy integration. His extensive experience in academia and industry, coupled with his research contributions, underscores his commitment to innovation in energy optimization and power electronics. Through his lecturing, mentoring, and research initiatives, he continues to shape the next generation of electrical engineers while advancing knowledge in intelligent power management and sustainable energy solutions.

Daemin Shin | Computer Science | Academic Luminary Achievement Award

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Dr. Daemin Shin | Computer Science | Academic Luminary Achievement Award

Manager at Financial Security Institute (FSI), South Korea

Daemin Shin is a Manager at the Financial Security Institute, where he has been actively involved in advancing financial security measures since April 2015. With expertise in cloud security, Zero Trust security models, and data security, he has played a significant role in shaping secure financial infrastructures. Before his current role, he was a Senior Researcher at the Financial Security Research Institute from July 2012 to April 2015. His contributions to financial cybersecurity research have been instrumental in addressing security threats and enhancing the resilience of financial institutions. Shin continues to lead innovative research and development in financial security.

Profile

Scopus

Education

Daemin Shin earned his Master of Science in Engineering from the Graduate School of Information Security at Korea University, South Korea, in February 2009. He further pursued his Ph.D. in Engineering at the Department of Information Security, Soonchunhyang University, South Korea, which he successfully completed in February 2020. His academic journey reflects a strong foundation in cybersecurity, particularly focusing on financial security, cloud computing, and data protection. Throughout his education, he has been deeply engaged in research on securing financial transactions and developing security frameworks for modern digital finance ecosystems.

Experience

Shin has over a decade of experience in the field of financial security, with a strong emphasis on cloud security, data protection, and Zero Trust architectures. He started his career as a Senior Researcher at the Financial Security Research Institute, where he contributed to innovative research projects on financial cybersecurity from 2012 to 2015. Since April 2015, he has been serving as a Manager at the Financial Security Institute, where he continues to work on financial security infrastructure, cybersecurity policies, and security compliance strategies. His professional experience has significantly contributed to the development of robust security measures for the financial sector.

Research Interests

Shin’s research interests primarily focus on cloud security, financial security, and Zero Trust security models. He has conducted extensive research on securing cloud-based financial infrastructures, ensuring compliance with regulatory requirements, and mitigating security threats in digital finance. His recent works include studies on security considerations for DevSecOps software supply chains and Zero Trust evaluation frameworks tailored for financial institutions. His expertise in these domains has positioned him as a thought leader in enhancing cybersecurity resilience in the financial industry.

Awards and Recognitions

Shin has been recognized for his outstanding contributions to financial security and cybersecurity research. He has been nominated for the Best Researcher Award in recognition of his groundbreaking research on cloud security and financial security frameworks. His efforts in improving security compliance policies and implementing Zero Trust methodologies in financial institutions have gained widespread recognition. Shin’s work has had a substantial impact on the cybersecurity domain, making financial transactions and data storage more secure against emerging threats.

Publications

D. Shin, V. Sharma, J. Kim, S. Kwon, and I. You (2017). “Secure and Efficient Protocol for Route Optimization in PMIPv6-Based Smart Home IoT Networks,” IEEE Access, vol. 5, pp. 11100-11117, DOI: 10.1109/ACCESS.2017.2710379. Cited by 200+ articles.

D. Shin, K. Yun, J. Kim, P. V. Astillo, J.-N. Kim, and I. You (2019). “A Security Protocol for Route Optimization in DMM-Based Smart Home IoT Networks,” IEEE Access, vol. 7, pp. 142531-142550, DOI: 10.1109/ACCESS.2019.2943929. Cited by 150+ articles.

Shin, Daemin, Kim, Jiyoon, & You, Ilsun (2023). “국내 금융구득 클라우드 전환 동형 및 보안,” REVIEW OF KIISC, 33(5), 57-68. Cited by 50+ articles.

Shin, Daemin, You, Ilsun, and Kim, Jiyoon (2024). “국내 금융구득 클라우드 보안 위험 및 보안 요구사항에 관한 연구,” Journal of Next-Generation Computing, 20(4), 77-96, DOI: 10.23019/kingpc.20.4.202408.007. Cited by 30+ articles.

Daemin Shin, Jiyoon Kim, I Wayan Adi Juliawan Pawana, Ilsun You (2025). “Enhancing Cloud-Native DevSecOps: A Zero Trust Approach for the Financial Sector,” Computer Standards & Interfaces, DOI: 10.1016/j.csi.2025.103975. Cited by 20+ articles.

Conclusion

Daemin Shin’s dedication to advancing financial security and cybersecurity has been instrumental in shaping modern security frameworks for financial institutions. His research on cloud security, Zero Trust models, and DevSecOps methodologies continues to drive innovation in securing financial infrastructures. With a strong academic and professional background, he remains committed to developing secure financial ecosystems and mitigating cybersecurity risks in an ever-evolving digital landscape. His contributions have earned him significant recognition, making him a leading figure in financial security research.