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

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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.

Gabriel Osei Forkuo | Machine Learning | Best Researcher Award

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Mr. Gabriel Osei Forkuo | Machine Learning | Best Researcher Award

Doctoral Researcher/ Research Assistant at Transilvania University of Brasov, Romania

Gabriel Osei Forkuo is a dedicated forestry specialist and researcher with an extensive background in forest operations engineering, postural ergonomics, and machine learning applications. He has built a career that merges practical field experience with academic research, contributing significantly to the development of innovative and cost-effective technologies in forest monitoring and conservation. Currently pursuing a Ph.D. in Forest Operations Engineering at Transilvania University of Brasov, Romania, Gabriel has emerged as a leading figure in the exploration of low-cost LiDAR technologies and smart solutions for ergonomic assessments in forestry. His multifaceted expertise is grounded in over two decades of professional service in teaching, field operations, and advanced scientific investigations.

Profile

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Education

Gabriel’s educational journey is marked by academic excellence and a continuous drive for specialized knowledge. He is currently enrolled in a Ph.D. program in Forest Operations Engineering at Transilvania University of Brasov, where his research focuses on integrating machine learning and computer vision for ergonomic assessments in forest operations. He previously earned a Master’s degree in Multiple Purpose Forestry from the same university, achieving excellent grades and a cumulative ECTS average of 9.76. His foundational studies include a Bachelor of Science degree in Natural Resources Management from Kwame Nkrumah University of Science and Technology, Kumasi, Ghana, where he graduated with First Class Honours. Earlier academic milestones include completing his GCE A-Level in science subjects and his GCE O-Level in science, supported by performance scholarships recognizing his consistent academic distinction.

Experience

Gabriel’s professional experience spans across teaching, research, and forest management. Between 2002 and 2011, he worked as a Forest Range Manager and Supervisor at the Forestry Commission Ghana, where he was instrumental in nursery planning, restoration of degraded forests, and report writing. From 1999 to 2001, he served as a Science and Maths Teacher at Maria Montessori School in Kumasi, followed by a role as a Teaching Assistant at his alma mater, Kwame Nkrumah University of Science and Technology. In this capacity, he conducted laboratory classes, supervised research data collection, and participated in academic presentations, establishing a strong foundation in both pedagogical and research methodologies. His leadership in afforestation programs and practical forest management further reflects his field-based competency and organizational capability.

Research Interest

Gabriel’s research interests are centered on forest operations engineering, with a special focus on postural ergonomics, machine learning applications, and smart technologies for environmental monitoring. He is passionate about developing affordable and efficient technological solutions, particularly the use of mobile LiDAR and AI-driven tools for soil disturbance estimation and posture evaluation in forest labor. His interdisciplinary approach merges forestry, computer science, and ergonomics, contributing to sustainable and safe forestry practices. Through these interests, he aims to bridge the gap between traditional forestry operations and modern intelligent systems.

Award

Gabriel’s academic and professional contributions have been recognized through several prestigious scholarships and awards. He has twice secured first place in the “My Bachelor/Dissertation Project” competitions held in 2022 and 2023, scoring nearly perfect marks. In 2022, he received the “Premiul special pentru studenti straini” award at the Premiul AFCO. He has also been a recipient of multiple scholarships, including the Transilvania Academica Scholarship, UNITBV Ph.D. Scholarship for International Graduates, and funding from “Proiectul Meu de Diploma” programs. Earlier in his career, he was awarded performance scholarships by the Government of Ghana and Poku Transport Ghana for his outstanding performance in forest sciences.

Publication

Gabriel has authored several notable publications that demonstrate his expertise in forest operations and technological innovation. His key works include:

Forkuo, G.O., & Borz, S.A. (2023). Accuracy and inter-cloud precision of low-cost mobile LiDAR technology in estimating soil disturbance in forest operations. Frontiers in Forests and Global Change, 6. Cited in multiple studies on forest soil impact monitoring.

Forkuo, G.O. (2023). A systematic survey of conventional and new postural assessment methods. Revista Padurilor, 138(3), 1-34.

Borz, S.A., Morocho Toaza, J.M., Forkuo, G.O., Marcu, M.V. (2022). Potential of measure app in estimating log biometrics: a comparison with conventional log measurement. Forests, 13(7), 1028.

Borz, S.A., Forkuo, G.O., Oprea-Sorescu, O., & Proto, A.R. (2022). Development of a robust machine learning model to monitor the operational performance of sawing machines. Forests, 13(7), 1115.

Forkuo, G.O., Proto, A.R., & Borz, S.A. (2024). Feasibility of low-cost mobile LiDAR technology in estimating soil disturbance in forest operations. SSRN.

Forkuo, G.O. (1999). Post-fire tree regeneration studies in the Kumawu Water Supply Forest Reserve. B.Sc. Thesis, KNUST-Kumasi.

Presented paper at FORMEC 2023 in Florence, Italy, highlighting applications of mobile LiDAR in operational environments.

Conclusion

Gabriel Osei Forkuo exemplifies the intersection of academic rigor, practical expertise, and technological innovation in the field of forest operations. His work continues to advance the integration of smart technologies into sustainable forestry, driven by a deep commitment to both ecological preservation and worker safety. Through his research, publications, and leadership roles, Gabriel has built a profile of excellence, contributing significantly to forestry engineering and shaping the next generation of sustainable forest management solutions.