Wei Zhang | Image Processing | Excellence in Research

Prof. Wei Zhang | Image Processing | Excellence in Research

Professor at Harbin Institute of Technology, China

Wei Zhang is an accomplished researcher and Assistant Professor at Harbin Institute of Technology, specializing in computer science and technology. He holds a Doctorate in Computer Science and Technology, with his research focusing on medical image processing based on deep learning. Zhang’s work encompasses various areas, including object detection, object tracking, and semantic segmentation. He has contributed to numerous significant projects, such as cerebral aneurysm rupture prediction and pulmonary nodule diagnosis, leveraging artificial intelligence. His collaborative efforts extend to working with national foundations and participating in key projects, enhancing his research’s geographic and practical impact. Zhang has been recognized with multiple awards, including prizes at the OI China Underwater Robot Competition and the Maritime RobotX Challenge. His publications in prestigious journals highlight his innovative contributions to his field, making him a notable figure in applied research and advanced technology development.

Profile
 Education

Wei Zhang has a solid educational background in various fields related to computer science and technology. He earned his Bachelor’s degree in Ship Building from Harbin Engineering University in 2016, where his thesis focused on motion modeling and heading control of wave gliders. He pursued a postgraduate degree in Robot Technology from Harbin Engineering University, completing it in 2019 with a thesis on detection and tracking algorithms for surface targets based on unmanned surface vehicles. In July 2024, he completed his Doctor of Computer Science and Technology at Harbin Institute of Technology, with a thesis on medical image processing based on deep learning. He is currently an Assistant Professor of Computer Science and Technology at Harbin Institute of Technology.

Professional Experience

Wei Zhang’s professional experience is primarily academic, serving as a research fellow and now as an Assistant Professor at Harbin Institute of Technology. His work revolves around intelligent interfaces and human-computer interaction, particularly in the fields of medical image processing and object detection.

Research Interests

Wei Zhang’s research interests include medical image processing, object detection, object tracking, semantic segmentation methods, hardware platform development, and vision system construction. These interests align with cutting-edge areas in computer science, particularly in the application of artificial intelligence and deep learning in medical and autonomous systems.

Research Skills

Wei Zhang has developed a range of research skills throughout his academic and professional career. These include proficiency in deep learning, algorithm development, image processing, and system construction for medical and autonomous applications. His work on various research projects demonstrates his ability to apply these skills effectively in real-world scenarios.

Research Contributions

Wei Zhang has made significant contributions to his field through various research projects. These include developing a cerebral aneurysm rupture prediction algorithm based on hemodynamics, diagnosing benign and malignant pulmonary nodules using artificial intelligence, and creating survival prediction and treatment plan recommendation algorithms for lung cancer patients. His contributions extend to low-dose CT denoising technology and tracking methods for underactuated micro-UAVs.

Geographic Impact

Wei Zhang’s work has had a significant geographic impact, particularly in China. His research at Harbin Institute of Technology and collaboration with various national foundations and universities have contributed to advancements in medical imaging and autonomous vehicle technology in the region.

Collaborative Efforts

Wei Zhang has demonstrated strong collaborative efforts in his research projects. He has worked with various institutions and researchers to advance his studies. His collaborative projects include working with the National Natural Science Foundation of China and participating in special funds for central colleges and universities.

Applied Research

Wei Zhang’s applied research focuses on practical applications of his work in medical imaging and autonomous systems. His projects on cerebral aneurysm prediction, pulmonary nodule diagnosis, and survival prediction algorithms for lung cancer patients highlight his focus on developing real-world solutions using advanced technologies.

Specific Projects and Publications

Wei Zhang has been involved in numerous specific projects and has published extensively. Some of his notable publications include research on noise extraction and denoising in low-dose CT using deep learning frameworks, brain tumor segmentation using federated learning, and pulmonary nodule detection using 3D convolutional neural networks. His work has been published in prestigious journals such as IEEE Access and the International Journal of Imaging Systems and Technology.

Environmental Health, Vector Control, Parasitology and Infectious Diseases

While Wei Zhang’s primary focus has been on medical imaging and autonomous systems, his work indirectly contributes to environmental health by advancing technologies that can be used in various health-related applications. His research in medical image processing has the potential to improve diagnostics and treatment plans, thereby contributing to better health outcomes.

Awards and Recognition

Wei Zhang has received numerous awards and recognitions for his contributions to his field. These include the Second Prize of Feasibility at the OI China Underwater Robot Competition, the Innovative Third Prize at the same competition, and the Effective Return on Investment award at the Maritime RobotX Challenge in the USA. He has also received scholarships and recognition for his academic achievements and volunteer work.

Conclusion

In conclusion, Wei Zhang’s extensive educational background, professional experience, and research contributions make him a strong candidate for the Research for Excellence in Research award. His work in medical image processing, autonomous systems, and deep learning, along with his collaborative efforts and numerous awards, demonstrate his commitment to advancing his field and making a significant impact.

Publications Top Notes

  1. A object detection and tracking method for security in intelligence of unmanned surface vehicles
    • Authors: W. Zhang, X. Gao, C. Yang, F. Jiang, Z. Chen
    • Journal: Journal of Ambient Intelligence and Humanized Computing
    • Pages: 1-13
    • Citations: 23
    • Year: 2022
  2. Research on unmanned surface vehicles environment perception based on the fusion of vision and lidar
    • Authors: W. Zhang, F. Jiang, C.F. Yang, Z.P. Wang, T.J. Zhao
    • Journal: IEEE Access
    • Volume: 9
    • Pages: 63107-63121
    • Citations: 22
    • Year: 2021
  3. Safety helmet wearing detection based on image processing and deep learning
    • Authors: W. Zhang, C. Yang, F. Jiang, X. Gao, X. Zhang
    • Conference: 2020 International Conference on Communications, Information System and …
    • Citations: 19
    • Year: 2020
  4. A review of research on light visual perception of unmanned surface vehicles
    • Authors: W. Zhang, C. Yang, F. Jiang, X. Gao, K. Yang
    • Journal: Journal of Physics: Conference Series
    • Volume: 1606 (1)
    • Article: 012022
    • Citations: 5
    • Year: 2020
  5. A water surface moving target detection based on information fusion using deep learning
    • Authors: W. Zhang, C. Yang, F. Jiang, X. Gao, K. Yang
    • Journal: Journal of Physics: Conference Series
    • Volume: 1606 (1)
    • Article: 012020
    • Citations: 4
    • Year: 2020
  6. 无人水面艇技术发展回顾与趋势分析 (Review and Trend Analysis of Unmanned Surface Vehicle Technology Development)
    • Authors: 张伟, 廖煜雷, 姜峰, 赵铁军 (W. Zhang, Y.L. Liao, F. Jiang, T.J. Zhao)
    • Journal: 无人系统技术 (Unmanned Systems Technology)
    • Volume: 2 (6)
    • Pages: 1-9
    • Citations: 3
    • Year: 2019
  7. Protecting the Ownership of Deep Learning Models with An End-to-End Watermarking Framework
    • Authors: W. Zhang, W. Cui, F. Jiang, C. Yang, R. Li
    • Conference: 2021 IEEE 20th International Conference on Trust, Security and Privacy in …
    • Citations: 1
    • Year: 2021
  8. Reducing Data Transmission Efficiency in Wireless Capsule Endoscopy through DL-CEndo Framework: Reconstructing Lossy Low-Resolution Luma Images and Improving Summarization
    • Authors: A. Salmi, W. Zhang, F. Jiang
    • Journal: Mobile Networks and Applications
    • Pages: 1-17
    • Citations: N/A
    • Year: 2024
  9. A super-resolution method of combined color image with depth map based on deep learning
    • Authors: W. Zhang, C. Yang, F. Jiang, X. Gao, K. Yang
    • Conference: Proceedings of the 2020 International Conference on Cyberspace Innovation of …
    • Citations: N/A
    • Year: 2020
  10. 水面无人艇的非稳像运动目标检测与跟踪方法 (Unsteady Image Moving Target Detection and Tracking Method for Unmanned Surface Vessels)
    • Author: 张伟 (W. Zhang)
    • Institution: 哈尔滨工程大学 (Harbin Engineering University)
    • Year: 2019

 

Rahman Farnoosh | Image Analysis Award | Excellence in Innovation

Prof. Rahman Farnoosh | Image Analysis Award | Excellence in Innovation

Academic Staff at The School of Mathematics and Computer Science, Statistics, Iran University of Science and Technology, Iran

Prof. Rahman Farnoosh is an accomplished researcher and academic with expertise in the field of image analysis. He has made significant contributions to the advancement of image processing and computer vision. His work has been recognized with prestigious awards and has been published in reputable journals and conferences. Prof. Farnoosh’s innovative research has had a positive impact on the field, inspiring others and contributing to the broader scientific community.

Professional Profiles:

Education:

Prof. Rahman Farnoosh obtained his qualifications over a span of several years, beginning with a B.Sc. in Applied Mathematics from Industrial Sharif University in Tehran, Iran, which he completed from 1978 to 1986. Building on this foundation, he pursued further studies at Shiraz University in Shiraz, Iran, where he obtained an M.Sc. in Statistics from 1986 to 1989. Prof. Farnoosh’s academic journey culminated in a Ph.D. in Statistics from Leeds University in Leeds, UK, which he completed from 1996 to 2000. His educational background reflects a strong commitment to the field of mathematics and statistics, demonstrating his dedication to advancing his knowledge and expertise in these areas.

Research Experience:

Prof. Rahman Farnoosh has conducted research in Statistics and Applied Mathematics, focusing on innovative numerical algorithms and statistical methods. His projects include developing a Numerical Algorithm based on the Monte Carlo method to enhance computational efficiency, studying Image Segmentation using Voronoi Polygons and the Markov Chain Monte Carlo method for advanced image processing, and proposing a Modified Measure of Kurtosis for heavy-tail distributions. Additionally, he has explored the application of Monte Carlo methods in solving stochastic differential equations and contributed to variance reduction methods in Monte Carlo simulations. Prof. Farnoosh’s research demonstrates his interdisciplinary approach and commitment to advancing mathematical knowledge.

Teaching Experience:

At Iran University of Science and Technology, Prof. Rahman Farnoosh teaches a variety of courses in Statistics and Applied Mathematics at different academic levels. For Ph.D. students, he covers topics like Advanced Simulation, Stochastic Differential Equations, Theory of Probability, and Applications of Monte Carlo Methods. At the M.Sc. level, he teaches courses such as Theory of Probability, Stochastic Processes and Applications, and Selected Topics in Advanced Probability. For B.Sc. students, Prof. Farnoosh offers courses including Probability and Statistics, Time Series Analysis, Ordinary Differential Equations, Numerical Computations, Stochastic Processes, and Engineering Probability and Statistics. His teaching demonstrates a commitment to imparting a strong mathematical foundation to students at all levels.

Interests:

Prof. Rahman Farnoosh has a keen interest in Financial Mathematics, focusing on probabilistic approaches for solving inverse diffusion problems. His expertise also extends to Image Analysis, Bayesian Data Analysis, and Markov Chain Monte Carlo Simulation. He has applied Monte Carlo methods to solve a variety of mathematical problems, including partial differential equations, systems of linear algebraic equations, and integral equations. Additionally, Prof. Farnoosh is skilled in the numerical solution of stochastic differential equations and has a strong interest in mathematical biology, demonstrating his diverse research interests and interdisciplinary approach to mathematics.

Skills:

Prof. Rahman Farnoosh possesses a diverse skill set in Mathematics and Statistics, including advanced mathematical modeling, numerical analysis, statistical analysis, and Monte Carlo simulation. He is proficient in image analysis and programming languages such as Python, MATLAB, and R. With a strong background in teaching, mentoring, and research, Prof. Farnoosh contributes significantly to the advancement of knowledge in these fields.

Publications:

  1. Image segmentation using Gaussian mixture model
    • Authors: R. Farnoush, PAKB ZAR
    • Year: 2008
    • Citations: 99
  2. Monte Carlo method for solving Fredholm integral equations of the second kind
    • Authors: R. Farnoosh, M Ebrahimi
    • Year: 2008
    • Citations: 84
  3. A stochastic perspective of RL electrical circuit using different noise terms
    • Authors: R. Farnoosh, P Nabati, R Rezaeyan, M Ebrahimi
    • Year: 2011
    • Citations: 40
  4. A semiparametric method for estimating nonlinear autoregressive model with dependent errors
    • Authors: R. Farnoosh, SJ Mortazavi
    • Year: 2011
    • Citations: 26
  5. Fuzzy nonparametric regression based on an adaptive neuro-fuzzy inference system
    • Authors: S Danesh, R Farnoosh, T Razzaghnia
    • Year: 2016
    • Citations: 25
  6. Numerical method for discrete double barrier option pricing with time-dependent parameters
    • Authors: R Farnoosh, A Sobhani, H Rezazadeh, MH Beheshti
    • Year: 2015
    • Citations: 23
  7. Image segmentation using Voronoi polygons and MCMC, with application to muscle fibre images
    • Authors: IL Dryden, R Farnoosh, CC Taylor
    • Year: 2006
    • Citations: 21
  8. Fuzzy regression analysis based on fuzzy neural networks using trapezoidal data
    • Authors: R Naderkhani, MH Behzad, T Razzaghnia, R Farnoosh
    • Year: 2021
    • Citations: 20
  9. Monte Carlo method via a numerical algorithm to solve a parabolic problem
    • Authors: R Farnoosh, M Ebrahimi
    • Year: 2007
    • Citations: 19
  10. A numerical method for discrete single barrier option pricing with time-dependent parameters
    • Authors: R Farnoosh, H Rezazadeh, A Sobhani, MH Beheshti
    • Year: 2016
    • Citations: 18