Mr Sajad Rezvani | Computer vision | Excellence in Research

Shahrood University of Technology , Iran

Sadjad Rezvani is a highly qualified candidate for the Research for Excellence in Research award. His impressive academic achievements, impactful research contributions, technical expertise, and leadership in mentoring make him a strong contender. His work in masked face recognition, medical image analysis, and image segmentation reflects both the depth and relevance of his research in today’s rapidly evolving tech landscape.

Publication Profile

scopus

Education :

Sadjad Rezvani holds a Master of Science in Computer Engineering with a specialization in Artificial Intelligence from Shahrood University of Technology, Iran. He completed his master’s degree between September 2020 and September 2022, graduating with a GPA of 4/4 (18.59/20). His thesis was titled “Masked Face Recognition Using Deep Learning,” under the guidance of Professor Mansoor Fateh. Prior to this, Sadjad earned his Bachelor of Science in Computer Engineering, specializing in Software Engineering, from Shahrood University of Technology, completing his degree between September 2015 and September 2019 with a GPA of 3.53/4 (16.92/20). His undergraduate thesis was titled “Profiling Web Applications to Improve Intrusion Detection,” supervised by Professor Mohsen Rezvani.

Professional Experience:

Sadjad has practical experience as a Computer Vision Software Engineer in several industries. He worked at Hookan Salt Factory in Shiraz, Iran, from November 2020 to September 2021, where he contributed to the development of a Salt Crack Sorting Machine. In this role, he employed advanced image processing techniques to detect salt impurities in real-time, utilizing tools such as OpenCV, Python, C#, and C++. Additionally, he worked at Shahaab, CO from June 2019 to December 2023 on a Plate Recognition Software project, where he contributed to a system that recognized license plates using CCTV camera data. His work involved maintaining and improving the software using C#, SQL, and other related technologies.

Research Skills:

Sadjad is highly skilled in programming languages such as Python, C++, and C#, and has a strong background in Machine Learning frameworks including PyTorch, TensorFlow, and Scikit-Learn. He is proficient in Computer Vision tools like OpenCV and has experience with databases such as Microsoft SQL Server and MySQL. His technical expertise also extends to advanced image processing, AI for medical diagnosis, and deep learning-based solutions for real-world applications.

Research Focus :

Sadjad’s research interests include Machine Learning (ML), Deep Learning (DL), Generative AI (GenAI), Medical Image Analysis, Limited Data Solutions, and Domain Adaptation. He has contributed to several journal publications, such as the development of ABANet: Attention Boundary-Aware Network for Image Segmentation (2024) and a paper on Single Image Denoising via a New Lightweight Learning-Based Model (2024), among others. His academic research also includes the application of deep learning models for lung CT image segmentation and innovations in masked face recognition using deep learning.

Awards :

Sadjad has received recognition for his achievements, including being a member of Iran’s National Elites Foundation in 2023 and being the third-ranked student in his Master of Science program. His certifications include AI for Medical Diagnosis from DeepLearning.AI (Coursera, 2023), Python Project for Data Science from IBM (Coursera, 2022), and specialization courses in Generative Adversarial Networks (GANs) and Machine Learning from Stanford University.

Honours and Awards

Member of Iran’s National Elites Foundation, 2023
Third-ranked student in the Master of Science in Computer Science program, 2022

Publication :

- Rezvani, S., Fateh, M., & Khosravi, H. (2024). ABANet: Attention Boundary-Aware Network for Image Segmentation. Expert Systems, e13625. [Published May 2024]
- Rezvani, S., Soleymani Siahkar, F., Rezvani, Y., Alavi Gharahbagh, A., & Abolghasemi, V. (2024). Single Image Denoising via a New Lightweight Learning-Based Model. IEEE Access, August 2024.
- Rezvani, S., Fateh, M., Fateh, A., & Jalali, Y. (2024). FusionLungNet: Multi-scale Fusion Convolution with Refinement Network for Lung CT Image Segmentation. Biomedical Signal Processing and Control, Revised Sep 2024.

conclusion:

Sadjad’s overall profile is well-rounded with strengths across research, academia, technical skills, and professional experience.
Continued focus on expanding publication reach, collaboration, and public speaking could further elevate his visibility and impact in the research community.
With his dedication and achievements, Sadjad is well-positioned for recognition in research excellence.

In conclusion, Sadjad is a strong candidate for the award, and with a few adjustments in outreach and collaboration, he could continue to make significant strides in the research world.

Mr. Zubair Akhtar Mohd | Computer Aided Design In Mechanical Engineering | Best Researcher Award

Mr. Zubair Akhtar Mohd, Technische Hochschule Ingolstadt, Germany

Mr. Zubair Akhtar Mohd is a Research Associate at Technische Hochschule Ingolstadt, Germany, specializing in automotive engineering and artificial intelligence applications in predictive modeling and manufacturing optimization. He holds a Master’s degree in Automotive Engineering from THI and a Bachelor’s in Mechanical Engineering from Aligarh Muslim University, India. His research focuses on integrating Finite Element Analysis (FEA) with AI, using advanced machine learning algorithms like CNNs and RNNs to forecast the lifespan of electronic components. Mr. Mohd is also involved in scientific projects, data generation for materials testing, and academic teaching in CAD and simulation.

Professional Profiles:

🎓 Education :

Holds a Master’s degree in Automotive Engineering from Technische Hochschule Ingolstadt, Germany, with a GPA of 1.9, focusing on production optimization and AI in automotive systems. Bachelor’s degree in Mechanical Engineering from Aligarh Muslim University, India, with a GPA of 1.6, specializing in vehicle technology and CAD/CAE programming.

🏢 Experience:

Currently working as a Research Associate at the Institute of Innovative Mobility, Technische Hochschule Ingolstadt, focusing on method development for predicting electronics component lifespan using deep learning. Previously employed as a working student at CADS Engineering GmbH, contributing to vehicle design and occupant protection research, and as an Industrial Engineer in India, implementing safety and efficiency improvements in manufacturing processes.

🛠️Skills:

Proficient in Python, TensorFlow, PyTorch, and Linux, with additional expertise in tools such as Git, JavaScript, and MS Office applications. Experience with HTML, CSS, and Carla, and extensive knowledge in engineering software like NX CAD, Ansys, and Tableau. Fluent in English and German at B2 level, alongside native proficiency in Hindi.

Research Focus :

Specialized in the integration of Finite Element Analysis (FEA) simulation data with deep learning for predictive modeling. Research includes advanced deep learning models, such as CNNs and RNNs, and generative forecasting with VQ-VAE. Emphasis on machine learning algorithms for materials inspection and automated data collection.

🔬Awards:

Awarded various certifications, including specialization in self-driving car technologies from the University of Toronto and advanced machine learning courses from DeepLearning.AI. Actively involved in technical leadership roles, such as Technical Coordinator for AMU’s national college fest and team leader for the American Society of Mechanical Engineers (ASME). Published work in Springer Publications on ergonomics for productivity improvement.

Conclusion:

Mr. Zubair Akhtar Mohd’s interdisciplinary skills, innovative research focus, and dedication to academia make him a deserving candidate for the Best Researcher Award. With potential to expand his publication record and increase his collaborative efforts, Mr. Mohd’s career trajectory reflects both current excellence and promise for further significant contributions to engineering and AI research.

Publications: