Sina Rezaei | Data Analysis | Best Researcher Award

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Mr. Sina Rezaei | Data Analysis | Best Researcher Award

University of Tehran | Iran

Publication Profile

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Biography of Mr. Sina Rezaei 📚

📚 Passionate Photogrammetry & 3D Reconstruction Specialist

Sina Rezaei is an innovative researcher currently pursuing his M.Sc. in Photogrammetry at the University of Tehran. With a strong foundation in Surveying Engineering, Sina’s academic journey focuses on the application of photogrammetry, 3D modeling, and remote sensing in the preservation of cultural heritage and geospatial analysis. His work is aimed at enhancing 3D reconstruction using spherical cameras and photogrammetry techniques, combining both scientific research and technology.

🎓 Education

Sina holds a B.Sc. in Surveying Engineering from the University of Tehran, where he achieved an impressive GPA of 3.72/4. He ranked among the top students in his class, showcasing his academic excellence. He is currently pursuing an M.Sc. in Photogrammetry at the same institution, with a focus on 3D Reconstruction of cultural heritage structures. His thesis examines the use of spherical cameras and photogrammetry techniques to improve accuracy in cultural preservation.

🔬 Research Interests

Sina’s research interests lie at the intersection of photogrammetry, computer vision, and 3D modeling. He is particularly interested in remote sensing, machine learning, and data analysis. His goal is to push the boundaries of image processing and optimization techniques to improve the accuracy and efficiency of 3D reconstruction methods, especially in the context of cultural heritage conservation and geospatial engineering.

🔧 Research & Work Experience

Sina has worked extensively in the field of photogrammetry and remote sensing. He is currently augmenting GNSS devices with visual positioning systems using photogrammetry solutions and implementing Structure from Motion (SFM) and Bundle Adjustment (BA) algorithms. In his Research Intern role at i3mainz, he focused on 3D reconstruction using spherical cameras, DSLR cameras, and laser scanners. Additionally, he contributed to the super-resolution of Sentinel-2 satellite data for urban sprawl analysis.

👨‍🏫 Teaching Experience

Sina has also gained valuable teaching experience as a Teaching Assistant for several courses at the University of Tehran, including Photogrammetric Data Acquisition, Data Collection from Images, and Technical English. His teaching role reflects his dedication to sharing his knowledge and passion for photogrammetry with the next generation of students.

🖥️ Selected Course Projects

During his academic career, Sina has worked on a variety of technical projects, including Photogrammetry Calculations, where he optimized stereo and aerial images using Python. He has also implemented optimization algorithms and image matching techniques using fuzzy logic in Soft Computing and worked on satellite image processing and radiometric corrections in his Remote Sensing Laboratory.

🏅 Honors & Awards

Sina has earned multiple honors during his academic journey, including being ranked 5th among 40 students in the College of Surveying and Geospatial Engineering at the University of Tehran. He is a proud member of the Iran’s National Elites Foundation, which granted him direct admission into the M.S. degree program. He has also earned certificates from Stanford University and DeepLearning.AI for completing courses in Supervised Machine Learning and Advanced Learning Algorithms.

📚 Top Notes Publications

Evaluation of Network Design and Solutions of Fisheye Camera Calibration for 3D Reconstruction

    • Authors: Sina Rezaei, Hossein Arefi

    • Journal: Sensors

    • Year: 2025

Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping

    • Authors: Sina Rezaei, Angelina Maier, Hossein Arefi

    • Journal: Sensors

    • Year: 2024

Yue Xin | inventory management | Women Researcher Award

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Dr. Yue Xin | inventory management | Women Researcher Award

Ocean university of chain | China

PUBLICATION PROFILE

Google Scholar

📝 Summary

Dr. Yue Xin is a dedicated researcher currently pursuing a Ph.D. in Probability Theory and Mathematical Statistics at China People’s University. His academic work focuses on time series analysis, data-driven decision-making, and inventory management strategies. With numerous published papers in top-tier SCI-indexed journals, he has gained recognition for his innovative contributions to mathematical modeling, particularly in financial markets and environmental science.

🎓 Education

Dr. Yue completed his Bachelor’s in Information and Computational Science from Shandong University, earning a GPA of 87.9/100. He is currently pursuing his Ph.D. at China People’s University with an impressive GPA of 3.84/4, specializing in Probability Theory and Mathematical Statistics. Additionally, he spent a year as a joint Ph.D. student at Nanyang Technological University in Singapore, enhancing his global academic exposure.

💼 Professional Experience

Dr. Yue has participated in several high-profile research projects, contributing to areas such as missile defense strategies, game theory, and underwater confrontation analysis. He played a key role in modeling and solving critical problems using differential game theory. His collaboration with top researchers and institutions has honed his expertise in both theoretical and applied mathematics.

📚 Academic Citations & Publications

Dr. Yue has published several influential papers in prominent journals like the Journal of Computational and Applied Mathematics and Applied Mathematics and Computation. His work includes advanced topics like maximum likelihood estimation, uncertain autoregressive models, and the valuation of options in uncertain fractional-order environments. His papers are highly cited in the academic community, reflecting the impact of his research.

🔧 Technical Skills

Dr. Yue is proficient in multiple programming languages and software tools, including Python, Matlab, R, and C++. He is skilled in using LaTeX for scientific writing and is also well-versed in Microsoft Office. His expertise extends to statistical analysis, machine learning, and developing data-driven models for complex decision-making in finance and risk management.

👨‍🏫 Teaching Experience

With a passion for teaching, Dr. Yue has served as a teaching assistant for various courses, such as Mathematical Modeling, Game Theory, Probability Theory, and Higher Algebra. He has also assisted in courses on Big Data Analysis and Supply Chain Management, where he shares his extensive knowledge and fosters a dynamic learning environment.

🔬 Research Interests

Dr. Yue’s research interests lie in the fields of time series analysis, differential equations, and data-driven decision-making. He focuses on the development of innovative models for inventory management and risk assessment, and his work is particularly relevant to financial market analysis and environmental forecasting. He is dedicated to applying mathematical techniques to real-world problems.

📚 Top Notes Publications 

Maximum likelihood estimation for uncertain autoregressive moving average model with application in financial market

    • Authors: Y Xin, J Gao, X Yang, J Yang

    • Journal: Journal of Computational and Applied Mathematics

    • Year: 2023

Orthogonal series method for uncertain nonparametric regression with application to carbon dioxide emissions

    • Authors: Y Xin, J Gao

    • Journal: Communications in Statistics-Simulation and Computation

    • Year: 2024

Power-barrier option pricing formulas in uncertain financial market with floating interest rate

    • Authors: H Zhao, Y Xin, J Gao, Y Gao

    • Journal: AIMS Math

    • Year: 2023

Time series prediction method for multi-source observation data

    • Authors: X Gao, Y Xin, J Yang

    • Journal: International Journal of General Systems

    • Year: 2024

Regional express delivery network planning: A location-routing model and two-tier adaptive GA

    • Authors: W Li, Y Xin, G Yang

    • Journal: Information Sciences

    • Year: 2025

Estimation of parameters and valuation of options written on multiple assets described by uncertain fractional differential equations

    • Authors: Y Xin, Y Zhang, I Noorani, F Mehrdoust, J Gao

    • Journal: Applied Mathematics and Computation

    • Year: 2025

Snehal Laddha | Medical Image Analysis | Best Researcher Award

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Prof. Snehal Laddha | Medical Image Analysis | Best Researcher Award

University of Bradford | United Kingdom

Publication Profile

Google Scholar

INTRODUCTION 🌟

Ms. Snehal Laddha is a highly accomplished and dynamic educator and professional, with an extensive background in Electronics and Computer Science, specializing in AI, Data Analysis, and Deep Learning. With over 16 years of experience in the academic field, she has significantly contributed to student engagement, research innovation, and global academic exposure through teaching roles in India, Australia, and the UK.

EARLY ACADEMIC PURSUITS 📚

Snehal’s academic journey began with a keen interest in electronics and computer science. She completed her formal education, setting the foundation for her research in emerging fields such as Artificial Intelligence (AI) and data analysis. Her academic pursuit took her across various top institutions, where she honed her skills in programming, image analysis, and deep learning, fueling her desire to make a significant impact on both the academic and technological landscapes.

PROFESSIONAL ENDEAVORS 👩‍🏫

Snehal’s career includes prestigious teaching roles such as Assistant Professor at Ramdeobaba University, H.V.P.M.C.O.E.T., and S.R.K.N.E.C. in India, as well as a Teaching Tutor position at the University of Technology, Sydney, Australia. She has also demonstrated remarkable versatility, contributing in non-teaching roles such as Customer Service at Sydney CBD METCENTER, where she gained invaluable international exposure and insights into multicultural education systems.

CONTRIBUTIONS AND RESEARCH FOCUS  ON MEDICAL IMAGE ANALYSIS💡

A passionate researcher, Snehal’s work primarily revolves around AI, computer vision, and Internet of Things (IoT) applications. Her efforts in data analysis, deep learning, and image analysis have led to several publications in both national and international journals. Her most notable invention is the IoT-powered smart trash bin (patent granted in 2024). She is deeply committed to improving academic practices by leading initiatives to organize Faculty Development Programs (FDPs) and research projects sponsored by AICTE.

IMPACT AND INFLUENCE 🌍

Snehal’s global exposure and significant contributions in both teaching and research have shaped the careers of many students and academics. She consistently fosters innovation, critical thinking, and leadership in her students by incorporating advanced technologies into the curriculum. Through her work, she has become a vital figure in the academic community, nurturing the next generation of scientists and engineers.

ACADEMIC CITATIONS AND PUBLICATIONS 📑

Snehal has published numerous papers in international and national journals, covering a wide range of topics, from embedded systems to AI applications. Her citations reflect her deep expertise and recognition in the academic community. She remains dedicated to advancing her field and continues to contribute valuable insights through her research endeavors.

HONORS & AWARDS 🏆

Ms. Snehal Laddha has been recognized for her exceptional academic and professional contributions with multiple prestigious honors:

  • Australian Qualified Professional Engineer certification by Engineers Australia
  • Best Paper Award at Cardiff Metropolitan University, UK
  • Grants for organizing Faculty Development Programs and SPICES from AICTE
  • IELTS Score of 6.5/10 from the British Council

These awards reflect her dedication to excellence in both teaching and research, cementing her as a leader in her field.

LEGACY AND FUTURE CONTRIBUTIONS 🌱

Looking forward, Snehal plans to continue her research and academic endeavors, focusing on the evolution of AI and its practical applications in various industries. She is determined to contribute to the development of innovative solutions that can address societal challenges, particularly through IoT, deep learning, and image processing.

FINAL NOTE ✨

Ms. Snehal Laddha’s career is a testament to her unwavering commitment to education, research excellence, and global academic collaboration. With a rich background spanning teaching, research, and innovative solutions, she continues to inspire and lead in the realm of AI and data science, ensuring her legacy as an influential figure in the academic and technological spheres.

TOP NOTES PUBLICATIONS 📚

Liver Segmentation from MR T1 In-Phase and Out-Phase Fused Images Using U-Net and Its Modified Variants
    • Authors: SVL Siddhi Chourasia, Rhugved Bhojane
    • Journal: Intelligent Systems. ICMIB 2024. Lecture Notes in Networks and Systems
    • Year: 2025
Deep-Dixon: Deep-Learning frameworks for fusion of MR T1 images for fat and water extraction
    • Authors: SV Laddha, RS Ochawar, K Gandhi, YD Zhang
    • Journal: Multimedia Tools and Applications
    • Year: 2024
Addressing Class Imbalance in Diabetic Retinopathy Segmentation: A Weighted Ensemble Approach with U-Net, U-Net++, and DuckNet
    • Authors: E Gawate, S Laddha
    • Journal: International Conference on Intelligent Systems for Cybersecurity (ISCS)
    • Year: 2024
A Novel Method of Enhancing Skin Lesion Diagnosis Using Attention Mechanisms and Weakly-Supervised Learning
    • Author: SV Laddha
    • Journal: World Conference on Artificial Intelligence: Advances and Applications
    • Year: 2024
Automated Liver Segmentation in MR T1 In-Phase Images Transfer Learning Technique
    • Authors: SV Laddha, AH Harkare
    • Journal: Proceedings of Eighth International Conference on Information System Design
    • Year: 2024
Automated Segmentation of Liver from Dixon MRI Water-Only Images Using Unet, ResUnet, and Attention-Unet Models
    • Authors: E Gawate, SV Laddha, RS Ochawar
    • Journal: Information System Design: AI and ML Applications: Proceedings of Eighth International Conference on Information System Design
    • Year: 2024