Nabi Mehri Khansari | Machine Learning | Best Researcher Award

Dr. Nabi Mehri Khansari | Machine Learning | Best Researcher Award

University Professor, Sahand University of Technology, Iran

Dr. Nabi Mehri-Khansari is an esteemed Assistant Professor at the Sahand University of Technology. With a rich academic background in Mechanical and Aerospace engineering from prestigious institutions like Iran University of Science and Technology and the University of Tehran, he has made significant contributions to the field. His research spans failure analysis, damage and fracture mechanics in lightweight composite structures, leveraging machine learning and deep learning. Dr. Mehri-Khansari has collaborated with various international research centers and industries, enhancing his expertise and impact in the field.

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Education

🎓 Dr. Nabi Mehri-Khansari obtained his B.Sc. degree in Mechanical Engineering from the Iran University of Science and Technology in 2011. He pursued his M.Sc. and Ph.D. degrees in Aerospace Engineering from the University of Tehran, completing them in 2014 and 2018, respectively. His academic excellence is marked by being ranked 2nd in M.Sc. and 1st in Ph.D., earning acceptance with quotas for talented students. He also served as a research fellow at NTNU University, Trondheim, Norway, further broadening his academic horizons.

Experience

🔧 Dr. Mehri-Khansari has an extensive professional background. He has been a faculty member at the Sahand University of Technology since January 2019. Prior to this, he was a lecturer at the University of Tehran – North Branch, a research assistant at NTNU University in Norway, and a technical expert at the Iranian Space Institute. His diverse roles reflect his versatile expertise and commitment to advancing engineering education and research.

Research Interests

🔬 Dr. Mehri-Khansari’s research interests are vast and interdisciplinary. They include wind turbine technology, multi-scale fracture mechanics of composites and inhomogeneous media, multi-scale damage mechanics, aeroelasticity, and defect detection methods. His innovative work often incorporates machine learning and deep learning techniques, pushing the boundaries of traditional engineering research.

Awards

🏅 Dr. Mehri-Khansari has received numerous accolades throughout his career. These include the prestigious Ph.D. acceptance with quotas for talented students, being ranked 1st in his Ph.D. program at the University of Tehran, and the Best Teacher Award from the Sahand University of Technology in June 2024. His membership in professional organizations such as the American Society of Mechanical Engineering and the Iranian Composites Scientific Association further underscores his professional excellence.

Publications

Orthotropic failure criteria based on machine learning and micro-mechanical matrix adapting coefficient
Mixed-modes (I/III) fracture of aluminum foam based on micromechanics of damage
Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models
Numerical & experimental assessment of mixed-modes (I/II) fracture of PMMA/hydroxyapatite nanocomposite

Narmilan Amarasingam | Artificial Intelligence | Best Researcher Award

Mr. Narmilan Amarasingam | Artificial Intelligence | Best Researcher Award

PhD student, Queensland University of Technology, Australia

Researcher specializing in drone-based remote sensing solutions for environmental and surveillance needs, focusing on precision agriculture and biosecurity. Expertise includes UAV remote sensing, artificial intelligence, and multispectral/hyperspectral image processing. Currently pursuing a PhD in Precision Agriculture at Queensland University of Technology.

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🎓 Education

PhD: Precision Agriculture, Queensland University of Technology (2021 – Present). MSc: Agricultural Engineering, Eastern University, Sri Lanka (2016 – 2018). BSc: Agriculture, Agricultural Engineering, Eastern University, Sri Lanka (2010 – 2015). BIT: Software Engineering, University of Colombo School of Computing, Sri Lanka (2011 – 2015)

🔍 Experience

Research Assistant at Charles Sturt University and Sunshine Coast Council on projects integrating AI and drone technology for environmental monitoring and invasive species detection.

🏆 Awards

QUT/Accelerate Higher Education Development Expansion and Development (AHEAD) World Bank Project Scholarship. Vice Chancellor’s Award for Early Career Researcher, Faculty of Technology, 2022.

🌍 Research Interests

Precision Agriculture, UAV-based Remote Sensing, Multispectral Image Processing, AI, Biosystems Engineering, Environmental Management.

📚 Publications

Co-authored numerous peer-reviewed articles in Q1 and non-Q1 ranking journals on topics related to UAV-based remote sensing and AI applications in agriculture and environmental management.

A review of UAV platforms, sensors, and applications for monitoring of sugarcane crops
Predicting canopy chlorophyll content in sugarcane crops using machine learning algorithms and spectral vegetation indices derived from UAV multispectral imageryDetection of white leaf disease in sugarcane using machine learning techniques over UAV multispectral images
Detection of White Leaf Disease in Sugarcane Crops Using UAV-Derived RGB Imagery with Existing Deep Learning Models
N Amarasingam, F Gonzalez, ASA Salgadoe, J Sandino, K Powell
E-agricultural concepts for improving productivity: A review