Dr. Yi Chen | Landslide Monitoring Awards | Best Researcher Award

Posted on by Global Sensor Awards

Dr. Yi Chen | Landslide Monitoring Awards | Best Researcher Award 

Dr. Yi Chen, changan university, China

Yi Chen is a dedicated researcher specializing in remote sensing and surveying. He earned his B.Eng. degree in Remote Sensing Science and Technology in 2018 and his M.Sc. degree in Surveying and Mapping in 2022 from Lanzhou Jiaotong University, Lanzhou, China. Currently, he is pursuing his Ph.D. at the College of Geological Engineering and Geomatics at Chang’an University in Xi’an, China. His research interests focus on InSAR (Interferometric Synthetic Aperture Radar) data processing and the study of earthquake-induced landslides, where he applies advanced methodologies to address critical geophysical challenges. With a strong academic background and a commitment to innovative research, Yi Chen aims to contribute significantly to the fields of geodesy and remote sensing.

Professional Profile:

SCOPUS

ORCID

Summary of Suitability for Best Researcher Award: Yi Chen

Research Background Yi Chen has an impressive academic trajectory, having obtained his B.Eng. degree in remote sensing science and technology and an M.Sc. degree in surveying and mapping from Lanzhou Jiaotong University, China, in 2018 and 2022, respectively. He is currently a Ph.D. candidate at the College of Geological Engineering and Geomatics, Chang’an University, Xi’an, China. His research is primarily focused on InSAR data processing and earthquake-induced landslides, crucial areas in the field of geoscience and remote sensing.

👨‍🎓 Education

Yi Chen earned his B.Eng. degree in Remote Sensing Science and Technology (2018) and his M.Sc. degree in Surveying and Mapping (2022) from Lanzhou Jiaotong University, Lanzhou, China. He is currently pursuing a Ph.D. degree at the College of Geological Engineering and Geomatics, Chang’an University, Xi’an, China.

🔍 Research Interests

Yi’s research focuses on InSAR data processing 📡 and earthquake-induced landslides 🌍. His work aims to advance understanding in these critical areas, utilizing innovative methodologies to address pressing geological challenges.

📚 Contributions

Through his academic journey, Yi Chen has been dedicated to exploring the intersection of technology and earth sciences, contributing valuable insights that have implications for disaster management and environmental monitoring.

🌟 Future Aspirations

As he continues his research, Yi aims to make significant contributions to the fields of geodesy and remote sensing, with the goal of improving safety and resilience in earthquake-prone regions.

Publication Top Notes

A ConvLSTM Neural Network Model for Spatiotemporal Prediction of Mining Area Surface Deformation Based on SBAS-InSAR Monitoring Data

Surface deformation prediction based on TS-InSAR technology and long short-term memory networks

Time-Series Analysis and Prediction of Surface Deformation in the Jinchuan Mining Area, Gansu Province, by Using InSAR and CNN–PhLSTM Network

Spatiotemporal characteristics of drought and its impact on vegetation in the vegetation region of Northwest China

Prediction of InSAR deformation time-series using a long short-term memory neural network

Dr. Meaad Almusined | Biological Indicators Awards | Best Researcher Award

Posted on by Global Sensor Awards

Dr. Meaad Almusined | Biological Indicators Awards | Best Researcher Award

Dr. Meaad Almusined, King Saud University, Saudi Arabia

Meaad Mohammed Almusined, in Riyadh, Saudi Arabia, is an accomplished academic and MRI technologist with over 12 years of experience in radiological sciences. She earned her Bachelor’s degree in Radiological Sciences from King Saud University in 2009, followed by a Master’s degree in Magnetic Resonance Technology from the University of Queensland in 2016. Currently, Meaad is completing her Ph.D. at the University of Leeds, where her research focuses on improving the diagnosis of ischaemic stroke through the investigation of circulating biomarkers in conjunction with CT and MRI imaging. She is currently an Assistant Professor at King Saud University, where she lectures and supervises students in the College of Applied Medical Sciences. Meaad is dedicated to staying current with advancements in her field, contributing to curriculum development, and enhancing the educational experience of her students. With a commitment to research and teaching, she aspires to further develop her skills within a professional team, aiming to elevate her university’s reputation in the field of radiological sciences.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award: Meaad Almusined

Professional Background: Meaad Almusined is an accomplished academic and researcher with over 12 years of experience in the field of radiological sciences, particularly in MRI technology. Currently serving as an Assistant Professor at King Saud University, she is deeply involved in both teaching and research. Her commitment to education is demonstrated by her extensive experience in supervising students and delivering lectures that align with contemporary developments in medical imaging.

Education:

  • PhD in Medicine and Health
    University of Leeds, Leeds, United Kingdom (2018 – 2023)
    Thesis: Investigation of circulating biomarkers in combination with computer tomography (CT) and magnetic resonance imaging (MRI) markers to improve diagnosis of ischaemic stroke.
  • MSc in Magnetic Resonance Technology
    The University of Queensland, Brisbane, Australia (2015 – 2016)
    Thesis: Optimisation of dynamic Gd enhanced MRI imaging in the mouse brain.
  • BSc in Radiological Sciences
    King Saud University, College of Applied Medical Sciences, Riyadh, Saudi Arabia (2004 – 2009)
    Degree: Very Good, GPA 4.40 out of 5.
  • High School
    Riyadh, Saudi Arabia (2004)

Employment History:

  • Assistant Professor
    King Saud University, College of Applied Medical Sciences, Radiological Sciences, Riyadh, Saudi Arabia (2023 – Present)
  • Demonstrator
    King Saud University, College of Applied Medical Sciences, Radiological Sciences, Riyadh, Saudi Arabia (2012 – 2023)
  • MRI Technologist
    King Fahad Medical City, Radiology Department, Riyadh, Saudi Arabia (2010 – 2012)
  • Radiologic Intern
    King Fahad Medical City, Radiology Department, Riyadh, Saudi Arabia (2009 – 2010)

Clinical Experience:

  • Research and Laboratory Supervisor on Undergraduate Students
    University of Leeds, Leeds, United Kingdom (April 2018 – November 2022)
  • Research and Laboratory Trainer for NHS Stroke Nurses
    The Leeds Teaching Hospital, Leeds, United Kingdom (June 2021 – November 2022)
  • Magnetic Resonance Imaging (MRI) Intern
    King Fahad Medical City (September 2009 – February 2010)
  • Computed Tomography (CT) Intern
    King Fahad Medical City (August 2009)
  • Nuclear Medicine Intern
    King Fahad Medical City (July 2009)
  • Ultrasound Intern
    King Faisal Specialist Hospital & Research Centre (June 2009)
  • Angiography Intern
    King Fahad Medical City (April 2009)
  • Fluoroscopy Intern
    King Fahad Medical City (April 2009)
  • General X-ray Intern
    King Fahad Medical City (March 2009)
  • Summer Trainee, Medical Imaging Department
    King Fahad Medical City (July – August 2007)
  • Training in Different Radiology Modalities
    King Khalid University Hospital (KKUH), King Abdulaziz University Hospital (KAUH), King Fahad Medical City (KFMC), and King Saud Medical City (KSMC) during academic years as part of the program requirements (2006 – 2009).

Publication top Notes: