Liming Ge | Environmental Science | Best Researcher Award

Liming Ge | Environmental Science | Best Researcher Award

Assist. Prof. Dr Liming Ge, Shanghai Jiaotong University, China

Liming Ge is a dedicated PhD candidate at Shanghai University of Finance and Economics, following a Masterโ€™s from the University of Shanghai for Science and Technology, where he ranked first in his major with a GPA of 3.8/4.0. His research focuses on low carbon development, highlighted in papers published in prominent journals. Liming has earned several academic honors, including national scholarships and multiple prizes in competitions related to energy economics and mathematical modeling. He actively participated in a significant National Social Science Foundation project, utilizing advanced data analysis techniques. ๐ŸŒฑ๐Ÿ“Š๐ŸŽ“

Publication profile

Scopus

Education

Pursuing excellence in academia, the individual completed a Masterโ€™s degree at the University of Shanghai for Science and Technology from September 2017 to July 2020, achieving a remarkable GPA of 3.8/4.0 and ranking first in their major. They received prestigious accolades, including the National Scholarship and the First-level Scholarship of the School. Currently a PhD candidate at Shanghai University of Finance and Economics (2020-2024), they maintain a GPA of 3.45/4.0 and were honored as an Outstanding Graduate of Shanghai and an Outstanding Student Leader at their previous university. ๐ŸŽ“๐ŸŒŸ๐Ÿ†

Honors

In 2021, He was honored with the Special Prize at the Seventh National College Students Competition on Energy Economics and secured the Third Prize at the Seventh National Academic Conference for Postgraduates in Economics. In 2019, He achieved the Third Prize in both the Fifth National College Students Competition on Energy Economics and the Ninth MathorCup College Mathematical Modeling Competition. My accolades also include the Second Prize at the Sixteenth “Challenge Cup” College Students Extracurricular Academic Technology Works Competition and the “SAIC Education Cup” in 2018. He received several Third Prizes in various mathematical modeling competitions, showcasing my dedication and talent in these fields! ๐Ÿ†๐Ÿ“Šโœจ

Academic Conferences

In 2021, several notable academic events showcased emerging research in economics across China. The Third China Regional Economist Scholars Forum and the Third China Development Economics Preface Academic Symposium were held in Shanghai and Beijing, respectively, fostering discussions on regional and developmental economic issues. Additionally, the Third Jintai Young Scholars Forum and the Third China Energy Environment and Climate Change Economists Forum facilitated dialogues on industrial economics and environmental challenges. Other significant gatherings included the Seventh Annual Academic Conference of Graduate Students in Economics and the Green and Low Carbon Development Seminar. The Camphor Economics “Cloud Seminar” also contributed to this vibrant academic landscape. ๐Ÿ“Š๐ŸŒโœจ

Projects

In 2021, He participated in a Major Project of the National Social Science Foundation of China focused on promoting green development in energy supply and consumption. He contributed to the chapter on industrial cohesion effects and governance, utilizing Matlab, Stata, and ArcGIS for data analysis and empirical investigations. Additionally, He worked on the Humanities and Social Sciences Climbing Foundation project at the University of Shanghai, analyzing total factor energy productivity across Chinese cities. In 2018, Her helped compile a monograph on the sustainable development of resource-based cities, and in 2017, He wrote two papers for a project on smog control and economic transformation. ๐ŸŒฑ๐Ÿ“Šโœ๏ธ

Research focus

Based on the provided articles, Ge, L.’s research focuses on the intersection of environmental sustainability and economic development, particularly in urban contexts. Key themes include green technology innovation, the role of government governance in fostering sustainable cities, and the impact of foreign direct investment on environmental outcomes. Ge’s work examines how policy frameworks and corporate practices affect green initiatives, exploring aspects like tax incentives, corporate governance, and low-carbon development strategies. This multidisciplinary approach highlights the importance of integrating economic policies with environmental goals to promote sustainable urban transformations. ๐ŸŒ๐Ÿ’ก๐Ÿ“ˆ

Publication top notes

The role of digital infrastructure construction on green city transformation: Does government governance matters?

The Impact of Foreign Direct Investment on Green Technology Innovation: Evidence from the Threshold Effect of Absorptive Capacity

 

 

 

 

Masoud Mahdianpari | Environmental Science | Research Hypothesis Excellence Award

Masoud Mahdianpari | Environmental Science | Research Hypothesis Excellence Award

Dr Masoud Mahdianpari, Memorial University of Newfoundland/C-CORE, Canada

Based on the provided information, Dr. Masoud Mahdianpari is indeed a strong candidate for the Research for Research Hypothesis Excellence Award. His extensive educational background, professional experience, and contributions to the field of remote sensing and data science highlight his qualifications.

Publication profile

google scholar

Educational Background

Dr. Masoud Mahdianpari holds a Ph.D. in Electrical Engineering from Memorial University of Newfoundland (2015-2019), along with a Masterโ€™s in Remote Sensing Engineering and a Bachelorโ€™s in Geomatics Engineering, both from the University of Tehran (2010-2013, 2006-2010). His robust academic foundation has equipped him with advanced knowledge in remote sensing and data analysis.

Professional Experience

Currently serving as a Cross-appointed Professor at Memorial University of Newfoundland and Remote Sensing Technical Lead at C-CORE, Ottawa, Dr. Mahdianpari has significant experience in applying machine learning and remote sensing technologies. His previous roles include Remote Sensing Scientist and Research Assistant at C-CORE, where he has developed expertise in image processing, feature extraction, and target detection.

Research Expertise

Dr. Mahdianpari specializes in machine learning, big data technologies, and radar remote sensing. His work encompasses high-resolution image processing, environmental monitoring, and GHG emission estimation. He is leading several projects focused on wetland mapping and methane emission estimation in the Arctic, leveraging advanced remote sensing data and cloud computing platforms.

Professional Appointments

As an associate editor for various journals, including IEEE Geoscience and Remote Sensing Letters and Frontiers in Environmental Science, Dr. Mahdianpari contributes to the academic community and promotes high-quality research. He is a member of several professional societies, such as IEEE and ASPRS, demonstrating his active engagement in the field.

Recent Honors and Awards

Dr. Mahdianpari has been recognized for his contributions to science, including being ranked in the top 1% of scientists worldwide (2023-2024) and receiving multiple awards for his research excellence. Notably, he has secured grants such as the NSERC Discovery Grant (2022-2027) and the Microsoft AI for Earth grant, highlighting his innovative work in environmental monitoring.

Project Leadership

Dr. Mahdianpari is currently leading the ESA Carbon Science Cluster project, aiming to enhance methane emission estimates from wetlands in the Arctic. This project underscores his leadership in addressing critical environmental challenges and advancing remote sensing methodologies.

Research Interests

His research focuses on environmental monitoring and wetland mapping using remote sensing data, emphasizing machine learning and multi-sensor image classification. Currently, he leads projects related to greenhouse gas (GHG) monitoring, showcasing his commitment to addressing pressing environmental issues.

Project Experience

He currently leads a project for the European Space Agency focused on improving methane emission estimates from wetlands, an initiative of significant environmental importance. This role emphasizes his leadership in research that impacts global environmental policies.

Publications and Presentations

Dr. Mahdianpari has authored numerous influential publications, including studies on remote sensing image classification and advanced machine learning applications in environmental monitoring. His research has contributed significantly to the field, evidenced by his citations and presentations at major international conferences.

Conference Contributions

He has presented at several prestigious conferences, showcasing his research on water quality monitoring and electrical potential preservation. His publications in leading journals further establish his reputation as a thought leader in remote sensing and environmental science.

Conclusion

In summary, Dr. Masoud Mahdianpari’s outstanding qualifications, research contributions, and recognition in the field make him a highly suitable candidate for the Research for Research Hypothesis Excellence Award. His dedication to advancing remote sensing technology and addressing pressing environmental issues through innovative research exemplifies excellence in academic and applied research.

Publication top notes

Google Earth Engine for geo-big data applications: A meta-analysis and systematic review

Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review

Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery

Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery

The first wetland inventory map of newfoundland at a spatial resolution of 10 m using sentinel-1 and sentinel-2 data on the google earth engine cloud computing platform

Deep convolutional neural network for complex wetland classification using optical remote sensing imagery

A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem

Comparing deep learning and shallow learning for large-scale wetland classification in Alberta, Canada

A systematic review of landsat data for change detection applications: 50 years of monitoring the earth

Bagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: a comparative evaluation