Lingling Li | Remote sensing | Best Researcher Award

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Dr. Lingling Li | Remote sensing | Best Researcher Award 

Associate professor, Xidian University, China

🎓 Dr. Lingling Li is an Associate Professor at the School of Artificial Intelligence, Xidian University, China. She specializes in deep learning, sparse representation, quantum evolutionary optimization learning theory, and complex image interpretation. She has founded her own research group focusing on the interpretation and understanding of remote sensing images and has supervised numerous master’s and Ph.D. students. Dr. Li has secured prestigious national-level grants, exceeding 1,000,000 RMB, to support her innovative research projects. 🌟

Publication Profile

ORCID

Strengths for the Award:

  1. Significant Research Contributions: Lingling Li has a strong record of impactful research in the fields of deep learning, image processing, and remote sensing. Her publications in prestigious journals, such as IEEE TIP and Neurocomputing, reflect her deep expertise in advanced topics like deep contourlet networks, human-object interaction detection, and quantum evolutionary learning.
  2. Leadership in Research: As the founder of her own research group on interpretation and understanding of remote sensing images at Xidian University, she has successfully supervised numerous students (16 masters, 6 Ph.D.). This shows her ability to mentor the next generation of researchers, which is a key indicator of her leadership in academia.
  3. Awarded Prestigious Grants: She has received multiple prestigious national and institutional research grants totaling over 1,000,000 RMB, which demonstrates her ability to attract funding and lead high-impact research projects, such as the National Natural Science Foundation and National Key Laboratory of Science and Technology for National Defense.
  4. Global Academic Exposure: Her experience as a visiting scholar at the University of the Basque Country and her role as a reviewer for top-tier conferences and journals underline her recognition and influence in the global academic community.

Areas for Improvement:

  1. Broader International Collaboration: While Lingling Li has an impressive research record, increasing her international research collaborations beyond China and Spain could further elevate her impact. This could enhance her visibility and influence in broader global networks.
  2. Diversification of Research Topics: Her research is heavily concentrated on deep learning and image processing. Expanding into adjacent areas, such as AI ethics, sustainable AI, or interdisciplinary applications of AI, could further diversify her research portfolio.

Education:

🎓 Dr. Li earned her Ph.D. in Intelligent Information Processing from Xidian University, China (2017). She also holds a Bachelor’s degree in Electronic Information Engineering from the same university (2011). From 2013 to 2014, she was a visiting scholar at the University of the Basque Country in Spain, enhancing her global research perspective. 🌍

Experience:

👩‍🏫 Since 2020, Dr. Li has served as an Associate Professor at the School of Artificial Intelligence, Xidian University. Prior to this, she was a Lecturer at the same institution. She has supervised 16 master’s students and co-supervised 6 Ph.D. students, establishing herself as a leader in AI and remote sensing image interpretation. 💼

Research Focus:

🔍 Dr. Li’s research revolves around deep learning, quantum evolutionary optimization, and multi-scale geometric analysis. She works on complex image interpretation and target recognition, contributing to advancements in AI-powered remote sensing. Her research addresses pressing issues in multi-objective learning and large-scale remote sensing image retrieval. 🚀

Awards and Honours:

🏆 Dr. Li has received multiple national-level funding grants, including projects funded by the National Natural Science Foundation of China and Xidian University. Her research accomplishments are well-recognized in the academic community. 💡

Publications Top Notes:

📚 Dr. Li has contributed to top-tier journals and conferences, collaborating with renowned researchers. Some of her most notable works include:

“Region NMS-based deep network for Gigapixel Level Pedestrian Detection with Two-Step Cropping” – Neurocomputing, 2021 Cited by: 45

“Deep multi-level fusion network for multi-source image pixel-wise classification” – Knowl. Based Syst., 2021 Cited by: 50

IPGN: Interactiveness Proposal Graph Network for Human-Object Interaction Detection” – IEEE Trans. Image Process., 2021 Cited by: 78

“C-CNN: Contourlet Convolutional Neural Networks” – IEEE Trans. Neural Networks Learn. Syst., 2021 Cited by: 120

“Multi-Scale Progressive Attention Network for Video Question Answering” – ACL/IJCNLP, 2021 Cited by: 34

Conclusion:

Lingling Li is a highly deserving candidate for the Best Researcher Award. Her significant contributions to AI and deep learning, coupled with her leadership in research and mentorship, place her in an excellent position. With further expansion of her international collaborations and diversification of research, she could become a more influential figure on the global stage.

Emanuele Calabrò | Environmental Science | Best Researcher Award

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Dr. Emanuele Calabrò | Environmental Science | Best Researcher Award

Docente, MIUR (Ministero dell’Istruzione e del merito), Italy

Prof. Emanuele Calabrò, a distinguished physicist, currently serves as a Full Professor of Physics at the Technological Technical Institute of Messina “Verona-Trento” in Italy. With a career spanning over several decades, Prof. Calabrò has made significant contributions to various fields, including applied physics, experimental physics of matter, and biophysics. His expertise extends to areas such as electromagnetic fields pollution, biotechnology, solar energy, astronomy, and topography.

 

Profile

Google Scholar

Education 🎓

Prof. Emanuele Calabrò obtained his Bachelor’s degrees in Physics and Engineering from the University of Messina in Italy, with exceptional final evaluations. He further pursued post-graduate training courses and a Master’s degree in the General Theory of Physical Sciences from the University “G. Marconi” in Rome, Italy.

Experience 💼

Throughout his career, Prof. Calabrò has held various academic positions, including teaching assistantships and professorships at esteemed institutions such as the University of Messina and the Technical Technological Institute in Siena, Italy. His teaching and research endeavors have spanned topics ranging from electrotechnics to biomedical physics and electronic measurements.

Research Interests 🔬

Prof. Calabrò’s research interests encompass a diverse range of areas, including man-made electromagnetic fields pollution, biophysics, the use of resonant frequencies for cancer treatment, solar energy optimization, astronomy, and topography. His pioneering work explores the effects of electromagnetic fields on organic systems, the alignment of proteins under electromagnetic fields, and the application of photogrammetry in evaluating structural decay.

Awards 🏆

Prof. Emanuele Calabrò has been recognized for his exemplary contributions to academia and research. He was the winner of the “Award for Excellence in Research” in the Academic Brand Awards-18 and has received certificates of recognition for his services as a reviewer for Bentham Science Publishers. His dedication to advancing scientific knowledge and fostering innovation is commendable.

Publications 📚

Calabrò, E., & Mammano, A. (1992). Forbidden high excitation lines and TiO bands in the symbiotic system QW Sge. Astrophysics and Space Science, 197, 251-256.

Calabrò, E. (2009). Determining optimum tilt angles of photovoltaic panels at typical north tropical latitudes. Journal of Renewable and Sustainable Energy, 1, 033104.

Calabrò, E., & Magazù, S. (2010). Monitoring Electromagnetic Field Emitted by High Frequencies Home Utilities. Journal of Electromagnetic Analysis & Applications, 2(9), 571-579.

Calabrò, E., & Magazù, S. (2018). Resonant interaction between electromagnetic fields and proteins: A possible starting point for the treatment of cancer. Electromagnetic Biology and Medicine, 37(3), 155-168.