Luning Li | Quantum Computing | Best Researcher Award

Assoc. Prof. Dr. Luning Li | Quantum Computing | Best Researcher Award

Associate Professor, Master’s Supervisor at Shanghai Institute of Technical Physics, Chinese Academy of Sciences, China

Dr. Luning Li 🎓, an Associate Professor and Master’s Supervisor at the Shanghai Institute of Technical Physics, Chinese Academy of Sciences, is a distinguished researcher in laser spectroscopy, laser plasma physics, and AI-driven chemometrics 🔬🤖. With dual degrees from SJTU and a PhD in Physics, his expertise bridges theoretical innovation and applied science. Dr. Li leads cutting-edge research in Laser-Induced Breakdown Spectroscopy (LIBS) for Mars exploration 🚀🔍 and has pioneered deep learning frameworks for spectral data analysis. He has published 17 SCI papers, secured 8 national patents (3 granted) 📄🔧, and led 9 major research projects including national talent programs 🏅. As a core member of China’s Mars mission team and a reviewer for NSFC, Dr. Li exemplifies excellence in advancing science and space research 🌌🇨🇳.

Professional Profile

Education 🎓

Dr. Luning Li holds a strong educational foundation in physics and engineering. He earned a dual bachelor’s degree in Measurement and Control Technology and Instrumentation and in Computer Science from Shanghai Jiao Tong University 🏛️. He went on to complete his Ph.D. in Physics at the Shanghai Institute of Technical Physics, Chinese Academy of Sciences, where he developed deep expertise in laser spectroscopy and photonics 🔬. His multidisciplinary training integrates precision instrumentation, computational methods, and physical sciences, enabling him to approach complex scientific challenges with a holistic and data-driven perspective 📚💡. Dr. Li’s academic path reflects both intellectual rigor and a commitment to cutting-edge research at the intersection of physics and technology, laying the groundwork for his impactful contributions to science and space exploration 🚀.

Professional Experience 🔬🤖

Dr. Luning Li is currently serving as an Associate Professor and Master’s Supervisor at the Shanghai Institute of Technical Physics, Chinese Academy of Sciences 🏢. His professional career is distinguished by leadership in national and international research initiatives, particularly in laser-induced breakdown spectroscopy (LIBS) for Mars exploration missions 🔭🪐. He has overseen numerous research projects, including nine competitive grants at the national level, and plays a core role in China’s Mars mission team 🚀. Dr. Li is also an active reviewer for the National Natural Science Foundation of China (NSFC), where his insights contribute to national research funding decisions. His cross-disciplinary collaborations and mentorship of graduate students demonstrate his dedication to advancing scientific discovery, fostering innovation, and cultivating the next generation of physicists and engineers 👨‍🏫🔧.

Research Interest 🔬

Dr. Luning Li’s research interests lie at the dynamic intersection of laser spectroscopy, plasma physics, and artificial intelligence 📡⚡🤖. He is particularly known for his work in Laser-Induced Breakdown Spectroscopy (LIBS), which he applies to planetary exploration, including Mars surface analysis 🚀🧪. His team has pioneered advanced deep learning techniques and AI-driven chemometric models to enhance spectral data interpretation and real-time mineral identification 📊🔍. Dr. Li also explores the physics of laser plasmas, contributing to a deeper understanding of material interactions at high energies. His integrative approach combines fundamental research with practical applications in space science, environmental monitoring, and industrial analysis 🌍💼. Through cutting-edge innovation and scientific precision, he continues to push the boundaries of analytical instrumentation and intelligent sensing technologies.

Awards and Honors 🏅

Dr. Luning Li has received multiple accolades recognizing his exceptional contributions to science and technology 🏆. He has been awarded funding and recognition through prestigious national talent programs, affirming his status as a leading researcher in China’s scientific community 🇨🇳. His groundbreaking work has resulted in 17 publications in high-impact SCI journals and 8 national patents, of which 3 have been granted 📄🔧. As a core member of China’s Mars exploration mission, Dr. Li was honored for his strategic role in developing LIBS systems for in-situ planetary analysis 🚀🔬. Additionally, his service as a reviewer for the NSFC reflects his respected judgment in scientific evaluation. These honors underscore his innovative spirit, technical excellence, and influential leadership in both academic and applied research domains.

Conclusion 🌟

Dr. Luning Li stands as a prominent figure in the fields of laser spectroscopy, space science, and AI-powered analytics 📈🌌. His academic training, professional accomplishments, and impactful research demonstrate a rare combination of scientific depth and innovative vision. As an Associate Professor at the Chinese Academy of Sciences, he leads forward-thinking projects that shape the future of planetary exploration and intelligent diagnostics 🚀🤖. His contributions—spanning over 17 scientific publications, numerous patents, and leadership in national programs—highlight his influence in both academia and applied science 🧠🔧. Dr. Li’s commitment to mentorship and collaboration ensures that his work will continue to inspire future generations. With a career rooted in excellence and driven by discovery, Dr. Li is truly advancing the frontiers of physics and engineering in the modern scientific era 🌍📡.

Publications Top Notes

A review of artificial neural network based chemometrics applied in laser-induced breakdown spectroscopy analysis
Authors: LN Li, XF Liu, F Yang, WM Xu, JY Wang, R Shu
Year: 2021
Citations: 145
Source: Spectrochimica Acta Part B: Atomic Spectroscopy, Vol. 180, 106183

The MarSCoDe instrument suite on the Mars Rover of China’s Tianwen-1 mission
Authors: W Xu, X Liu, Z Yan, L Li, Z Zhang, Y Kuang, H Jiang, H Yu, F Yang, C Liu, …
Year: 2021
Citations: 115
Source: Space Science Reviews, Vol. 217, 1–58

A laser-induced breakdown spectroscopy multi-component quantitative analytical method based on a deep convolutional neural network
Authors: LN Li, XF Liu, WM Xu, JY Wang, R Shu
Year: 2020
Citations: 63
Source: Spectrochimica Acta Part B: Atomic Spectroscopy, Vol. 169, 105850

Laser-induced breakdown spectroscopy combined with a convolutional neural network: A promising methodology for geochemical sample identification in Tianwen-1 Mars mission
Authors: F Yang, LN Li, WM Xu, XF Liu, ZC Cui, LC Jia, Y Liu, JH Xu, YW Chen, …
Year: 2022
Citations: 22
Source: Spectrochimica Acta Part B: Atomic Spectroscopy, Vol. 192, 106417

Ionization and high harmonic generation of two-dimensional quasiperiodic structures in arbitrarily polarized strong laser fields
Authors: LN Li, F He
Year: 2016
Citations: 15
Source: Journal of the Optical Society of America B, Vol. 34 (1), 52–59

Convolutional neural network chemometrics for rock identification based on laser-induced breakdown spectroscopy data in Tianwen-1 pre-flight experiments
Authors: F Yang, W Xu, Z Cui, X Liu, X Xu, L Jia, Y Chen, R Shu, L Li
Year: 2022
Citations: 12
Source: Remote Sensing, Vol. 14 (21), 5343

Development and Testing of the MarSCoDe LIBS Calibration Target in China’s Tianwen-1 Mars Mission
Authors: X Liu, W Xu, H Qi, X Ren, J Liu, L Li, Z Yan, C Liu, J Chen, Z Zhang, C Li, …
Year: 2023
Citations: 9
Source: Space Science Reviews, Vol. 219 (5), 43

Bintree seeking: a novel approach to mine both bi-sparse and cohesive modules in protein interaction networks
Authors: QJ Jiao, YK Zhang, LN Li, HB Shen
Year: 2011
Citations: 8
Source: PLoS One, Vol. 6 (11), e27646

Investigation into the Affect of Chemometrics and Spectral Data Preprocessing Approaches upon Laser-Induced Breakdown Spectroscopy Quantification Accuracy Based on MarSCoDe …
Authors: Z Liu, L Li, W Xu, X Xu, Z Cui, L Jia, W Lv, Z Shen, R Shu
Year: 2023
Citations: 5
Source: Remote Sensing, Vol. 15 (13), 3311

Initial drift correction and spectral calibration of MarSCoDe laser-induced breakdown spectroscopy on the Zhurong rover
Authors: L Jia, X Liu, W Xu, X Xu, L Li, Z Cui, Z Liu, R Shu
Year: 2022
Citations: 5
Source: Remote Sensing, Vol. 14 (23), 5964

A Laser-Induced Breakdown Spectroscopy Experiment Platform for High-Degree Simulation of MarSCoDe In Situ Detection on Mars
Authors: Z Cui, L Jia, L Li, X Liu, W Xu, R Shu, X Xu
Year: 2022
Citations: 5
Source: Remote Sensing, Vol. 14 (9), 1954

Roles of Coulomb potentials in below-and above-threshold harmonic generation for a hydrogen atom in strong laser fields
Authors: LN Li, JP Wang, F He
Year: 2016
Citations: 5
Source: Journal of the Optical Society of America B, Vol. 33 (7), 1558–1563

Comparison on quantitative analysis of olivine using MarSCoDe laser-induced breakdown spectroscopy in a simulated Martian atmosphere
Authors: X Liu, W Xu, L Li, X Xu, H Qi, Z Zhang, F Yang, Z Yan, C Liu, R Yuan, …
Year: 2022
Citations: 4
Source: Remote Sensing, Vol. 14 (21), 5612

An Overview of Quantum Machine Learning Research in China
Authors: L Li, X Zhang, Z Cui, W Xu, X Xu, J Wang, R Shu
Year: 2025
Citations: 1
Source: Applied Sciences, Vol. 15 (5), 2555

Numerical Simulation of Heat Conduction in Laser Ablation Based on Optimal Weight Factor
Authors: LN Li, ZC Cui, R Shu, JY Wang, XS Xu, WM Xu
Year: 2023
Citations: 1
Source: Atomic Spectroscopy, Vol. 44 (4), 236–246

Automatic morphologic classification of Martian craters using imbalanced datasets of Tianwen-1’s MoRIC images with deep neural networks
Authors: Q Zheng, R Huang, Y Xu, F Zhang, C Xiao, L Li, X Tong
Year: 2025
Source: Planetary and Space Science, Vol. 262, 106104

Mariana Durcheva | Cryptography | Best Researcher Award

Dr. Mariana Durcheva | Cryptography | Best Researcher Award

Lecturer at Sami Shamoon College of Engineering, Israel

Mariana Durcheva is a distinguished academic and researcher specializing in mathematics, cryptography, fuzzy theory, and AI applications. She holds a Ph.D. in Mathematics from the Technical University of Sofia, Bulgaria, and is currently a lecturer at Sami Shamoon College of Engineering in Israel. With over 30 peer-reviewed publications, her research spans several critical areas, including public key cryptography, fuzzy graphs, and the application of AI in various domains. Durcheva has also made significant contributions to the development of educational tools, including her work on the TeSLA project, a Horizon 2020 initiative for e-assessment in higher education. Her leadership and research have earned her international recognition, and she continues to push the boundaries of mathematical theory with a focus on practical applications. Her interdisciplinary approach and commitment to advancing both mathematical theory and education make her a prominent figure in her field.

Professional Profile 

Scopus Profile

Education

Mariana Durcheva’s educational journey began with an M.Sc. in Mathematics and Informatics from Sofia University “St. Kliment Ohridski,” Bulgaria, in 1989. She then pursued a Ph.D. in Mathematics at the Technical University of Sofia, Bulgaria, from 2010 to 2013, specializing in mathematical modeling and cryptography. Her doctoral dissertation focused on discrete logarithms and cryptographic protocols using semigroups and semirings. Durcheva’s strong academic foundation laid the groundwork for her subsequent research career, where she has integrated mathematical theory with real-world applications, especially in the fields of cryptography, AI, and education.

Professional Experience

Mariana Durcheva has an extensive teaching career, currently serving as a lecturer at Sami Shamoon College of Engineering, Israel. She has previously held academic positions at the Technical University of Sofia and other esteemed Bulgarian institutions. Durcheva has contributed to various leadership roles, including serving as Vice Dean of the Faculty of Applied Mathematics and Informatics at TU Sofia and leading the TeSLA project, an EU-funded initiative aimed at developing innovative e-assessment systems for higher education.

Research Interests

Durcheva’s primary research interests lie in cryptography, fuzzy theory, artificial intelligence, and mathematical education. Her work focuses on applying AI and deep learning to improve cryptographic systems and exploring advanced topics in public key cryptography, fuzzy graphs, and network analysis. Additionally, she has explored the intersection of mathematics and technology in education, contributing to projects that enhance teaching methodologies using Computer Algebra Systems (CAS) and machine learning. Her research is interdisciplinary, combining mathematical theory with practical applications in diverse fields like electronic circuit design and financial systems.

Awards and Honors

Throughout her career, Durcheva has received numerous awards and grants for her research. These include funding for her contributions to cryptography and AI-based applications. Notably, she played a key role in the TeSLA project under the Horizon 2020 initiative, which aimed to revolutionize e-assessment systems in education. Her work in both cryptography and education technology has garnered attention in the international research community, and she has published widely in high-impact journals and presented at global conferences. Her commitment to research excellence and innovation has earned her a well-deserved reputation in the academic community.

Conclusion

Mariana Durcheva is an accomplished mathematician and researcher whose work blends theoretical mathematics with practical technological applications. With a deep passion for cryptography, fuzzy theory, and AI, she continues to contribute groundbreaking insights to her field. Durcheva’s academic career reflects a dedication to both advancing scientific knowledge and improving educational practices. Her leadership in international projects and her ongoing contributions to mathematics make her a highly respected figure in her field. As she continues to push the boundaries of her research, her impact on both academia and industry is poised to grow further.

Publications Top Noted

  • Cryptography Based on (Idempotent) Semirings: Abandoning Tropicality?

    • Authors: Mariana Durcheva

    • Year: February 2025

    • Source: Article

  • Secure Key Exchange in Tropical Cryptography: Leveraging Efficiency with Advanced Block Matrix Protocols

    • Authors: Mariana Durcheva, Kiril Danilchenko

    • Year: May 2024

    • Source: Article

  • Comprehensive E-learning of Mathematics using the Halomda Platform enhanced with AI tools

    • Authors: Philip Slobodsky, Mariana Durcheva

    • Year: April 2024

    • Source: Article

  • Secure Key Exchange in Tropical Cryptography: Leveraging Efficiency with Advanced Block Matrix Protocols

    • Authors: Mariana Durcheva, Kiril Danilchenko

    • Year: April 2024

    • Source: Preprint

  • Enhancing Students’ Preparation for Math Exams using the Advanced Features of the Halomda Platform

    • Authors: Philip Slobodsky, Mariana Durcheva, Leonid Kugel

    • Year: March 2024

    • Source: Article

  • Interactive guided training in higher mathematics courses with Halomda platform

    • Authors: Mariana Durcheva, Philip Slobodsky

    • Year: January 2024

    • Source: Conference Paper

  • Trusted e-assessment in high math courses based on the Halomda platform

    • Authors: Philip Slobodsky, Mariana Durcheva

    • Year: January 2024

    • Source: Conference Paper

  • Guided e-Assessment of Math Proofs with the Halomda Platform

    • Authors: Philip Slobodsky, Mariana Durcheva

    • Year: December 2023

    • Source: Article

  • Combined Teaching of Mathematics with the Halomda Platform

    • Authors: Leonid Kugel, Philip Slobodsky, Mariana Durcheva

    • Year: October 2023

    • Source: Article

  • Cryptography Based on Fuzzy Graphs

    • Authors: Mariana Durcheva, Malinka Ivanova

    • Year: August 2023

    • Source: Chapter

  • M-Polar Fuzzy Graphs and Deep Learning for the Design of Analog Amplifiers

    • Authors: Malinka Ivanova, Mariana Durcheva

    • Year: February 2023

    • Source: Article

  • E-assessment of mathematics courses using the Halomda platform

    • Authors: Philip Slobodsky, Mariana Durcheva

    • Year: January 2023

    • Source: Conference Paper

  • Some ideas for applying technology in the discrete mathematics course

    • Authors: Mariana Durcheva

    • Year: September 2022

    • Source: Conference Paper

  • TrES: Tropical Encryption Scheme Based on Double Key Exchange

    • Authors: Mariana Durcheva

    • Year: August 2022

    • Source: Article

  • Transfer Entropy Networks of Cryptocurrencies and Stocks: A Comparative Study

    • Authors: Pavel Tsankov, Mariana Durcheva

    • Year: October 2021

    • Source: Chapter

  • Granger causality networks of S&P 500 stocks

    • Authors: Mariana Durcheva, Pavel Tsankov

    • Year: March 2021

    • Source: Conference Paper

  • My new book: “Semirings as building blocks in cryptography”

    • Authors: Mariana Durcheva

    • Year: January 2020

    • Source: Book

  • Authentication with TeSLA system instruments supporting eAssessment models in engineering courses

    • Authors: Mariana Durcheva, Anna Rozeva

    • Year: November 2019

    • Source: Conference Paper

  • Analysis of similarities between stock and cryptocurrency series by using graphs and spanning trees

    • Authors: Mariana Durcheva, Pavel Tsankov

    • Year: November 2019

    • Source: Conference Paper

  • Innovations in teaching and assessment of engineering courses, supported by authentication and authorship analysis system

    • Authors: Mariana Durcheva, Ivailo Milanov Pandiev, Elena Halova, Anna Rozeva

    • Year: November 2019

    • Source: Conference Paper

  • How to Use CAS (Maple) to Help Students Learn Number Theory

    • Authors: Mariana Durcheva

    • Year: June 2019

    • Source: Article

  • Enhancing Trust in eAssessment – the TeSLA System Solution

    • Authors: Malinka Ivanova, Sushil Bhattacharjee, Sébastien Marcel, Mariana Durcheva

    • Year: May 2019

    • Source: Preprint

  • e-Training and e-Assessment of Mathematical Courses by Xpress-Tutor Integrated in Moodle

    • Authors: Philip Slobodsky, Alexander Ocheretovy, Mariana Durcheva

    • Year: March 2019

    • Source: Conference Paper

  • Modeling mathematical logic using MAPLE

    • Authors: Mariana Durcheva, Elena Nikolova

    • Year: December 2018

    • Source: Conference Paper

  • Learning process enhancement through self-testing and self-assessment using the TeSLA system

    • Authors: Mariana Durcheva, Malinka Ivanova

    • Year: December 2018

    • Source: Conference Paper