Qingguo Lü | Computational Methods | Best Researcher Award

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Assoc. Prof. Dr. Qingguo Lü | Computational Methods | Best Researcher Award

Chongqing University | China

Dr. Qingguo Lü is currently an Associate Professor at the College of Computer Science, Chongqing University, China. With a Ph.D. in Computational Intelligence and Information Processing from Southwest University, his academic journey has been marked by excellence. His work primarily focuses on distributed control and optimization in networked systems, especially in areas involving machine learning, cooperative control, and smart grids.

👨‍🎓Profile

Scopus

🎓 Early Academic Pursuits

Dr. Lü began his academic journey with a Bachelor’s degree in Measurement Control Technology and Instrument from Anhui University of Technology, before advancing to a Master’s degree in Signal and Information Processing at Southwest University. His early academic years were dedicated to mastering core concepts of computational intelligence and information processing, laying the foundation for his later groundbreaking research.

💼 Professional Endeavors

Throughout his career, Dr. Lü has held significant positions, including being a Research Assistant at the Texas A&M University Science Program, Qatar, where he contributed to the research in networked control systems, distributed computing, and smart grids. Following this, he transitioned to his postdoctoral research at Chongqing University, collaborating with Prof. Shaojiang Deng on topics like cooperative control, distributed optimization, and machine learning. His role as an Associate Professor has enabled him to further deepen his research and lead academic projects.

🔬 Contributions and Research Focus

Dr. Lü’s research is deeply embedded in solving real-world problems using distributed optimization algorithms across networked systems. Notable contributions include the development of asynchronous algorithms for decentralized resource allocation, privacy protection algorithms, and the design of algorithms for economic dispatch in smart grids. His research focus is centered on improving distributed optimization through stochastic algorithms, cooperative control, and networked machine learning.

📚 Academic Cites

Dr. Lü’s research has been extensively cited in major journals, indicating the high impact of his work. For example, his paper in IEEE Transactions on Cybernetics (2021) has garnered attention for its privacy-masking stochastic algorithms, highlighting his role in advancing the field of privacy in decentralized systems. His consistent contributions to top-tier journals underscore his prominence as a thought leader in computational intelligence and information processing.

🛠 Research Skills

Dr. Lü possesses advanced skills in developing decentralized algorithms, with expertise in distributed optimization, privacy protection, and machine learning for networked systems. His ability to design efficient algorithms that are not only theoretically sound but also computationally feasible has enabled the practical deployment of these methods in diverse real-world applications, including energy optimization and economic dispatch in smart grids.

🏫 Teaching Experience

As an Associate Professor, Dr. Lü plays an active role in shaping the next generation of researchers and engineers. His teaching focuses on distributed control systems, networked optimization, and machine learning, ensuring that students are well-versed in the latest techniques and applications of computational intelligence. His involvement in academic mentorship and research supervision is highly regarded, helping foster a collaborative and innovative research environment.

🏆 Legacy and Future Contributions

Dr. Lü’s career is already distinguished by his extensive research publications, patents, and contributions to academic growth. His research continues to shape the development of distributed algorithms for complex networks, offering solutions that are highly relevant in today’s rapidly evolving technological landscape. Looking ahead, he aims to expand his work on energy optimization, privacy protection, and networked control systems to tackle emerging challenges in fields like smart cities and autonomous systems.

Publications Top Notes

 

 

Maurizio Dapor| Computational Methods | Best Researcher Award

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Dr. Maurizio Dapor| Computational Methods | Best Researcher Award

Physicist at Fondazione Bruno Kessler, Italy

Maurizio Dapor is an esteemed Italian physicist and Senior Research Scientist at the Interdisciplinary Laboratory for Computational Science at ECT*-FBK. Born on April 23, 1959, he has made significant contributions to both theoretical and experimental physics. With dual habilitations as a Full Professor in these fields, Dapor has been pivotal in advancing computational methods in materials science. His role as an Associate Editor for Computational Materials Science and various visiting professorships across Europe further exemplify his commitment to research and education. Recognized as one of Stanford’s Top 2% Scientists, his work continues to impact the scientific community.

 🎓Profile: 

Education:

Dapor earned his M.Sc. in Physics with Summa Cum Laude from the University of Trento in 1984. He later pursued a Ph.D. in Materials Science and Engineering from the same institution, completing it in 2013. His educational journey began at Liceo Antonio Rosmini, where he graduated with a High School Diploma in 1978. This strong academic foundation has enabled him to excel in various roles in academia and research, contributing extensively to the scientific understanding of materials and their applications.

Professional Experience:

With over three decades of experience, Dapor has held key positions, including Senior Scientist at ECT* and Head of the FBK Interdisciplinary Laboratory for Computational Science. He has served as a Teaching Fellow at the University of Trento, focusing on Solid State Physics and Computational Methods. His international experience includes visiting professorships at Gdansk University of Technology and the University of Sheffield, enhancing his global academic profile. Additionally, Dapor has contributed as a scientific consultant at ETH Zurich, reinforcing his expertise in computational materials research and development.

Research Focus:

Maurizio Dapor’s research primarily centers on computational science and its application to materials physics. He investigates complex phenomena in solid-state systems and develops innovative computational methods to enhance our understanding of material properties. His work addresses critical challenges in transport phenomena and aims to bridge theoretical insights with practical applications. By focusing on interdisciplinary collaborations, Dapor’s research not only advances theoretical frameworks but also contributes to the development of cutting-edge materials for various technological applications.

Awards and Honors:

Dapor’s contributions to physics and materials science have earned him notable recognition, including the distinction of being listed among Stanford’s Top 2% Scientists. His academic achievements are further highlighted by two National Scientific Habilitations as a Full Professor in both Theoretical and Experimental Physics of Matter. His work as an Associate Editor for Frontiers in Materials demonstrates his leadership in advancing the field. These accolades reflect his dedication to research excellence and his influence on the scientific community.

📚Publication Top Notes:

Title: Charge Phenomena in the Elastic Backscattering of Electrons from Insulating Polymers
  • Authors: Dapor, M.
    Publication Year: 2024
    Citations: 0
Title: Electron-induced hydrogen desorption from selected polymers (polyacetylene, polyethylene, polystyrene, and polymethyl-methacrylate)
  • Authors: Dapor, M.
    Publication Year: 2024
    Citations: 1
Title: The role of low-energy electrons in the charging process of LISA test masses
  • Authors: Taioli, S., Dapor, M., Dimiccoli, F., Villani, M., Weber, W.J.
    Publication Year: 2023
    Citations: 11
Title: Mechanical Properties of Twisted Carbon Nanotube Bundles with Carbon Linkers from Molecular Dynamics Simulations
  • Authors: Pedrielli, A., Dapor, M., Gkagkas, K., Taioli, S., Pugno, N.M.
    Publication Year: 2023
    Citations: 6
Title: The Role of Molecular Structure in Monte Carlo Simulations of the Secondary Electron Yield and Backscattering Coefficient from Methacrylic Acid
  • Authors: Wiciak-Pawłowska, K., Winiarska, A., Taioli, S., Franz, M., Franz, J.
    Publication Year: 2023
    Citations: 0
Title: Spin-polarization after scattering
  • Authors: Dapor, M.
    Publication Year: 2023
    Citations: 1
Title: In search of the ground-state crystal structure of Ta2O5 from ab initio and Monte Carlo simulations
  • Authors: Pedrielli, A., Pugno, N.M., Dapor, M., Taioli, S.
    Publication Year: 2023
    Citations: 5

 

 

 

D. Easwaramoorthy | Computational Methods | Best Researcher Award

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Assoc Prof Dr. D. Easwaramoorthy | Computational Methods | Best Researcher Award

Associate Professor at Vellore Institute of Technology, Vellore, India

Dr. D. Easwaramoorthy is an Associate Professor in the Department of Mathematics at Vellore Institute of Technology, Tamil Nadu, India. With a passion for mathematics and education, he has dedicated over 11 years to teaching and nurturing the next generation of mathematicians. Dr. Easwaramoorthy’s commitment to academic excellence is evident through his extensive research and publication record, which has earned him recognition both nationally and internationally. He actively engages in interdisciplinary research and is known for his contributions to fuzzy mathematics and fractal analysis.

🎓Profile:

Education:

Dr. Easwaramoorthy holds a Ph.D. in Mathematics from The Gandhigram Rural Institute, where he completed his thesis on Fuzzy Fractal Analysis with Applications. He earned his M.Sc. in Mathematics from Bharathidasan University with a focus on the Stone-Weierstrass Theorem, achieving a commendable 79.5%. His academic journey began with a B.Sc. in Mathematics from Bishop Heber College, where he excelled with 82%. His educational qualifications reflect a strong foundation in mathematical principles, further enhanced by various certifications in advanced topics, including machine learning and data science.

Professional Experience:

Dr. Easwaramoorthy has over 16 years of research experience and 11 years of teaching experience. He began his academic career as a Junior Assistant Professor at Vellore Institute of Technology and has progressed to his current role as an Associate Professor. Throughout his tenure, he has taught various courses, including Discrete Mathematics, Graph Theory, and Machine Learning. His role as a Ph.D. guide demonstrates his commitment to mentoring aspiring researchers. Dr. Easwaramoorthy’s experience is complemented by participation in international conferences and collaborative projects, enhancing his profile as a well-rounded academic.

Research Focus:

Dr. Easwaramoorthy’s research primarily focuses on Fractal Analysis, Fuzzy Mathematics, and Signal and Image Analysis. His notable work includes publications in peer-reviewed journals and contributions to various academic books. He is particularly interested in applying mathematical concepts to real-world problems, emphasizing the significance of fuzzy logic and fractals in various domains. His involvement in a UGC-funded project exploring chaotic dynamics in the human brain demonstrates his interdisciplinary approach to research. Dr. Easwaramoorthy continues to explore new frontiers in mathematics, aiming to contribute to both theoretical and applied mathematics.

Awards and Honors:

Dr. Easwaramoorthy has received several accolades for his contributions to mathematics, including the Young Scientist Award at the International Scientist Awards on Engineering, Science, and Medicine. He qualified for the Tamil Nadu State Eligibility Test (TNSET) in 2017, showcasing his proficiency in mathematical sciences. Additionally, he was awarded the GRI-Fellowship at The Gandhigram Rural Institute, further recognizing his academic achievements. His accolades reflect not only his research excellence but also his commitment to fostering a culture of academic achievement and innovation.

📚Publication Top Notes:

Generation of Fractals via Iterated Function System of Kannan Contractions in Controlled Metric Space
    • Authors: Thangaraj, C., Easwaramoorthy, D., Selmi, B., Chamola, B.P.
    • Year: 2024
    • Citations: 6
Enumeration of Multivariate Independence Polynomial for Iterations of Sierpinski Triangle Graph
    • Authors: Nithiya, K.S., Easwaramoorthy, D.
    • Year: 2024
    • Citations: 0
Multifractal Detrended Fluctuation Analysis on COVID-19 Dynamics
    • Authors: Dhanzeem Ahmed, M., Easwaramoorthy, D., Selmi, B., Darabi, S.
    • Year: 2023
    • Citations: 1
An Integrated Perspective of Fractal Time Series Analysis for Infected Cases of COVID-19
    • Authors: Gowrisankar, A., Easwaramoorthy, D., Valarmathi, R., Ananth, C., Vasiliev, I.
    • Year: 2023
    • Citations: 0
Fractal Based Automatic Detection of Complexity in COVID-19 X-ray Images
    • Authors: Thangaraj, C., Easwaramoorthy, D., Muhiuddin, G., Selmi, B., Kulish, V.
    • Year: 2023
    • Citations: 0
Fractals via Controlled Fisher Iterated Function System
    • Authors: Thangaraj, C., Easwaramoorthy, D.
    • Year: 2022
    • Citations: 2
Generalized Fractal Dimensions Based Comparison Analysis of Edge Detection Methods in CT Images for Estimating the Infection of COVID-19 Disease
    • Authors: Thangaraj, C., Easwaramoorthy, D.
    • Year: 2022
    • Citations: 9