Arun Kumar | Mathematical Modeling | Best Researcher Award

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Dr. Arun Kumar | Mathematical Modeling | Best Researcher Award

IIT Mandi | India

Dr. Arun Kumar is a Research Associate (Postdoctoral) at the Indian Institute of Technology Mandi, specializing in mathematical and computational modeling of complex biological, ecological, and epidemiological systems. He earned his Ph.D. in Mathematics from Banaras Hindu University in 2023, following an M.Sc. in Mathematics from the same institution and a B.Sc. from CCS University, Meerut. His research focuses on nonlinear dynamics, bifurcation theory, delay differential equations, reaction–diffusion systems, Turing patterns, and the integration of deep learning and physics-informed neural networks (PINNs) for solving partial differential equations. Dr. Kumar has published extensively in high-impact journals on topics including SIR/SIRS epidemic models, cross-diffusion models, predator–prey dynamics, and pattern formation in spatial ecological systems. His work bridges theoretical mathematics and practical applications in disease modeling and ecology, offering insights into complex population interactions, control strategies, and spatio-temporal dynamics. Currently, he is developing deep learning algorithms to solve PDEs with applications in ecological and epidemiological systems. His ongoing research explores predator-prey interactions, learning in ecological models, and the forecasting of infectious diseases such as monkeypox. Dr. Kumar’s contributions have advanced the understanding of nonlinear systems, providing both analytical and computational tools for studying complex biological and ecological phenomena.

Profile : Google Scholar

Featured Publications

Gupta, R. P., & Kumar, A. (2022). “Endemic bubble and multiple cusps generated by saturated treatment of an SIR model through Hopf and Bogdanov–Takens bifurcations.” Mathematics and Computers in Simulation, 197, 1–21.

Gupta, R. P., Kumar, A., & Yadav, D. K. (2024). “The complex dynamical study of a UAI epidemic model in non-spatial and spatial environments.” The European Physical Journal Plus, 139(2), 117.

Yadav, D. K., Gupta, R. P., & Kumar, A. (2022). “Nonlinear dynamics of a three species prey-predator system incorporating fear effect and harvesting.” Journal of Mathematical Control Science and Applications.

Kumar, A., Gupta, R. P., & Tiwari, S. (2022). “Influences of nonlinear cross-diffusion on a reduced SI epidemic model with saturated treatment.”

Kumar, A., Kumari, N., Mandal, S., & Tiwari, P. K. (2025). “Autonomous and non-autonomous dynamics of an SIRS model with convex incidence rate.” Journal of the Franklin Institute, 108236.

Mauro Gallegati | computational economics | Best Researcher Award

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Prof. Mauro Gallegati | computational economics | Best Researcher Award

Università Politecnica delle Marche | Italy

Mauro Gallegati is an influential economist internationally recognized for his contributions to complexity economics, agent-based modeling, and financial instability analysis. He serves as a Full Professor at the Università Politecnica delle Marche, building on earlier academic appointments at the Universities of Teramo, Pescara, and Urbino. His academic foundation includes a Laurea in Political Science and a PhD in Economics under the supervision of Hyman Minsky, shaping his long-standing interest in credit dynamics, business fluctuations, and systemic risk. Over the course of his career, he has held numerous visiting positions at leading global institutions, including Cambridge, Stanford, MIT, Columbia, Kyoto, ETH Zurich, Santa Fe Institute, and the Bank of International Settlements, reflecting the international demand for his expertise. His research encompasses econophysics, income distribution, macroeconomic network models, credit propagation, and financial fragility, resulting in widely cited publications and collaborations with scholars such as Stiglitz, Battiston, and Greenwald. He has coordinated major European research projects, contributed to editorial leadership across multiple journals and book series, and received notable distinctions including the JEBO Best Paper Prize, the WEHIA Prize, DOSI Prize, KNAPP Prize, and Presidential Medals. He continues to advance interdisciplinary economic research and the development of agent-based methodologies for understanding complex economic systems.

Profile : Google Scholar

Featured Publication

Battiston, S., Gatti, D.D., Gallegati, M., Greenwald, B., & Stiglitz, J.E. (2012). “Liaisons dangereuses: Increasing connectivity, risk sharing, and systemic risk” in Journal of Economic Dynamics and Control, 36(8), 1121–1141.

Caiani, A., Godin, A., Caverzasi, E., Gallegati, M., Kinsella, S., & Stiglitz, J.E. (2016). “Agent based-stock flow consistent macroeconomics: Towards a benchmark model” in Journal of Economic Dynamics and Control, 69, 375–408.

Gatti, D.D., Di Guilmi, C., Gaffeo, E., Giulioni, G., Gallegati, M., & Palestrini, A. (2005). “A new approach to business fluctuations: heterogeneous interacting agents, scaling laws and financial fragility” in Journal of Economic Behavior & Organization, 56(4), 489–512.

Battiston, S., Gatti, D.D., Gallegati, M., Greenwald, B., & Stiglitz, J.E. (2007). “Credit chains and bankruptcy propagation in production networks” in Journal of Economic Dynamics and Control, 31(6), 2061–2084.

Tola, V., Lillo, F., Gallegati, M., & Mantegna, R.N. (2008). “Cluster analysis for portfolio optimization” in Journal of Economic Dynamics and Control, 32(1), 235–258.

Shujian Gao | Data Analysis | Best Researcher Award

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Assoc Prof Dr. Shujian Gao | Data Analysis | Best Researcher Award

Ocean University of China | China

PUBLICATION PROFILE

Scopus

🔰INTRODUCTION

ASSOCIATE PROFESSOR DR. SHUJIAN GAO is a dedicated academic and researcher in the field of Ocean Engineering, currently serving as an Associate Professor at the College of Engineering, Ocean University of China. With a passion for advancing offshore engineering through data-driven methods and cutting-edge technologies, Dr. Gao has contributed significantly to both research and education in the marine and oceanic domain.

🎓 EARLY ACADEMIC PURSUITS

DR. GAO BEGAN HIS JOURNEY in academia with an intensive doctoral program at Ocean University of China, where he pursued a Ph.D. in Ocean Engineering from 2018 to 2022. During this time, he honed his expertise in the dynamic behavior of floating structures and laid the foundation for his future contributions in offshore engineering systems and machine learning applications.

💼 PROFESSIONAL ENDEAVORS

FOLLOWING THE COMPLETION of his Ph.D., Dr. Gao continued his career at the Ocean University of China, taking on the role of Lecturer from 2022 to 2024. His dedication and innovative research earned him a promotion in 2024 to Associate Professor, marking a new phase in his academic leadership and mentorship within the university’s engineering faculty.

🧠 CONTRIBUTIONS AND RESEARCH FOCUS

DR. GAO’S RESEARCH INTERESTS span a rich blend of engineering and modern computational techniques. His key focus areas include:

  • Machine learning applications for offshore wind turbines,

  • Motion response monitoring and analysis for deep-sea floating structures, and

  • Dynamic coupling analysis methods and the development of digital twins for offshore structures.
    These interests reflect a strong commitment to enhancing the sustainability, reliability, and intelligence of marine systems using modern data and AI-driven methods.

🌍 IMPACT AND INFLUENCE

DR. GAO’S INFLUENCE extends beyond research publications into the broader academic community. Since 2022, he has served on the Editorial Board of Frontiers in Sustainable Resource Management and joined the Early Career Editorial Board of Marine Engineering Equipment and Technology in 2023. His reviewer role for prestigious journals—including Marine Structures, Ocean Engineering, IEEE Access, and Nonlinear Dynamics—reflects his respected standing among peers in the global research arena.

📚 ACADEMIC CITATIONS AND PUBLICATIONS

DR. GAO HAS AUTHORED impactful research papers in top-tier journals. Selected recent publications include:

  1. A method for reconstructing dynamic displacement of floating structures (Communications Engineering, 2025).

  2. Nonlinear effects in floating structure motion responses (Ocean Engineering, 2024).

  3. Dynamic response decomposition for floating structures (Ocean Engineering, 2024).

  4. Field data analysis of nonlinearity in offshore wind turbines (Applied Ocean Research, 2024).
    These works emphasize practical innovations and theoretical advancements in ocean structure behavior analysis, positioning him as a key thought leader in the field.

🏅 HONORS & AWARDS

WHILE SPECIFIC AWARDS were not listed, Dr. Gao’s rapid rise from Ph.D. graduate to Associate Professor and his selection for editorial boards and peer-review roles speak volumes about his reputation and recognition in the academic and engineering communities.

🔮 LEGACY AND FUTURE CONTRIBUTIONS

DR. GAO’S WORK is poised to shape the future of offshore engineering. His interdisciplinary approach—merging AI, sensor data, and marine engineering mechanics—lays the groundwork for next-generation sustainable and resilient offshore structures. As a mentor, editor, and researcher, his continued influence will extend across academia, industry, and policy sectors tackling the challenges of ocean sustainability and renewable energy.

📝 FINAL NOTE

DR. SHUJIAN GAO EMBODIES the spirit of innovation and academic excellence in ocean engineering. From machine learning applications to structural health monitoring of offshore platforms, his dedication continues to inspire students, collaborators, and global researchers. His journey illustrates the evolving nature of marine technology and the critical role of young scholars in navigating the oceans of tomorrow.

📚 TOP NOTES PUBLICATIONS 

Separation and Quantitative Evaluation for the Nonlinear Effects in the Motion Response of Floating Structures

  • Authors: Shujian Gao, Guoning Feng, Dianzi Liu, Fushun Liu

  • Journal: Ocean Engineering

  • Year: 2024

Investigation to the Nonlinearity Evolution of Offshore Wind Turbines Using Field Data: Application to a 4 MW Monopile Offshore Wind Turbine

  • Authors: Shujian Gao, Guoning Feng, Fushun Liu

  • Journal: Applied Ocean Research

  • Year: 2024

Research on Non-Stationary Characteristic Test and Decomposition for Dynamic Response of Floating Structures

  • Authors: Shujian Gao, Fushun Liu

  • Journal: Ocean Engineering

  • Year: 2024

A Motion Tracking Approach to Position Marine Floating Structures Based on Measured Acceleration and Angular Velocity

  • Authors: Fushun Liu, Shujian Gao, Yong Liu, Dianzi Liu

  • Journal: Ocean Engineering

  • Year: 2022