Haiwei Wu | Engineering | Best Researcher Award

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Prof. Dr. Haiwei Wu | Engineering | Best Researcher Award

Jilin Agricultural University | China

Prof. Dr. Haiwei Wu is an emerging multidisciplinary researcher whose contributions span energy systems, machine learning, spectroscopy, and intelligent diagnostics. His recent research focuses on advanced computational methods applied to energy storage and electric vehicle systems, including the development of an attention-based multi-feature fusion physics-informed neural network for accurate state-of-health estimation of lithium-ion batteries and the application of queuing-theoretic models for sustainable EV charging infrastructure planning. Beyond energy research, he has contributed significantly to the use of mid-infrared spectroscopy combined with machine learning and support vector machines for the authentication and identification of biological and agricultural products, reflecting strong capabilities in analytical modeling and pattern recognition. His publications from 2022 to 2025 highlight expertise in spectral analysis, counterfeit detection, and quality assessment. In addition, he has explored applications of improved YOLOv8 for mechanical part inspection and contributed to research on task-driven cooperative inquiry learning in education. His innovative work is supported by several patents related to electric vehicle charging technologies, demonstrating a commitment to advancing practical, technology-driven solutions across sectors.

Profile : Scopus | Orcid

Featured Publications

Wu, H., Liu, J., Wang, Z., & Li, X. (2025). An attention-based multi-feature fusion physics-informed neural network for state-of-health estimation of lithium-ion batteries. Energies.

Wang, Z., Zou, J., Tu, J., Li, X., Liu, J., & Wu, H. (2025). Towards sustainable EV infrastructure: Site selection and capacity planning with charger type differentiation and queuing-theoretic modeling. World Electric Vehicle Journal.

He, T., Kaimin, W., & Wu, H. (2025). Research on the construction and implementation of a task-driven cooperative inquiry learning model for postgraduate students majoring in music education. Chinese Music Education, (05), 47–53.

Yang, C.-E., Wu, H., Yuan, Y., et al. (2025). Efficient recognition of plum blossom antler hats and red deer antler hats based on support vector machine and mid-infrared spectroscopy. Journal of Jilin Agricultural University, 1–7.

Yang, C.-E., Su, L., Feng, W.-Z., Zhou, J.-Y., Wu, H.-W., Yuan, Y.-M., & Wang, Q. (2023). Identification of Pleurotus ostreatus from different producing areas based on mid-infrared spectroscopy and machine learning. Spectroscopy and Spectral Analysis.

Yang, C.-E., Su, L., Feng, W., et al. (2023). Identification of Pleurotus ostreatus from different origins by mid-infrared spectroscopy combined with machine learning. Spectroscopy and Spectral Analysis, 43(02), 577–582.

Yang, C.-E., Wu, H.-W., Yang, Y., Su, L., Yuan, Y.-M., Liu, H., Zhang, A.-W., & Song, Z.-Y. (2022). A model for the identification of counterfeited and adulterated Sika deer antler cap powder based on mid-infrared spectroscopy and support vector machines. Spectroscopy and Spectral Analysis.

Yang, C.-E., Wu, H., Yang, Y., et al. (2022). Identification model of counterfeiting and adulteration of plum blossom antler cap powder based on mid-infrared spectroscopy and support vector machine. Spectroscopy and Spectral Analysis, 42(08), 2359–2365.

Bo Zhang | Computer Science | Research Excellence Award

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Assoc. Prof. Dr. Bo Zhang | Computer Science | Research Excellence Award

Northwest Polytechnic University | China

Assoc. Prof. Dr. Bo Zhang is an accomplished researcher whose work spans remote sensing, geospatial intelligence, environmental monitoring, and machine learning–driven Earth observation analytics. With 252 citations,  an h-index of 7, and 5, i10-index publications, his scholarly contributions demonstrate a growing and impactful presence in environmental data science. His research advances high-resolution satellite image processing, atmospheric pollutant estimation, digital elevation model reconstruction, and intelligent geospatial mapping. He has produced notable work on transfer learning–enhanced remote sensing, sparse-sample super-resolution mapping, neural-network–based PMx estimation, land surface temperature retrieval, and ozone concentration modeling. His publications in leading journals such as IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Science Bulletin, Remote Sensing, and Indoor and Built Environment highlight his expertise in integrating artificial intelligence with satellite observations to address environmental challenges. His research also contributes to epidemiological spatial analysis and geospatial data fusion, offering multidisciplinary value in Earth system science. Through continuous work on novel algorithms and high-fidelity environmental datasets, he has strengthened the scientific foundation for climate monitoring, pollution assessment, and large-scale geospatial modeling, positioning him as a significant contributor to advanced remote sensing and environmental informatics.

Profile : Scopus | Orcid | Google Scholar

Featured Publications

Yang, C., Zhang, B., Zhang, M., Wang, Q., & Zhu, P. (2025). Research on decision-making strategies for multi-agent UAVs in island missions based on Rainbow Fusion MADDPG algorithm. Drones, 9(10), 673.

Zhang, B., Shi, Z., Hong, D., Wang, Q., Yang, J., Ren, H., & Zhang, M. (2025). Super-resolution reconstruction of the 1 arc-second Australian coastal DEM dataset. Geo-Spatial Information Science, 1–21.


Zhang, B., Xiong, W., Ma, M., Wang, M., Wang, D., Huang, X., Yu, L., Zhang, Q., & others. (2022). Super-resolution reconstruction of a 3 arc-second global DEM dataset. Science Bulletin, 67(24), 2526–2530.


Pan, D., Zhang, M., & Zhang, B. (2021). A generic FCN-based approach for road-network extraction from VHR remote sensing images using OpenStreetMap as benchmarks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.


Zhang, B., Zhang, M., Kang, J., Hong, D., Xu, J., & Zhu, X. (2019). Estimation of PMx concentrations from Landsat 8 OLI images based on a multilayer perceptron neural network. Remote Sensing, 11(6), 646.


Zhu, B., Liu, J., Fu, Y., Zhang, B., & Mao, Y. (2018). Spatio-temporal epidemiology of viral hepatitis in China (2003–2015): Implications for prevention and control policies. International Journal of Environmental Research and Public Health, 15(4), 661.

Chao Wang | Computer Science | Research Excellence Award

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Mr. Chao Wang | Computer Science | Research Excellence Award

North China University of Technology | China

Mr. Chao Wang is an accomplished researcher whose work spans vehicular networks, IoT security, blockchain mechanisms, and food engineering applications, reflecting a multidisciplinary impact. With 809 citations, an h-index of 12, and 16 i10-index publications, he has established a strong scholarly presence supported by numerous high-impact journal articles and competitive conference papers. His research contributions include advanced blockchain-based frameworks for secure communication, innovative privacy-preserving data-sharing models, anomaly detection algorithms for intelligent vehicles, and distributed system security. He has also co-authored influential studies on anti-glycation mechanisms, food bioactive compounds, and cellular protection. His publications from 2021 to 2025 demonstrate consistent output across IEEE Transactions, Future Generation Computer Systems, Food Biomacromolecules, and other reputable venues. His work on collaborative quality control, CAN bus anomaly detection, distributed GAN attack resistance, and multi-party payment channels represents notable advancements in secure systems. He has also contributed to reviews on AGEs inhibition, IoV security, NGS applicability, and blockchain-enabled vehicular applications. Beyond technical innovation, his research extends to biologically focused studies that explore glycation inhibition, fermentation mechanisms, and cellular oxidative protection. Across domains, his scholarly contributions continue to advance secure intelligent systems, data integrity solutions, and interdisciplinary applications, reinforcing his role as a productive and influential researcher.

Profiles : Orcid | Google Scholar

Featured Publications

Bao, C., Niu, Z., He, B., Li, Y., Han, S., Feng, N., Huang, H., Wang, C., Wang, J., & others. (2025). A novel high‐protein composite rice with anti‐glycation properties prepared with crushed rice flour, whey protein and lotus seed proanthocyanidins. Food Biomacromolecules, 2(1), 23–34.

He, Y., Zhou, Z., Wu, B., Xiao, K., Wang, C., & Cheng, X. (2024). Game-theoretic incentive mechanism for collaborative quality control in blockchain-enhanced carbon emissions verification. IEEE Transactions on Network Science and Engineering.

Li, Q., Xiao, K., Yi, C., Yu, F., Wang, W., Rao, J., Liu, M., Zhang, L., Mu, Y., Wang, C., & others. (2024). Inhibition and mechanism of protein nonenzymatic glycation by Lactobacillus fermentum. Foods, 13(8), 1183.

Wang, C., Xu, X., Xiao, K., He, Y., & Yang, G. (2024). Traffic anomaly detection algorithm for CAN bus using similarity analysis. High-Confidence Computing, 4(3), 100207.

Xiao, K., Li, J., He, Y., Wang, X., & Wang, C. (2024). A secure multi-party payment channel on-chain and off-chain supervisable scheme. Future Generation Computer Systems, 154, 330–343.

Feng, N., Feng, Y., Tan, J., Zhou, C., Xu, J., Chen, Y., Xiao, J., He, Y., Wang, C., & others. (2023). Inhibition of advance glycation end products formation, gastrointestinal digestion, absorption and toxicity: A comprehensive review. International Journal of Biological Macromolecules, 249, 125814.

Wu, Q., Kong, Y., Liang, Y., Niu, M., Feng, N., Zhang, C., Qi, Y., Guo, Z., Xiao, J., & others. (2023). Protective mechanism of fruit vinegar polyphenols against AGEs-induced Caco-2 cell damage. Food Chemistry: X, 19, 100736.

Wang, C., Liu, X., He, Y., Xiao, K., & Li, W. (2023). Poisoning the competition: Fake gradient attacks on distributed generative adversarial networks. In Proceedings of the IEEE International Conference on Mobile Ad Hoc and Smart Systems.

Xu, X., Wang, L., Wang, C., Zhu, H., Zhao, L., Yang, S., & Xu, C. (2023). Intelligent connected vehicle security: Threats, attacks and defenses. Journal of Information Science & Engineering, 39(6).

Wang, C., Jiang, L., He, Y., Yang, G., & Xiao, K. (2023). Age of information-based channel scheduling policy in IoT networks under dynamic channel conditions. In China Conference on Wireless Sensor Networks (pp. 88–98).

Zhou, J., Wang, C., Luo, M., Liu, X., Xu, X., & Chen, S. (2023). Spatial-temporal based multi-head self-attention for in-vehicle network intrusion detection system. SSRN 4581213.

Wang, C., Wang, S., Cheng, X., He, Y., Xiao, K., & Fan, S. (2022). A privacy and efficiency-oriented data sharing mechanism for IoTs. IEEE Transactions on Big Data, 9(1), 174–185.

Li, Q., Li, L., Zhu, H., Yang, F., Xiao, K., Zhang, L., Zhang, M., Peng, Y., Wang, C., & others. (2022). Lactobacillus fermentum as a new inhibitor to control advanced glycation end-product formation during vinegar fermentation. Food Science and Human Wellness, 11(5), 1409–1418.

Wu, Q., Liang, Y., Kong, Y., Zhang, F., Feng, Y., Ouyang, Y., Wang, C., Guo, Z., & others. (2022). Role of glycated proteins in vivo: Enzymatic glycated proteins and non-enzymatic glycated proteins. Food Research International, 155, 111099.

Wang, C., Cheng, X., Li, J., He, Y., & Xiao, K. (2021). A survey: Applications of blockchain in the Internet of Vehicles. EURASIP Journal on Wireless Communications and Networking, 2021(1), 77.

Xu, S., Chen, X., Wang, C., He, Y., Xiao, K., & Cao, Y. (2021). A lattice-based ring signature scheme to secure automated valet parking. In Wireless Algorithms, Systems, and Applications.

Leema Nelson | Computer Science | Research Excellence Award

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Dr. Leema Nelson | Computer Science | Research Excellence Award

Chitkara University | India 

Dr. Leema Nelson is an accomplished researcher whose scholarly contributions span machine learning, clinical decision support systems, composite materials, signal processing, and intelligent diagnostic frameworks. With a total of 1206 citations, an h-index of 17, and 29 i10-index publications, her research demonstrates both depth and sustained impact across interdisciplinary domains. She has produced numerous high-quality peer-reviewed articles, many in leading Elsevier journals such as Applied Soft Computing and Materials & Design, focusing on neural network optimization, characterization of metal-matrix composites, wear modelling, and advanced computational methods. Her work in clinical data classification, including diabetes and PCOS diagnosis, highlights the integration of artificial intelligence into healthcare decision-making. In recent years, she expanded her research into video smoke detection, cyber-security–based email filtering, audio source separation, and welding parameter optimization using intelligent algorithms. Her studies in deepfake detection, text recognition, and clinical support systems reflect her continuing advancements in data-driven AI models. She has also contributed extensively to IEEE conferences, presenting innovations in masked face detection, ultrasound image analysis, mobile app frameworks, and disease prediction models. Overall, her scientific output reflects strong productivity, interdisciplinary expertise, and meaningful contributions to both computational intelligence and applied engineering research.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Jibinsingh, B. R., & Nelson, L. (2025). FL-WOSP: Federated learning with Walrus Optimization for sepsis prediction using MIMIC-III physiological and clinical data. Pattern Recognition. Advance online publication.

Batra, H., & Nelson, L. (2024). ESD: E-mail spam detection using cybersecurity-driven header analysis and machine learning-based content analysis. International Journal of Performability Engineering, 20(4).

Nelson, L. (2024). Data-driven clinical decision support system using neural network topology optimization for PCOS diagnosis. Journal of Soft Computing and Data Mining.

Batra, H., & Nelson, L. (2024). A three-stage deepfake detection framework using deep learning models with multimedia data. International Journal of Intelligent Systems and Applications.

Shanmuga Priya, M., Pavithra, A., & Leema, N. (2024). Character/word modelling: A two-step framework for text recognition in natural scene images. Computer Science.

Batra, H., & Nelson, L. (2023). DCADS: Data-driven computer aided diagnostic system using machine learning techniques for polycystic ovary syndrome. International Journal of Performability Engineering, 19(3).

Kumar, V. A., Rao, C. V. R., & Leema, N. (2023). Audio source separation by estimating the mixing matrix in underdetermined condition using successive projection and volume minimization. International Journal of Information Technology, 15(4), 1831–1844.

Ramesh, A., Sivapragash, M., Ajith Kumar, K. K., & Leema, N. (2023). Investigating the quality of TIG-welded aluminium alloy 5086 using the online acoustic emission and optimization of welding parameters using global best-based modified artificial bee colony algorithm. Transactions of the Indian Institute of Metals, 1–14.

Pranshu Kumar Soni, & Leema, N. (2023). PCP: Profit-driven churn prediction using machine learning techniques in banking sector. International Journal of Performability Engineering, 19(5), 303–311.

Vettum Perumal, S., Suyamburajan, V., Chidambaranathan, V. S., & Nelson, L. (2023). Characterization of microstructure and mechanical behaviour in activated tungsten inert gas welded dissimilar AA joint of AA 5083 and AA 6061 alloys. Journal of the Institution of Engineers (India): Series D, 1–9.

Zhang Zhenqian | Neuroscience | Best Researcher Award

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Mr. Zhang Zhenqian | Neuroscience | Best Researcher Award

University of Toyama | Japan

Mr. Zhang Zhenqian is a dedicated researcher whose work bridges artificial intelligence, machine learning, and meteorology, with an emphasis on developing advanced neural network models for predictive analytics. His recent publication, “RD2: Reconstructing the Residual Sequence via Under Decomposing and Dendritic Learning for Generalized Time Series Predictions,” featured in Neurocomputing (October 2025), showcases his innovative approach to enhancing time series forecasting accuracy through the integration of dendritic learning mechanisms and residual sequence reconstruction. Collaborating with Houtian He, Zhenyu Lei, Zihang Zhang, and Shangce Gao, Mr. Zhang contributes to advancing the computational intelligence field by addressing challenges in dynamic data modeling and predictive reliability. His research explores the intersection of data-driven modeling and environmental systems, offering valuable insights for improving real-world forecasting, particularly in meteorological and environmental applications. With a growing scholarly presence and contributions recognized through peer-reviewed international publications, Mr. Zhang exemplifies a new generation of researchers committed to interdisciplinary innovation. His work not only strengthens the theoretical foundations of artificial intelligence but also demonstrates its transformative potential in understanding and managing complex natural and engineered systems.

Profile : Orcid

Featured Publication

Zhang, Z., He, H., Lei, Z., Zhang, Z., & Gao, S. (2025). RD2: Reconstructing the residual sequence via under decomposing and dendritic learning for generalized time series predictions. Neurocomputing, 131867.

Simy Baby | Engineering | Best Researcher Award

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Mrs. Simy Baby | Engineering | Best Researcher Award

National Institute of Technology | India

Mrs. Simy Baby is an emerging researcher whose scholarly contributions center on semantic communications, machine learning, and computer vision, with a strong emphasis on communication-efficient feature extraction for edge inference tasks. She has authored 2 documents, received 2 citations, and holds an h-index of 1, reflecting the growing impact of her research in advanced communication technologies. Her publications in SCI-indexed journals, including Elsevier’s Computers & Electrical Engineering and IEEE Transactions on Cognitive Communications and Networking, demonstrate her commitment to innovation and excellence. Her study, “Complex Chromatic Imaging for Enhanced Radar Face Recognition”, introduced a novel complex-valued representation preserving amplitude and phase information of mmWave radar signals, achieving 99.7% recognition accuracy. Another major contribution, “Complex-Valued Linear Discriminant Analysis on mmWave Radar Face Signatures for Task-Oriented Semantic Communication”, proposed a CLDA-based encoding framework that improved feature interpretability and robustness under varying channel conditions. Her ongoing projects explore Data Fusion Discriminant Analysis (DFDA) for multi-view activity recognition and Semantic Gaussian Process Regression (GPR) for vehicular pose estimation, advancing the integration of semantic communication and computer vision. Mrs. Simy Baby’s research represents a vital step toward the development of intelligent, efficient, and adaptive communication systems for next-generation technologies.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Baby, S. M., & Gopi, E. S. (2025). Complex valued linear discriminant analysis on mmWave radar face signatures for task-oriented semantic communication. IEEE Transactions on Cognitive Communications and Networking.

Baby, S. M., & Gopi, E. S. (2025, April). Complex chromatic imaging for enhanced radar face recognition. Computers and Electrical Engineering.

Diogo Santiago | Computer Science | Best Researcher Award

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Mr. Diogo Santiago | Computer Science | Best Researcher Award

Oracle | Brazil

Mr. Diogo Santiago is a highly accomplished technology professional with extensive experience spanning software engineering, big data, and artificial intelligence. Beginning his career in 2009 as a software engineer developing major e-commerce platforms in Brazil, he transitioned into data engineering and science, mastering technologies like Hadoop, Spark, Hive, and Sqoop for large-scale data processing and migration. Since 2018, he has specialized in data science and AI, contributing to diverse projects in computer vision, anomaly detection, logistics optimization, and generative AI, including GAN and diffusion model applications for virtual try-on systems. As an AI Architect at Oracle for LATAM, he designs advanced AI architectures, supports clients with resource planning, and enhances model deployment efficiency through GPU optimization and large language model serving using vLLM and SGLang. His prior roles at Lambda3, Tivit, and Qintess involved developing ML models, data pipelines, and automation systems using cloud technologies such as GCP, AWS, and OCI. With multiple postgraduate qualifications in Big Data and Machine Learning for Finance, along with a Master’s in Medical Texture Imaging, he exemplifies innovation and leadership in merging AI research with scalable enterprise solutions.

Profile : Orcid

Featured Publication

Adorno, P. L. V., Jasenovski, I. M., Santiago, D. F. D. M., & Bergamasco, L. (2023, May 29). Automatic detection of people with reduced mobility using YOLOv5 and data reduction strategy. Conference paper.

 

Yaqin Wu | Computer Science | Excellence in Research Award

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Ms. Yaqin Wu | Computer Science | Excellence in Research Award

Shanxi Agricultural University | China

Ms. Yaqin Wu is an accomplished researcher and educator specializing in acoustic signal analysis, deep learning, and multimodal information fusion, with a research record reflecting 80 citations across 78 documents, 9 publications, and an h-index of 3. She holds a Master of Engineering in Electronic and Communication Engineering from Tianjin University and a Bachelor’s degree in Communication Engineering from Dalian Maritime University. Currently serving as a full-time faculty member at the School of Software, Shanxi Agricultural University, she teaches courses such as Speech Signal Processing, Natural Language Processing, and Human-Computer Interaction. Ms. Wu has led and contributed to several cutting-edge research projects, including pathological voice restoration, multimodal animal behavior monitoring, and AVS audio codec development. She has authored multiple SCI-indexed papers and holds several patents and software copyrights related to voice signal processing. Her technical proficiency spans Python, MATLAB, Linux systems, and MySQL databases. Notably, her master’s thesis earned the Outstanding Achievement Award of Engineering Master’s Practice from Tianjin University. Through her innovative contributions in signal processing and intelligent systems, Ms. Wu continues to advance the intersection of engineering and artificial intelligence research.

Profiles : Scopus | Orcid

Featured Publications

Zhang, J., Wu, Y., & Zhang, T. (2025). Fusing time-frequency heterogeneous features with cross-attention mechanism for pathological voice detection. Journal of Voice. Advance online publication.

Li, X., Wang, K., Chang, Y., Wu, Y., & Liu, J. (2025). Combining Kronecker-basis-representation tensor decomposition and total variational constraint for spectral computed tomography reconstruction. Photonics, 12(5), 492.

Michele Buzzicotti | Physics and Astronomy | Best Paper Award

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Dr. Michele Buzzicotti | Physics and Astronomy | Best Paper Award

University of Rome Tor Vergata | Italy

Dr. Michele Buzzicotti is an accomplished physicist whose research bridges turbulence modeling, data-driven fluid dynamics, and machine learning applications in complex systems. He has authored 39 scientific documents, accumulating 703 citations across 513 records with an h-index of 17, reflecting his consistent scientific influence in computational physics and atmospheric modeling. Holding a Ph.D. in Physics from the University of Rome Tor Vergata (2017), his doctoral work focused on the effects of Fourier mode reduction in turbulence. Currently serving as a tenure-track faculty member at the Department of Physics, University of Rome Tor Vergata, Dr. Buzzicotti has held visiting research appointments at the Technical University of Eindhoven and the University of Rochester. His projects, such as the €1.3 million Italian Ministry of Research–funded “Data-driven and Equation-based Tools for Complex Turbulent Flows,” showcase his leadership in advancing AI-integrated turbulence studies. His publications in Nature Machine Intelligence, Physical Review Letters, and Europhysics Letters highlight pioneering contributions to stochastic modeling and generative AI for fluid dynamics. A reviewer for top-tier journals and a member of EUROMECH and APS, Dr. Buzzicotti continues to shape the future of theoretical and applied turbulence research through innovative interdisciplinary approaches.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Li, T., Biferale, L., Bonaccorso, F., Buzzicotti, M., & Centurioni, L. (2025). Stochastic reconstruction of gappy Lagrangian turbulent signals by conditional diffusion models. Communications Physics.

Freitas, A., Um, K., Desbrun, M., Buzzicotti, M., & Biferale, L. (2025). Solver-in-the-loop approach to closure of shell models of turbulence. Physical Review Fluids.

Martin, J., Lübke, J., Li, T., Buzzicotti, M., Grauer, R., & Biferale, L. (2025). Generation of cosmic-ray trajectories by a diffusion model trained on test particles in 3D magnetohydrodynamic turbulence. The Astrophysical Journal Supplement Series.

Li, T., Tommasi, S., Buzzicotti, M., Bonaccorso, F., & Biferale, L. (2024). Generative diffusion models for synthetic trajectories of heavy and light particles in turbulence. International Journal of Multiphase Flow.

Khatri, H., Griffies, S. M., Storer, B. A., Buzzicotti, M., Aluie, H., Sonnewald, M., Dussin, R., & Shao, A. (2024). A scale‐dependent analysis of the barotropic vorticity budget in a global ocean simulation. Journal of Advances in Modeling Earth Systems.

Li, T., Biferale, L., Bonaccorso, F., Scarpolini, M. A., & Buzzicotti, M. (2024). Synthetic Lagrangian turbulence by generative diffusion models. Nature Machine Intelligence.

Li, T., Lanotte, A. S., Buzzicotti, M., Bonaccorso, F., & Biferale, L. (2023). Multi-scale reconstruction of turbulent rotating flows with generative diffusion models. Atmosphere, 15(1), 60.

Storer, B. A., Buzzicotti, M., Khatri, H., Griffies, S. M., & Aluie, H. (2023). Global cascade of kinetic energy in the ocean and the atmospheric imprint. Science Advances.

Li, T., Buzzicotti, M., Biferale, L., Bonaccorso, F., Chen, S., & Wan, M. (2023). Multi-scale reconstruction of turbulent rotating flows with proper orthogonal decomposition and generative adversarial networks. Journal of Fluid Mechanics.

Buzzicotti, M., Storer, B. A., Khatri, H., Griffies, S. M., & Aluie, H. (2023). Spatio‐temporal coarse‐graining decomposition of the global ocean geostrophic kinetic energy. Journal of Advances in Modeling Earth Systems.

Seyedeh Azadeh Fallah Mortezanejad | Mathematics | Best Researcher Award

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Dr. Seyedeh Azadeh Fallah Mortezanejad | Mathematics | Best Researcher Award

Jiangsu University | China

Author Profile

Scopus

Orcid

Google Scholar

Early Academic Pursuits

Dr. Seyedeh Azadeh Fallah Mortezanejad began her academic journey with a strong foundation in statistics, completing her undergraduate and postgraduate studies at Guilan University, Iran. Her master’s research on semi-parametric estimation of conditional copula reflected her interest in statistical theory and dependence structures. She advanced her academic training with doctoral research at Ferdowsi University of Mashhad, focusing on applications of entropy in statistical quality control, which laid the groundwork for her later interdisciplinary research.

Professional Endeavors

Following her doctoral studies, Dr. Mortezanejad pursued postdoctoral research at Jiangsu University in China, under the guidance of Professor Ruochen Wang. Supported by the National Natural Science Foundation of China, her work explored advanced applications of statistical inference in engineering systems. Her professional engagements span teaching, collaborative research, and presenting at international conferences, reflecting her role as both a researcher and an academic contributor.

Contributions and Research Focus

Her research lies at the intersection of statistics, data science, and engineering. She has significantly contributed to areas such as time series analysis, dependence data, deep learning, and statistical quality control. Her expertise in copula functions and entropy has enabled novel methods for addressing challenges in multivariate data analysis and control charts. More recently, her work integrates machine learning and physics-informed neural networks for solving complex problems in multivariate time series and image processing.

Accolades and Recognition

Dr. Mortezanejad’s scholarly contributions have been recognized through numerous publications in leading journals, including Entropy, Sankhya B, and Physica A. She has been invited to present her findings at international workshops in Germany, France, Vietnam, and Spain, underscoring her recognition in global research communities. Her role as a reviewer for reputed journals and conferences further reflects her professional standing in the field.

Impact and Influence

Through her interdisciplinary research, Dr. Mortezanejad has bridged the gap between theoretical statistics and practical applications in fields such as healthcare, engineering, and financial modeling. Her contributions to statistical quality control, machine learning applications, and Bayesian inference have influenced both academic discourse and applied research, making her work relevant across diverse scientific domains.

Legacy and Future Contributions

With her strong background in both theoretical and applied statistics, Dr. Mortezanejad is poised to continue advancing research in modern statistical methods, particularly in integrating entropy-based approaches with machine learning. Her future work is expected to focus on enhancing predictive analytics, developing robust statistical tools for big data, and contributing to sustainable innovations in engineering and healthcare.

Publications

Article: Physics-Informed Neural Networks with Unknown Partial Differential Equations: An Application in Multivariate Time Series
Authors: Seyedeh Azadeh Fallah Mortezanejad, Ruochen Wang, Ali Mohammad-Djafari
Journal: Entropy
Year: 2025

Article: Variational Bayesian Approximation (VBA): Implementation and Comparison of Different Optimization Algorithms
Authors: Seyedeh Azadeh Fallah Mortezanejad, Ali Mohammad-Djafari
Journal: Entropy
Year: 2024

Article: Variational Bayesian Approximation (VBA) with Exponential Families and Covariance Estimation
Authors: Seyedeh Azadeh Fallah Mortezanejad, Ali Mohammad-Djafari
Journal: Physical Sciences Forum
Year: 2023

Article: Variational Bayesian Approximation (VBA): A Comparison between Three Optimization Algorithms
Authors: Seyedeh Azadeh Fallah Mortezanejad, Ali Mohammad-Djafari
Journal/Conference: MaxEnt 2022 (Conference Proceedings)
Year: 2023

Article: Evaluation of Anti-lice Topical Lotion of Ozonated Olive Oil and Comparison of its Effect with Permethrin Shampoo
Authors: Omid Rajabi, Atoosa Haghighizadeh, Seyedeh Azadeh Fallah Mortezanejad, Saba Dadpour
Journal: Reviews on Recent Clinical Trials
Year: 2022

Conclusion

Dr. Seyedeh Azadeh Fallah Mortezanejad’s career reflects a rare blend of statistical rigor, innovative application, and international recognition. Her early commitment to statistical theory, coupled with her interdisciplinary contributions, has positioned her as a rising figure in applied statistics and data science. With her expanding research footprint, she is set to leave a lasting impact on statistical research and its applications in science, technology, and industry.