Mr. Andi Chen | Deep Learning | Excellence in Research Award

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Mr. Andi Chen | Deep Learning | Excellence in Research Award

Nanjing University | China

Mr. Andi Chen is an interdisciplinary researcher specializing in quantum-inspired neural networks, tensorized deep learning, and artificial intelligence for pattern recognition and multimodal generation. His research integrates quantum computing concepts with modern neural architectures, including convolutional, residual, and diffusion-based models. He has contributed to high-impact journals and conferences in neural computation and applied physics, while actively engaging in innovation projects on large language models, reinforcement learning fine-tuning, and AI-driven scientific applications across mathematics, engineering, and economics.

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Dr. Alexandra Moshou | Seismological Data analysis | Research Excellence Award

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Dr. Alexandra Moshou | Seismological Data analysis | Research Excellence Award

Civil Engineering | Ηellenic Mediterranean University | Greece

Dr. Alexandra Moshou is a seismologist specializing in earthquake source characterization, waveform analysis, and advanced computational methods in seismology. Her research integrates mathematical modeling, signal processing, and geophysical data analysis to improve understanding of seismic sources and fault systems. She has contributed to software development for seismic parameter estimation and applies modern approaches such as machine learning and spatiotemporal modeling to large seismic datasets, supporting innovation in earthquake science, hazard assessment, and subsurface structural interpretation.

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Mr. Zeeshan Rasheed | Machine Learning | Research Excellence Award

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Mr. Zeeshan Rasheed | Machine Learning | Research Excellence Award

Lecturer Computer Science | Mir Chakar Khan Rind University Sibi Balochistan | Pakistan

Mr. Zeeshan Rasheed is a computer science researcher whose work spans machine learning, data intelligence, wireless networks, and AI-driven decision systems. His research focuses on optimizing network cooperation, developing neural models for sustainable wireless resource management, improving early disease prediction, and analyzing AI’s role in media and social systems. He has contributed to studies on sentiment analysis, intelligent network strategies, pandemic modelling, and crowdsourced data reliability. His scholarly output reflects a continuous commitment to advancing practical and socially relevant AI applications, supported by publications across multidisciplinary journals. His work also demonstrates growing academic impact with ongoing contributions to emerging technological challenges.

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Mr. Thyago Lima | Remote Sensing | Research Excellence Award

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Mr. Thyago Lima | Remote Sensing | Research Excellence Award

São Paulo State University | Brazil

Mr. Thyago Anthony Soares Lima is a geographer and disaster-risk analyst specializing in remote sensing, InSAR, and multi-hazard urban risk assessment. His research focuses on characterizing ground deformation, identifying subsidence typologies, and integrating geological, geomorphological, and geospatial data to strengthen urban resilience. He develops workflows that link land subsidence with flooding, erosion, and slope-instability risks, enabling evidence-based planning for hazard-prone regions. His work bridges geoscience and public decision-making by producing operational subsidence and hazard maps used by civil-defense agencies, planners, and environmental managers. His contributions advance sustainable territorial management and innovative methodologies in geoinformatics and environmental hazard analysis.

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Mr. Wenpei Lu | Thermodynamics | Research Excellence Award

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Mr. Wenpei Lu | Thermodynamics | Research Excellence Award

University of Paris-Saclay | France

Mr. Wenpei Lu is an emerging researcher in thermal physics and energy engineering, with a strong interdisciplinary background spanning microfluidics, heat transfer, multiphase flow, and enhanced oil recovery systems. His work focuses on experimental and numerical investigations that advance the understanding of droplet-based microfluidics and convective heat transfer, contributing to high-efficiency energy utilization and improved subsurface engineering processes. He has carried out influential research on Steam-Assisted Gravity Drainage (SAGD), foam-assisted recovery systems, steam flow control technologies, and the behavior of nanoparticle-stabilized foams in porous media. Across his projects, he integrates advanced experimentation with computational simulation tools such as ANSYS Fluent, MATLAB, and CMG-STARS to evaluate flow behaviors, optimize thermal mechanisms, and develop innovative solutions for oilfield challenges. His scientific output includes studies on reservoir stimulation, stress-sensitive pressure behavior, steam distribution optimization, and capillary-driven flow mechanisms. According to Scopus, his research has received citations from 32 documents, demonstrating growing recognition in the field, and he holds an h-index of 1. His Google Scholar profile similarly reflects active citation growth aligned with his early career stage. With a research foundation that connects thermal sciences, micro-scale flow physics, and applied petroleum engineering, Mr. Lu is well-positioned for impactful contributions to the scientific community. His innovative methodologies, interdisciplinary approach, and commitment to advancing energy-efficient recovery technologies align strongly with the objectives of the Research Excellence Award, making him a suitable candidate whose work demonstrates both scientific rigor and real-world relevance.

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Scopus

Featured Publications

Cao, C., Zhou, F., Cheng, L., Liu, S., Lu, W., & Wang, Q. (2021). A comprehensive method for acid diversion performance evaluation in strongly heterogeneous carbonate reservoirs stimulation using CT. Journal of Petroleum Science and Engineering, 203, 108614.

Cao, C., Cheng, L., Zhang, X., Jia, P., & Lu, W. (2021). A comprehensive model integrating stress sensitivity for pressure transient behavior study on the two-zone for offshore loose sandstone reservoirs. SPE Middle East Oil and Gas Show and Conference, Bahrain.

Prof. Dr. Ahmed Ghezal | Difference Equations | Research Excellence Award

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Prof. Dr. Ahmed Ghezal | Difference Equations | Research Excellence Award

Researcher | Abdelhafid Boussouf University of Mila | Algeria

Prof. Dr. Ahmed Ghezal is a highly accomplished researcher in mathematical statistics, stochastic processes, and difference equations, widely recognized for his meaningful contributions to nonlinear time series modeling and Markov-switching frameworks. His work bridges theoretical rigor with applied mathematical modeling, focusing on the probabilistic properties, asymptotic inference, and stability analysis of advanced stochastic systems. With a research output that spans influential studies on bilinear, threshold, GARCH-type, and fuzzy difference equations, he has significantly advanced the understanding of periodicity, regime switching, and nonlinear dynamics in statistical models. His scholarly impact is evidenced through strong citation metrics across global indexing platforms, including Scopus with 342 citations from 93 documents and an h-index of 13, and Google Scholar with 304 citations, an h-index of 10, and an i10-index of 10. His Web of Science metrics further highlight his influence, with 214 citations, 41 publications, and an h-index of 10, reflecting consistent contributions to high-quality mathematical and statistical journals. His research excellence is marked by rigorous analytical methods, strong theoretical proofs, and innovative extensions to existing models, making his work foundational for scholars exploring nonlinear dynamics and complex stochastic structures. Given his impactful publication record, strong citation profile, and substantial contributions to statistical theory and difference equations, Prof. Ghezal demonstrates outstanding suitability for the Research Excellence Award, representing a researcher whose contributions continue to shape contemporary developments in mathematical modeling and stochastic analysis.

Profile

Scopus | Google Scholar

Featured Publications

Bibi, A., & Ghezal, A. (2018). Markov-switching bilinear−GARCH models: Structure and estimation. Communications in Statistics – Theory and Methods, 47(2), 307–323. (Cited 21)

Bibi, A., & Ghezal, A. (2015). On the Markov-switching bilinear processes: Stationarity, higher-order moments and beta-mixing. Stochastics, 87(6), 889–912. (Cited 21)

Ghezal, A. (2023). Note on a rational system of 4k+4-order difference equations: Periodic solution and convergence. Journal of Applied Mathematics and Computing, 69(2), 2207–2215. (Cited 20)

Ghezal, A. (2021). QMLE for periodic time-varying asymmetric GARCH models. Communications in Mathematics and Statistics, 9(3), 273–297. (Cited 16)

Ghezal, A. (2024). Spectral representation of Markov-switching bilinear processes. São Paulo Journal of Mathematical Sciences, 18(1), 459–479. (Cited 15)

Prof. Qingna Li | Numerical Optimization | Best Researcher Award

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Prof. Qingna Li | Numerical Optimization | Best Researcher Award

Professor in Numerical Optimization | Beijing Institute of Technology | China

Prof. Qingna Li is a leading scholar in computational mathematics and optimization, widely recognized for her influential contributions to numerical algorithms, matrix optimization, support vector machines, hyperparameter tuning through bilevel optimization, large-scale MIMO detection, and correlation matrix modeling. Her research bridges theoretical rigor with practical impact, with applications spanning signal processing, machine learning, classification systems, hypergraph matching, molecular conformation, and radar surveillance analysis. She has produced a strong body of high-impact work, reflected in a growing citation footprint: Google Scholar reports 489 citations, an h-index of 10, and 11 i10-index publications, while Scopus documents also reflect sustained citation growth and scholarly impact across optimization, numerical analysis, and applied machine learning. Professor Li has developed several widely used MATLAB packages for SVM models, correlation matrix estimation, and MIMO detection, each grounded in her peer-reviewed publications and enabling significant uptake by researchers and practitioners. Her work on derivative-free methods, semismooth Newton frameworks, quadratic programming relaxations, and data-driven wavelet approaches has strengthened modern optimization theory and advanced computational tools for high-dimensional problems. She is an active researcher with a documented record of collaborative publications in top journals such as SIAM Journal on Optimization, IMA Journal of Numerical Analysis, Computational Optimization and Applications, and Applied and Computational Harmonic Analysis. With a demonstrated ability to generate impactful methods, lead research groups, and contribute meaningfully to the global optimization community, Professor Qingna Li is exceptionally well suited for a Best Researcher Award, given her innovation, citation influence, research leadership, and sustained contributions to computational optimization.

Profile

Google Scholar

Featured Publications

Li, Q., & Li, D. H. (2011). A class of derivative-free methods for large-scale nonlinear monotone equations. IMA Journal of Numerical Analysis, 31(4), 1625–1635. Citations: 192.

Li, Q., & Qi, H. (2011). A sequential semismooth Newton method for the nearest low-rank correlation matrix problem. SIAM Journal on Optimization, 21(4), 1641–1666. Citations: 50.

Yin, J., & Li, Q. (2019). A semismooth Newton method for support vector classification and regression. Computational Optimization and Applications, 73(2), 477–508. Citations: 33.

Zhao, P. F., Li, Q., Chen, W. K., & Liu, Y. F. (2021). An efficient quadratic programming relaxation-based algorithm for large-scale MIMO detection. SIAM Journal on Optimization, 31(2), 1519–1545. Citations: 9.

Pang, T., Li, Q., Wen, Z., & Shen, Z. (2020). Phase retrieval: A data-driven wavelet frame-based approach. Applied and Computational Harmonic Analysis, 49(3), 971–1000. Citations: 11.

Mr. Muheki Jonas | Computational Physics | Research Excellence Award

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Mr. Muheki Jonas | Computational Physics | Research Excellence Award

University of Houston | United States

Mr. Jonas Muheki is a rising researcher in physics specializing in biological, medical, and photonic sciences, with a focus on developing opto-electromagnetic modalities for cancer therapy and advanced biosensing technologies. His work spans computational modeling, metamaterial design, plasmonics, and machine learning-enhanced diagnostics, contributing to innovations in solar absorbers, terahertz sensors, and high-sensitivity biomedical detection platforms. He has produced impactful research recognized through citations on major indexing platforms, including Scopus and Google Scholar, reflecting strong scientific visibility. His interdisciplinary expertise positions him as a strong candidate for innovation-focused awards in physics, biomedical engineering, and advanced sensing technologies.

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Prof. Xin Tang | Bioengineering | Best Researcher Award

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Prof. Xin Tang | Bioengineering | Best Researcher Award

Associate Professor | University of Florida | United States

Prof. Xin Tang is a leading researcher in biomechanics, mechanobiology, biophysics, and cancer mechanomedicine, focusing on how mechanical forces, calcium signaling, YAP pathways, and microenvironmental cues shape cancer progression. His work integrates advanced imaging, nano and microfabrication, genome editing, and protein engineering to develop all-optical mechanobiology platforms and functional interrogation systems for tumor biology. He also contributes to theoretical modeling that links molecular mechano-sensitivity with tissue-scale behavior. His research bridges fundamental biophysics with translational cancer science, providing new insights into drug resistance, mechanotransduction, and therapeutic targeting, while shaping interdisciplinary advances in soft matter and cellular mechanics.

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Mr. Sumit Hassan Eshan | Smart Antenna | Young Researcher Award

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Mr. Sumit Hassan Eshan | Smart Antenna | Young Researcher Award

Lead Technical Engineer | Contessa Solutions and Consultants Ltd | Bangladesh

Mr. Sumit Hassan Eshan is a Bangladeshi researcher and engineering professional whose scholarly contributions span smart antennas, nanomaterial-based biomedical sensing, wireless power transfer, terahertz communication, and advanced materials for next-generation wireless systems. His research integrates materials science with electromagnetic design, focusing on nano-engineered antennas using graphene, carbon nanotubes, and transition-metal dichalcogenides for medical diagnostics, on-body sensing, and 6G terahertz applications. Eshan has authored 12 peer-reviewed publications, including four journal papers and eight conference papers across respected SCI and Scopus-indexed venues. One of his works was highlighted on the front cover of a Q1 journal, showcasing the novelty of his contributions to nanomaterial-enabled antennas for cancer detection. His citation record demonstrates his growing academic influence, with 76 citations, an h-index of 6, and an i10-index of 4 on Google Scholar, and 54 citations with an h-index of 5 on Scopus. Eshan’s research covers interdisciplinary domains such as wireless power amplification, nanomaterial spin-coating techniques, biomedical on-body antenna systems, and efficient THz structures for future communication technologies. His continuous engagement as a peer reviewer for prominent engineering journals further reflects his expertise in antenna design, wireless communication, applied electromagnetics, and emerging materials. With a strong foundation in experimental and simulation-based design using CST Studio Suite, Eshan aims to advance innovative antenna technologies that bridge healthcare diagnostics and next-generation wireless systems. His scholarly record positions him as a promising early-career researcher contributing impactful solutions at the intersection of engineering, materials science, and biomedical sensing.

Profiles

Scopus | ORCID | Google Scholar | LinkedIn | ResearchGate

Featured Publications

Hasan, R. R., Jasmine, J., Saleque, A. M., Eshan, S. H., Tusher, R. T. H., Zabin, S., Nowshin, N., Rahman, M. A., & Tsang, Y. H. (2023). Spin coated multi-walled carbon nanotube patch antenna for breast cancer detection. Advanced Materials Technologies, 8(20), 1–13. (Q1, cited)

Anowar, T. I., Hasan, R. R., Eshan, S. H., & Foysal, M. (2025). Enhanced wireless power transfer system using integrated RF amplification. Results in Engineering. (Q1, cited)

Hasan, R. R., Saha, S., Eshan, S. H., Basak, R., Ivan, M. N. A. S., Saleque, A. M., Tusher, R. T. H., Zabin, S., Rahman, M. A., & Tsang, Y. H. (2024). A compact spin-coated graphene UWB antenna for breast tumor detection. Advanced Engineering Materials. (Q1, cited)

Roy, A., Bhuiyan, M. R., Islam, M. A., Saha, P., Eshan, S. H., Hasan, R. R., & Basak, R. (2024). Tungsten disulfide based wearable antenna in terahertz band for sixth generation applications. Telecommunication Computing Electronics and Control, 22(2), 545–555. (Scopus, cited)

Lia, L., Zishan, M. S. R., Eshan, S. H., & Hasan, R. R. (2024). Graphene based terahertz patch antenna for breast tumor detection. Telecommunication Computing Electronics and Control, 22(5), 1073–1082. (Scopus, cited)