Miodrag Spalevic | Numerical Analysis | Best Researcher Award

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Prof. Dr. Miodrag Spalevic | Numerical Analysis | Best Researcher Award

Professor | University of Belgrade | Serbia

Prof. Dr. Miodrag Spalevic is a distinguished full professor in the Department of Mathematics at the Faculty of Mechanical Engineering, University of Belgrade, with additional teaching at the Mathematical Grammar School in Belgrade. He completed his PhD studies in mathematics at the universities of Kragujevac and Montenegro and serves as an external member of the University of Athens for faculty selection in numerical analysis, approximation theory, and numerical integration. Prof. Dr. Miodrag Spalevic has made significant contributions as editor and referee for numerous international journals and is actively involved in shaping research in applied and numerical analysis, scientific computing, and quadrature theory, particularly focusing on Gaussian-type quadrature formulas and orthogonal polynomials. He has authored over a hundred papers and several influential books and chapters, mentoring numerous MSc and PhD theses. His research explores advanced quadrature rules, error bounds, and numerical algorithms, emphasizing practical computation for analytic functions and Fourier coefficients. Prof. Dr. Miodrag Spalevic has participated in and contributed to a wide array of international conferences, delivering invited lectures, plenary talks, and keynote presentations, while also organizing specialized minisymposia. His work bridges rigorous theoretical development with practical computational methods, making him a leading figure in approximation, quadrature, and scientific computing. He has 88 documents with 723 citations, an h-index of 14, and is widely recognized for his editorial and academic leadership across global mathematical communities.

Profile: Scopus | Orcid | Researchgate 

Featured Publications

Milovanović, G. V., & Spalević, M. M. (1998). Construction of Chakalov–Popoviciu’s type quadrature formulae. Rend. Circ. Mat. Palermo, 52, 625–636.

Spalević, M. M. (1999). Bessel’s inequality in terms of a basis of Vk. Acta Sci. Math. (Szeged), 65, 169–177.

Spalević, M. M. (1999). Product of Turán quadratures for cube, simplex, surface of the sphere, Ern, Er2n. Journal of Computational and Applied Mathematics, 106, 99–115.

Spalević, M. M. (2002). Quadrature formulae of Radau and Lobatto type connected to s-orthogonal polynomials. Zh. Vychisl. Mat. Mat. Fiz., 42, 615–626.

Spalević, M. M. (2001). Remainder term in Chakalov–Popoviciu quadratures of Radau and Lobatto type and influence function. Publ. Inst. Math. (Beograd) (N.S.), 70(84), 79–93.

Abdulrazak Otaru | Modelling and Simulation | Best Researcher Award

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Dr Abdulrazak Otaru | Modelling and Simulation | Best Researcher Award

Assistant Professor, King Faisal University, Saudi Arabia ✨
Dr. Abdulrazak Jinadu Otaru is an Assistant Professor in the Department of Chemical Engineering at King Faisal University, Al Ahsa, Saudi Arabia. With extensive teaching experience, he has held academic roles at King Faisal University and the Federal University of Technology Minna, Nigeria. His expertise spans Chemical Engineering Thermodynamics, Numerical Methods, and Computational Fluid Dynamics (CFD). Dr. Otaru’s research focuses on machine learning applications in modeling thermal degradation and porous material systems.

Profile

Orcid

Education 🎓

Dr. Otaru holds a PhD in Chemical Engineering, specializing in Modelling and Simulation. His education has equipped him with advanced knowledge in CFD, machine learning, and material sciences, allowing him to contribute significantly to his field.

Experience đź’Ľ

Dr. Otaru has served as an academic in both undergraduate and postgraduate settings. At King Faisal University, he teaches courses such as Chemical Engineering Thermodynamics, Numerical Methods, and Engineering Computing. Previously, at the Federal University of Technology Minna, he taught subjects like Transport Phenomena, Chemical Reaction Engineering, and Chemical Engineering Design.

Research Interests 🔬

Dr. Otaru’s research revolves around Chemical Engineering, focusing on Computational Fluid Dynamics (CFD), machine learning for material modeling, carbon capture technologies, and the thermo-kinetic analysis of bio-composites. His ongoing projects include studies on the thermal degradation of palm fronds, polyolefin bio-composites, and various zeolite synthesis processes.

Awards 🏆

Dr. Otaru has earned recognition for his contributions to chemical engineering and material sciences, particularly for his innovative use of machine learning techniques in chemical process modeling. His work in computational techniques for sustainable material processing has been highly regarded in both academic and industrial circles.

Publications đź“š

Dr. Otaru has published numerous research papers in renowned journals. His recent works include:

Thermal Decomposition of Date Seed/Polypropylene Homopolymer: Machine Learning CDNN, Kinetics, and Thermodynamics published in Polymers, MDPI on 23rd January 2025. Link to the article.

Kinetics Study of the Thermal Decomposition of Date Seed Powder/HDPE Plastic Blends published in Bioresource Technology Reports, Elsevier on 13th January 2025. Link to the article.

Conclusion

Dr. Otaru stands out as a highly deserving candidate for the Best Researcher Award. His exceptional research output, interdisciplinary work, and continuous contributions to the scientific community make him a key figure in the field of Chemical Engineering. With a proven record of academic excellence and an innovative approach to solving complex engineering problems, Dr. Otaru exemplifies the qualities of a distinguished researcher.

 

Amin Mahdavi-Meymand | Simulation | Best Researcher Award

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Mr Amin Mahdavi-Meymand | Simulation | Best Researcher Award

Specialist, institute of hydro engineering polish academy of science, Poland ✨

Amin Mahdavi-Meymand, MSc, is a dedicated specialist in hydro-engineering and structural dynamics at the Institute of Hydro-Engineering of the Polish Academy of Sciences. With a strong academic and professional background in water structures and wave mechanics, he focuses on innovative solutions to complex engineering challenges. He is fluent in Persian and English and actively contributes to cutting-edge research in his field.

Profile

Scopus

Education 🎓

Amin Mahdavi-Meymand is currently pursuing his Ph.D. in Civil Engineering, Geodesy, and Transport at the Tri-city Doctoral School of the Polish Academy of Sciences under the supervision of Wojciech Sulisz. He holds an M.S. in Agriculture Engineering specializing in Water Structures from Shahid Chamran University of Ahwaz, where he achieved a GPA of 3.47. His master’s dissertation focused on Fuzzy and Neural-Fuzzy Modeling of Aerator Structure Geometric and Chute Hydraulic in the Discharge of Air Flow, supervised by Dr. Javad Ahadian. He also earned a B.A. in Agriculture Engineering with a focus on Water from Shahid Bahonar University of Kerman, achieving a GPA of 3.18 under the guidance of Dr. Mohammad Zounemat-Kermani.

Experience 🏗️

Amin has been actively engaged in research and development in wave mechanics and structural dynamics. His work combines theoretical insights and practical applications, addressing challenges such as sediment transport, hydraulic modeling, and dam water-level variations.

Research Interests 🔬

Amin Mahdavi-Meymand’s research interests span innovative applications of artificial intelligence in water engineering, focusing on enhancing predictive and analytical models. He specializes in neural-fuzzy modeling to optimize hydraulic systems, providing deeper insights into complex water flow behaviors. His work also delves into scour depth prediction and sediment transport, critical for maintaining structural integrity in aquatic environments. Additionally, Amin explores the use of meta-heuristic algorithms in hydro-structural simulation, aiming to improve the efficiency and accuracy of water resource management solutions.

Awards 🏆

Amin has been recognized for his innovative approaches to water structure engineering and his contributions to the development of advanced numerical models in hydro-dynamics.

Publications Top Notes đź“š

Depth Prediction in Sand Beds using Artificial Neural Networks and ANFIS Methods (Indian Journal of Science and Technology, 2015) Link

Numerical Modeling of Scour Depth at Side Piers of the Bridge (Computational and Applied Mathematics, 2015) Link

Estimating Incipient Motion Velocity of Bed Sediments using Different Data-driven Methods (Applied Soft Computing, 2018) Link

Hybrid Meta-heuristics Artificial Intelligence Models in Simulating Discharge Passing the Piano Key Weirs (Journal of Hydrology, 2018) Link

Evaluating Performance of Meta-Heuristic Algorithms and Decision Tree Models in Simulating Water Level Variations of Dams’ Piezometers (Journal of Hydraulic Structures, 2018) Link

Conclusion

Amin Mahdavi-Meymand is a strong contender for the Best Researcher Award due to his innovative contributions to water structure engineering, advanced modeling techniques, and academic dedication. With a focus on expanding his research scope and enhancing his global presence, he is poised to become a leading figure in hydraulic and civil engineering. His potential and achievements make him a noteworthy candidate for the award. 🌟

Hyeryung Jang | Machine Learning | Best Researcher Award

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Assist. Prof. Dr Hyeryung Jang | Machine Learning | Best Researcher Award

Assistant Professor, Dongguk University, South Korea 🧑‍🏫

Hyeryung Jang is an Assistant Professor at the Division of AI Software Convergence at Dongguk University, Seoul, South Korea. His research interests lie at the intersection of communication systems, probabilistic graphical models, and networked machine learning. He has contributed significantly to the development of algorithms for large-scale communication networks, with applications in healthcare, manufacturing, and beyond. He has held academic and research positions at prestigious institutions, including King’s College London and KAIST.

Profile

Google Scholar

🎓 Education

Hyeryung Jang earned his Ph.D. in Electrical Engineering from the Korea Advanced Institute of Science and Technology (KAIST), South Korea, from March 2012 to February 2017. His doctoral thesis, titled Optimization and Learning of Graphical Models: A Stochastic Approximation Approach, was supervised by Prof. Yung Yi and co-advised by Prof. Jinwoo Shin. He also holds a Master’s degree in Electrical Engineering from KAIST, completed between March 2010 and February 2012, with a thesis on the Economic Benefits of ISP-CDN and ISP-ISP Cooperation, under the guidance of Prof. Yung Yi. Hyeryung Jang completed his Bachelor’s degree in Electrical Engineering at KAIST in February 2010.

đź’Ľ Experience

Hyeryung Jang currently serves as an Assistant Professor in the Division of AI Software Convergence at Dongguk University, where he has been leading the Intelligence and Optimization in Networks (ION) lab since March 2021. Before this, he was a Research Associate at King’s College London, in the Centre for Telecommunications Research, Department of Engineering, from March 2018 to February 2021. His post-doctoral research was conducted at KAIST from March 2017 to February 2018. Hyeryung also gained valuable experience as a Research Intern at Los Alamos National Laboratory in the USA during the summer of 2015.

🔬 Research Interests

Hyeryung Jang’s research interests are centered on mathematical modeling and communication systems, with a particular emphasis on networked machine learning. He explores innovative learning algorithms for probabilistic graphical models, deep learning, and reinforcement learning. His work aims to improve the stability and representation quality of generative models such as GANs, VAEs, and diffusion models. Jang is also focused on the learning and inference of graphical models, specifically for applications like robust recommendation systems and communication-efficient algorithms. Moreover, his research delves into efficient learning methods to address noisy data and real-world challenges in fields like healthcare, highlighting his broad interdisciplinary approach to solving complex problems in communication networks.

🏆 Awards

Hyeryung Jang has received recognition for his groundbreaking work in networked machine learning, contributing to innovative applications in healthcare and telecommunications. His research has been published in top-tier journals such as IEEE Transactions on Communications, IEEE Transactions on Neural Networks and Learning Systems, and Journal of Medical Internet Research (JMIR).

đź“š Publications Top Notes

LinkFND: Simple Framework for False Negative Detection in Recommendation Tasks with Graph Contrastive Learning, IEEE Access, Dec. 2023.

In-Home Smartphone-based Prediction of Obstructive Sleep Apnea in Conjunction with Level 2 Home Polysomnography, JAMA Otolaryngology-Head & Neck Surgery, Nov. 2023.

Prediction of Sleep Stages via Deep Learning using Smartphone Audio Recordings in Home Environments, Journal of Medical Internet Research, June 2023.

Real-time Detection of Sleep Apnea based on Breathing Sounds and Prediction Reinforcement using Home Noises, Journal of Medical Internet Research, Feb. 2023.

Conclusion

Given his strong academic credentials, innovative contributions, and high-impact research, Hyeryung Jang is undoubtedly a strong contender for the Best Researcher Award. His work not only advances theoretical knowledge but also drives practical applications that address critical real-world challenges, particularly in communication systems and healthcare. Jang’s passion for interdisciplinary research and teaching further solidifies his suitability for this prestigious recognition.

Sarah Di Grande | Analytics | Best Researcher Award

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Ms. Sarah Di Grande | Analytics | Best Researcher Award

PhD student, University of Catania, Italy

Sarah Di Grande is a driven researcher and data scientist currently pursuing a PhD in Systems, Energy, Computer, and Telecommunications Engineering at the University of Catania, Italy. With expertise in machine learning and a focus on sustainable water-energy optimization, she has contributed extensively to data science applications in renewable energy and smart city initiatives.

Profile

Orcid

Education 🎓

Sarah completed a Master’s in Data Science for Management at the University of Catania in 2022, graduating summa cum laude with a thesis on unsupervised machine learning for photovoltaic systems. She also holds a Bachelor’s degree in Business Economics from the same institution and graduated from Liceo Megara with top honors in 2017. Her studies have centered on advanced machine learning, big data, and data security.

Experience đź’Ľ

Currently, Sarah is a PhD student and researcher at the University of Catania, working in collaboration with Darwin Technologies on machine learning-based water-energy optimization. She previously interned as a data scientist at BaxEnergy, where she applied predictive maintenance techniques for photovoltaic panels, gaining hands-on experience in industrial data science applications.

Research Interests 🔬

Her research is dedicated to leveraging artificial intelligence for sustainable energy systems, focusing on machine learning applications in hydropower forecasting, urban traffic prediction, and water distribution network optimization. Sarah’s work aims to enhance resource management and promote sustainability in smart cities.

Awards 🏆

Sarah has received recognition for her innovative contributions, winning the Start-Cup Sicilia 2023 for her work on the “Smart Knee Project,” a device aimed at diagnosing knee osteoarthritis. She also secured second place in the University of Catania’s Start-Cup competition for the same project.

Publications Top Notesđź“š

Sarah has contributed numerous papers to international conferences and journals, exploring AI in hydropower, water distribution, and urban traffic management. Some key publications include:

“A Proactive Approach for the Sustainable Management of Water Distribution Systems” (2023) in 12th International Conference on Data Science, Technology and Applications – DATA [cited by 10 articles].

“Detection and Prediction of Leakages in Water Distribution Networks” (2023) in DATA 2023 [cited by 7 articles]

“A Machine Learning Approach for Hydroelectric Power Forecasting” (2023) in 14th International Renewable Energy Congress – IREC [cited by 5 articles].

“Data Science for the Promotion of Sustainability in Smart Water Distribution Systems” (2024) in Communications in Computer and Information Science, Springer [cited by 12 articles].