Husniddin Khayrullayevn | Mathematics | Best Researcher Award

Published on 03/06/202503/06/2025 by NESIN Awards

Dr. Husniddin Khayrullayevn | Mathematics | Best Researcher Award

Husniddin at University of Miskolc, Hungary

Husniddin Khayrullaev is a promising early-career researcher currently pursuing a PhD at the University of Miskolc, specializing in numerical methods for solving complex differential equations. He has published several peer-reviewed articles in reputable journals, focusing on positivity-preserving and dynamically consistent methods for Fisher’s and heat equations. His strong technical background in finite element and finite difference methods, supported by a solid educational foundation in electrical and computer engineering, underlines his research capabilities. Despite limited professional experience and the need for improved academic communication and presentation skills, his dedication to research and growing publication record reflect significant potential. Enhancing his international collaborations, refining his CV, and increasing the visibility and impact of his work would strengthen his candidacy. While he may not yet be fully competitive for a Best Researcher Award, he is well-suited for emerging researcher recognition and is on a clear trajectory toward becoming a strong contender in the future.

Professional Profile

Education🎓

Husniddin Khayrullaev has a solid educational background in electrical engineering and computational science. He is currently pursuing a PhD at the University of Miskolc in Hungary, focusing on advanced numerical methods and their applications in solving partial differential equations. Prior to this, he completed his master’s degree in Electric Mechanics at the Bukhara Engineering-Technological Institute from 2018 to 2020, where he deepened his understanding of electromechanical systems. His undergraduate studies, completed between 2014 and 2018 at the same institute, were in Electrical Engineering, Electromechanics, and Electrical Technologies, laying the groundwork for his technical and analytical skills. Additionally, he holds a Technician Diploma in Computer Systems Service Informatics from the Industrial Vocational College in Peshku, which he earned between 2011 and 2014. This progression highlights a continuous and focused academic journey, combining theoretical and practical expertise, and leading to his current specialization in computational modeling and numerical analysis.

Professional Experience📝

Husniddin Khayrullaev has gained valuable professional experience that complements his academic background. From January 2021 to September 2022, he worked as an IT assistant at the Bukhara Institute of Natural Resources Management, part of the National Research University TIIAME. In this role, he supported academic and technical operations, contributing to research activities and data management, which enhanced his technical proficiency and organizational skills. Prior to that, from November 2020 to January 2021, he worked as an electrician in the Bukhara cotton textile industry. This hands-on experience provided him with practical knowledge in electrical systems and maintenance, strengthening his problem-solving skills and understanding of real-world engineering applications. Though his early professional roles were not exclusively research-focused, they helped build a strong foundation in technical and quality control processes. These experiences have equipped him with a combination of practical and analytical skills that support his ongoing research in computational and numerical methods.

Research Interest🔎

Husniddin Khayrullaev’s research interests lie in the field of computational mathematics, particularly in the development and analysis of numerical methods for solving partial differential equations (PDEs). He focuses on explicit, positivity-preserving, and dynamically consistent numerical schemes for equations such as Fisher’s equation, the heat equation, and diffusion equations. His work aims to improve the stability, accuracy, and physical consistency of numerical simulations used in engineering and scientific modeling. Husniddin is especially interested in finite element and finite difference methods and their applications to problems involving time- and space-dependent diffusion coefficients. His research addresses critical challenges in ensuring numerical methods maintain essential properties like positivity and conservation, which are vital for realistic physical simulations. By advancing these techniques, he contributes to improving computational tools used in areas such as thermal analysis, fluid dynamics, and material science. His interests are grounded in both theoretical development and practical implementation of numerical algorithms.

Award and Honor🏆

As an emerging researcher, Husniddin Khayrullaev is in the early stages of his academic career and is steadily building a foundation for future recognition. While he has not yet received major international awards or honors, his recent accomplishments reflect a growing presence in the research community. His scholarly contributions, including multiple peer-reviewed publications in reputable journals such as Computation, Multidiszciplináris Tudományok, and IJANSER, demonstrate his dedication to advancing numerical methods in applied mathematics. Being accepted as a PhD candidate at the University of Miskolc and successfully publishing as a lead author at this stage of his academic journey is itself a commendable achievement. These accomplishments signal strong potential for future honors and awards as his research impact grows. His ongoing commitment to high-quality research and his contributions to computational science position him as a strong candidate for early-career or emerging researcher awards in the near future.

Research Skill🔬

Husniddin Khayrullaev possesses a strong set of research skills, particularly in the areas of numerical analysis and computational modeling. His expertise includes the development and implementation of finite element and finite difference methods, which he applies to solve complex partial differential equations such as the heat equation, Fisher’s equation, and diffusion models. He is skilled in analyzing the stability, consistency, and positivity-preserving properties of numerical schemes—an essential aspect of ensuring accurate and reliable simulations in scientific computing. Husniddin demonstrates proficiency in mathematical modeling, algorithm design, and scientific programming, allowing him to effectively translate theoretical concepts into practical computational tools. Additionally, he has experience in academic writing and publishing, with several research articles accepted in peer-reviewed journals. His ability to interpret mathematical problems, design numerical solutions, and evaluate their performance reflects a deep understanding of applied mathematics. These research skills form the foundation of his contributions to the field of computational science.

Conclusion💡

Husniddin Khayrullaev shows promising potential as a researcher, with a clear focus on numerical methods and applied mathematics. His publication record as a PhD student is commendable and reflects a solid foundation in computational science.

However, to be fully competitive for a Best Researcher Award, especially in broader or international settings, he would benefit from:

Sharpening the presentation and clarity of his academic profile.
Expanding research collaborations.
Demonstrating greater research impact and professional development.

Verdict:
Conditionally suitable. His current trajectory is impressive for an early-career researcher, and with continued progress and refinement, he could be a strong candidate in the near future. For this cycle, he may be better suited for an Emerging Researcher Award or similar recognition.

Publications Top Noted✍

Title: Comprehensive investigation of the explicit, positivity preserving methods for the heat equation: Part 1
Authors: K. Husniddin, K. Endre
Year: 2024
Citations: 6
Title: Interpolated spline method for a thermal distribution of a pipe with a turbulent heat flow
Authors: A. Hazim, A.A. Habeeb, J. Károly, K. Endre
Year: 2021
Citations: 5
Title: A kis létszámban átmentett cikta juh származási adatainak értékelése különös tekintettel a családokra
Authors: P. János, K. Endre, T. Károly, S. László, B.P. Ágnes, G. András
Year: 2019
Citations: 5
Title: Doroszló hiedelemvilága
Authors: K. Endre, J. Károly
Year: 1982
Citations: 5
Title: Testing and improving a non-conventional unconditionally positive finite difference method
Authors: M. Saleh, K. Endre, P. Gábor
Year: 2020
Citations: 3
Title: A cikta juh jellemzése a mitokondriális DNS kontrollrégiója alapján
Authors: K. Endre, M.A. Ákos, H. Levente, A. Kata, Z. Petra, T. Károly, S. László, …
Year: 2020
Citations: 3
Title: Multi objective optimization for house roof using artificial neural network model
Authors: A.A. Habeeb, K. Endre, B. Betti
Year: 2023
Citations: 2
Title: Construction and investigation of new numerical algorithms for the heat equation: Part III
Authors: S. Mahmoud, N. Ádám, K. Endre
Year: 2020
Citations: 1
Title: Characterisation of Hungarian Cikta sheep based on the control region of mtDNA
Authors: K. Endre, M.A. Akos, H. Levente, A. Kata, Z. Petra, T. Karolyn, S. Laszlo, …
Year: 2020
Citations: 1

Mohammad Shifat-E-Rabbi | Mathematical Modeling | Best Researcher Award

Published on 25/02/202525/02/2025 by NESIN Awards

Dr. Mohammad Shifat-E-Rabbi | Mathematical Modeling | Best Researcher Award

Assistant Professor at North South University, Bangladesh

Dr. Mohammad Shifat-E-Rabbi is an Assistant Professor in the Department of Electrical and Computer Engineering at North South University, Bangladesh. He earned his Ph.D. in Biomedical Engineering from the University of Virginia, where his dissertation, “Transport Generative Models in Pattern Analysis and Recognition,” focused on developing mathematical and computational frameworks for artificial intelligence and machine learning. Dr. Shifat-E-Rabbi’s research interests include applied mathematics, machine learning, image informatics, computational biology, and pattern recognition. He has contributed to various publications, such as “End-to-End Signal Classification in Signed Cumulative Distribution Transform Space” in IEEE Transactions on Pattern Analysis and Machine Intelligence. At North South University, he teaches courses in Artificial Intelligence, Machine Learning, and programming languages. His academic journey began with a B.Sc. in Electrical and Electronic Engineering from the Bangladesh University of Engineering and Technology.

Professional Profile

Google Scholar
Scopus Profile
ORCID Profile

Education

Dr. Mohammad Shifat-E-Rabbi’s educational journey began at Rangpur Zilla School and Rangpur Cadet College in Bangladesh. He earned his B.Sc. in Electrical and Electronic Engineering from the Bangladesh University of Engineering and Technology (BUET) in 2015. He then pursued his Ph.D. in Biomedical Engineering at the University of Virginia (UVa), USA, focusing on Pattern Analysis and Recognition within the Imaging and Data Science Laboratory. His dissertation, titled “Transport Generative Models in Pattern Analysis and Recognition,” centered on developing mathematical and computational frameworks for artificial intelligence and machine learning. During his doctoral studies, Dr. Shifat-E-Rabbi served as a research assistant under the supervision of Prof. Gustavo Rohde.

Professional Experience

Dr. Mohammad Shifat-E-Rabbi is an Assistant Professor in the Department of Electrical and Computer Engineering at North South University, Bangladesh. He earned his Ph.D. in Biomedical Engineering from the University of Virginia, USA, where he specialized in Pattern Analysis and Recognition within the Imaging and Data Science Laboratory. During his doctoral studies, Dr. Shifat-E-Rabbi served as a research assistant under the supervision of Prof. Gustavo Rohde. Prior to his Ph.D., he completed his B.Sc. in Electrical and Electronic Engineering at the Bangladesh University of Engineering and Technology (BUET) in 2015. At BUET, he was involved in the Digital Signal Processing research lab. Dr. Shifat-E-Rabbi’s research interests encompass applied mathematics, machine learning, image informatics, computational biology, and pattern recognition. In his current role, he teaches courses in Artificial Intelligence, Machine Learning, and programming languages. His academic journey began at Rangpur Zilla School and Rangpur Cadet College in Bangladesh.

Research Interest

Dr. Mohammad Shifat-E-Rabbi’s research interests encompass applied mathematics, machine learning, image informatics, computational biology, and pattern recognition. He has contributed to the development of the Radon Signed Cumulative Distribution Transform (R-CDT) and its applications in classifying signed images. Additionally, he has worked on predictive modeling of hematoma expansion in intracerebral hemorrhage patients and the real-time intelligent classification of COVID-19 and thrombosis through massive image-based analysis of platelet aggregates. Dr. Shifat-E-Rabbi has also explored transport-based morphometry for analyzing nuclear structures in digital pathology images across various cancers. His work aims to bridge theoretical advancements with practical applications, enhancing the understanding and analysis of complex biological and medical data.

Award and Honor

Dr. Mohammad Shifat-E-Rabbi has been recognized for his significant contributions to the fields of artificial intelligence and machine learning. His collaborative research on “End-to-End Signal Classification in Signed Cumulative Distribution Transform Space” was published in the prestigious IEEE Transactions on Pattern Analysis and Machine Intelligence. This work, conducted alongside colleagues from the University of Virginia, received support from esteemed institutions such as the National Institutes of Health and the Office of Naval Research, underscoring its impact and importance.

Research Skill

Dr. Mohammad Shifat-E-Rabbi possesses a robust set of research skills that bridge applied mathematics, machine learning, and computational biology. His expertise includes developing mathematical models and computational frameworks, notably in pattern recognition and image informatics. Dr. Shifat-E-Rabbi has contributed to the advancement of the Radon Cumulative Distribution Transform (R-CDT), enhancing image classification techniques. His collaborative work on “End-to-End Signal Classification in Signed Cumulative Distribution Transform Space” exemplifies his ability to integrate theoretical concepts with practical applications, leading to more efficient signal classification methods. His research portfolio demonstrates proficiency in handling complex datasets, developing innovative algorithms, and applying interdisciplinary approaches to solve real-world problems. Dr. Shifat-E-Rabbi’s commitment to advancing artificial intelligence and machine learning is evident through his scholarly publications and ongoing projects.

Conclusion

If the researcher has made significant contributions through innovation, publications, and demonstrated impact, they would be a strong candidate for the Best Researcher Award. However, if the research is still in its early stages or lacks broader validation, additional work on practical applications, benchmarking, and interdisciplinary collaborations could further strengthen their case.

Publications Top Noted

Massive image-based single-cell profiling reveals high levels of circulating platelet aggregates in patients with COVID-19
- Authors: M. Nishikawa, H. Kanno, Y. Zhou, T.H. Xiao, T. Suzuki, Y. Ibayashi, J. Harmon, M. Shifat-E-Rabbi, et al.
- Published in: Nature Communications
- Year: 2021
- Citations: 71
Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning
- Authors: S. Kundu, B.G. Ashinsky, M. Bouhrara, E.B. Dam, S. Demehri, M. Shifat-E-Rabbi, et al.
- Published in: Proceedings of the National Academy of Sciences
- Year: 2020
- Citations: 57
Cell image classification: a comparative overview
- Authors: M. Shifat-E-Rabbi, X. Yin, C.E. Fitzgerald, G.K. Rohde
- Published in: Cytometry Part A
- Year: 2020
- Citations: 39
Radon cumulative distribution transform subspace modeling for image classification
- Authors: M. Shifat-E-Rabbi, X. Yin, A.H.M. Rubaiyat, S. Li, S. Kolouri, A. Aldroubi, G.K. Rohde
- Published in: Journal of Mathematical Imaging and Vision
- Year: 2021
- Citations: 28
PREHEAT: Precision heart rate monitoring from intense motion artifact corrupted PPG signals using constrained RLS and wavelets
- Authors: M.S. Islam, M. Shifat-E-Rabbi, A.M.A. Dobaie, M.K. Hasan
- Published in: Biomedical Signal Processing and Control
- Year: 2017
- Citations: 26
Blind Deconvolution of Ultrasound Images Using $ℓp\ell_p$ -Norm-Constrained Block-Based Damped Variable Step-Size Multichannel LMS Algorithm
- Authors: M.K. Hasan, M. Shifat-E-Rabbi, S.Y. Lee
- Published in: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
- Year: 2016
- Citations: 12
Local sliced Wasserstein feature sets for illumination invariant face recognition
- Authors: Y. Zhuang, S. Li, M. Shifat-E-Rabbi, X. Yin, A.H.M. Rubaiyat, G.K. Rohde
- Published in: Pattern Recognition
- Year: 2025
- Citations: 10
End-to-end signal classification in signed cumulative distribution transform space
- Authors: A.H.M. Rubaiyat, S. Li, X. Yin, M. Shifat-E-Rabbi, Y. Zhuang, G.K. Rohde
- Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence
- Year: 2024
- Citations: 9
Nearest Subspace Search in The Signed Cumulative Distribution Transform Space for 1D Signal Classification
- Authors: A.H.M. Rubaiyat, M. Shifat-E-Rabbi, Y. Zhuang, S. Li, G.K. Rohde
- Published in: IEEE International Conference on Acoustics, Speech and Signal Processing
- Year: 2022
- Citations: 9
Speckle tracking and speckle content based composite strain imaging for solid and fluid filled lesions
- Authors: M. Shifat-E-Rabbi, M.K. Hasan
- Published in: Ultrasonics
- Year: 2017
- Citations: 9