Shougui Zhang | Computational mathematics | Best Researcher Award

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Prof. Dr. Shougui Zhang | Computational mathematics | Best Researcher Award

Teacher| Chongqing Normal Univercity | China

Dr. Xin Li is an accomplished scholar and educator specializing in clinical medicine and sports health sciences, currently serving as an Associate Professor at Tianjin Normal University since 2006. She obtained her Doctor of Medicine (MD) in Clinical Medicine from Tianjin Medical University in July 2006. Over nearly two decades of academic and research experience, Dr. Li has established herself as a leading expert at the intersection of clinical medicine, exercise science, and health management. Her teaching portfolio encompasses a range of courses in sports health, exercise rehabilitation, and health management, with a focus on integrating clinical case studies into theoretical instruction to enhance students’ practical competencies and professional insight. Dr. Li’s research primarily addresses exercise interventions for chronic diseases, sports injury prevention, and rehabilitation science, contributing to the growing evidence base linking physical activity with disease prevention and functional recovery. She has led and participated in numerous provincial and municipal research projects, achieving notable outcomes that have advanced the application of medical principles in sports health practice. To date, Dr. Li has published over ten academic papers in peer-reviewed core journals and international conferences, several of which have been widely cited and recognized within the academic community for their methodological rigor and clinical relevance. Her collaborative approach bridges disciplines such as physiology, public health, and kinesiology, fostering innovation and interdisciplinary integration in sports medicine research. Beyond academia, Dr. Li’s work holds significant societal impact, promoting the scientific development of exercise-based health strategies for chronic disease management and population well-being. Combining solid clinical expertise, pedagogical excellence, and a strong research record, Dr. Xin Li continues to contribute meaningfully to the advancement of sports health education and evidence-based medical practice in China and beyond.

Profile: Scopus | ORCID

Featured Publications

Zhang, S. (2025). A self-adaptive alternating direction multiplier method for variational inequality in two domains. Applied Mathematics and Mechanics.

Zhang, S., & Coauthors. (2025). Analysis of a Crank–Nicolson fast element-free Galerkin method for the nonlinear complex Ginzburg–Landau equation. Journal of Computational and Applied Mathematics.

Zhang, S. (2024). Self-adaptive alternating direction method of multiplier for a fourth order variational inequality. Journal of Inequalities and Applications.

Professor Shougui Zhang’s research advances the development of efficient computational methods for complex variational inequalities and partial differential equations, strengthening the mathematical foundation for modern engineering, physics, and optimization problems. His work enhances scientific computing capabilities, supporting innovation in technology, modeling, and data-driven decision-making across academic and industrial domains worldwide.

Abdelhadi Abta | Mathematics | Best Researcher Award

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Dr. Abdelhadi Abta | Mathematics | Best Researcher Award 

Professor | Cadi Ayyad University | Morocco

Prof. Abdelhadi Abta is a distinguished Moroccan mathematician and Professeur de l’Enseignement Supérieur at the Université Cadi Ayyad, Faculté Polydisciplinaire de Safi, where he has been serving since 2014 and obtained his habilitation in 2022. With a strong academic foundation in mathematics and applied sciences, he has built a notable career focusing on applied mathematics, dynamical systems, stability analysis, and optimal control, particularly in mathematical biology and epidemiology. His research interests include epidemic modeling, delayed differential equations, control theory, and their applications in public health challenges such as COVID-19 and tuberculosis, as well as in sociological and engineering systems. Over the years, Prof. Abta has developed advanced research skills in mathematical modeling, bifurcation analysis, nonlinear dynamics, and optimal control techniques, leading to more than 19 peer-reviewed publications in prestigious international journals indexed in IEEE, Springer, and Scopus. He has also actively contributed to the academic community by coordinating several national and international conferences and serving on scientific committees and editorial review boards, reflecting his leadership and service to the discipline. His role as coordinator of the “Sciences Mathématiques Générales et Applications” program at Université Cadi Ayyad demonstrates his dedication to curriculum development and student mentorship. Prof. Abta’s work has been recognized through numerous collaborations with international researchers, impactful contributions to epidemic control strategies, and his involvement in advancing interdisciplinary applications of mathematics. With his strong expertise, continuous scholarly output, and academic leadership, he stands out as a researcher committed to advancing both theoretical and applied mathematics with tangible societal impact. 239 Citations by 219 documents, 19 Documents, 5 h-index.

Profiles: Google Scholar | Scopus | ORCID 

Featured Publications

  1. Laarabi, H., Abta, A., & Hattaf, K. (2015). Optimal control of a delayed SIRS epidemic model with vaccination and treatment. Acta Biotheoretica, 63(2), 87–97. Cited by: 104

  2. Kaddar, A., Abta, A., & Talibi Alaoui, H. (2011). A comparison of delayed SIR and SEIR epidemic models. Nonlinear Analysis: Modelling and Control, 16(2), 181–190.
    Cited by: 100

  3. Abta, A., Kaddar, A., & Talibi Alaoui, H. (2012). Global stability for delay SIR and SEIR epidemic models with saturated incidence rates. Electronic Journal of Differential Equations, 2012(23), 1–13. Cited by: 93

  4. Laarabi, H., Abta, A., Rachik, M., & Bouyaghroumni, J. (2016). Stability analysis of a delayed rumor propagation model. Differential Equations and Dynamical Systems, 24(4), 407–415.Cited by: 40

  5. Abta, A., Laarabi, H., & Talibi Alaoui, H. (2014). The Hopf bifurcation analysis and optimal control of a delayed SIR epidemic model. International Journal of Analysis, 2014(1), Article 940819, 1–10. Cited by: 32

Ji Eun Kim | Analysis | Best Researcher Award

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Prof. Dr. Ji Eun Kim | Analysis | Best Researcher Award

Professor at Dongguk University, WISE | South Korea

Dr. Ji Eun Kim is a highly accomplished mathematician specializing in hypercomplex analysis, quaternionic and Clifford analysis, and advanced computational techniques such as dual and hyper-dual numbers and automatic differentiation. Her work seamlessly integrates theoretical mathematics with practical applications, including higher-order derivatives and applied mathematical modeling. As a faculty member at a leading Korean university, she contributes not only through research but also by mentoring students and fostering interdisciplinary collaborations. Her expertise, scholarly rigor, and dedication to advancing mathematical knowledge position her as a prominent figure in her field, with a growing impact both nationally and internationally. Beyond research, Dr. Kim engages in academic leadership, peer review, and editorial activities, reflecting a holistic commitment to the advancement of science and education. Her profile embodies the qualities of a researcher who combines intellectual depth with practical relevance, demonstrating leadership, innovation, and influence in the mathematical sciences.

Professional Profile

ORCID Profile 

Education

Dr. Kim earned her Ph.D. in Mathematics from Pusan National University, one of Korea’s top institutions for advanced studies in mathematics. Her doctoral work laid the foundation for her expertise in quaternionic and Clifford analysis, hypercomplex systems, and higher-order derivatives. During her studies, she developed strong skills in mathematical modeling, proof construction, and computational tools, equipping her to tackle complex research problems. Her education emphasized both theoretical rigor and practical application, preparing her for contributions to computational mathematics, automatic differentiation, and applied analysis. In addition to her doctoral training, she pursued postdoctoral research at the same institution, further deepening her knowledge and establishing herself as a productive scholar. Throughout her academic journey, Dr. Kim demonstrated exceptional aptitude for original research, critical thinking, and problem-solving, which continues to influence her teaching and research activities today. Her education reflects a consistent trajectory of excellence, intellectual curiosity, and commitment to advancing mathematical science.

Experience

Dr. Kim currently serves as an Assistant Professor in the Department of Mathematics at Dongguk University WISE, where she combines teaching, mentoring, and research leadership. Prior to this, she completed a postdoctoral research tenure, during which she refined her expertise in hypercomplex analysis, higher-order derivatives, and applied mathematics. Her academic career has been marked by active participation in research projects, development of advanced computational models, and contributions to scientific publications. She has guided students in both theoretical and computational mathematics, fostering critical thinking and analytical skills. Her faculty role also includes involvement in departmental administration, academic events, and interdisciplinary initiatives that bridge mathematics with engineering and applied sciences. Dr. Kim’s experience highlights a balance between rigorous research, education, and scholarly service, establishing her as a respected member of the academic community and a mentor to the next generation of mathematicians.

Research Interests

Dr. Kim’s research primarily focuses on quaternionic and Clifford analysis, dual and hyper-dual numbers, higher-order derivatives, and automatic differentiation, with an emphasis on bridging theoretical mathematics and applied computational solutions. She explores the applications of hypercomplex analysis in areas such as scientific computing, mathematical modeling, and engineering problems, providing practical relevance to abstract mathematical concepts. Her work in automatic differentiation and higher-order derivatives contributes to efficient computational methods for complex systems, benefiting both mathematics and applied sciences. Dr. Kim is particularly interested in developing new methodologies and frameworks that combine advanced algebraic structures with computational techniques. Her research also includes interdisciplinary collaboration, leveraging her mathematical expertise to solve problems in physics, engineering, and data analysis. This combination of theory, computation, and application positions her research at the cutting edge of modern mathematics, enabling impactful contributions to both academia and industry.

Awards and Honors

Throughout her academic and professional career, Dr. Kim has been recognized for excellence in research, teaching, and scholarly service. She has earned accolades for her publications in reputed journals and conferences, demonstrating significant impact in hypercomplex and computational mathematics. Her postdoctoral and faculty contributions have been acknowledged by peers for advancing theoretical and applied research. Beyond formal awards, her editorial and peer review roles reflect recognition of her expertise and integrity within the mathematical community. Dr. Kim’s leadership in mentoring students and fostering research collaborations further underscores her contributions to academia. While specific international awards are not listed, her growing influence and engagement in scholarly activities indicate a trajectory toward global recognition. These honors and achievements collectively highlight her commitment to advancing knowledge, promoting academic excellence, and shaping the next generation of researchers.

Research Skills

Dr. Kim possesses a comprehensive skill set combining advanced mathematical knowledge with computational proficiency. She excels in mathematical modeling, proof writing, and hypercomplex analysis, and is proficient in tools such as LaTeX, MATLAB, and Python for scientific computing. Her expertise in automatic differentiation and higher-order derivatives allows her to tackle complex problems in applied mathematics with precision. She is also adept at academic writing, peer review, and editorial correspondence, ensuring high-quality research output and contributing to the wider scholarly community. Her ability to integrate theory with computational practice enables her to address interdisciplinary challenges effectively. Additionally, Dr. Kim demonstrates strong mentorship, project management, and collaborative skills, facilitating research teams and guiding students in advanced mathematical techniques. This combination of analytical, technical, and leadership abilities makes her a highly effective researcher capable of producing impactful and innovative contributions.

Publication Top Notes

Title: A Hyper-Dual Number Approach to Higher-Order Derivative Computation
Authors: Ji Eun Kim
Year: 2025
Journal: Axioms

Title: Representation of Integral Formulas for the Extended Quaternions on Clifford Analysis
Authors: Ji Eun Kim
Year: 2025
Journal: Mathematics

Title: Hyperholomorphicity by Proposing the Corresponding Cauchy–Riemann Equation in the Extended Quaternion Field
Authors: Ji-Eun Kim
Year: 2024
Journal: Axioms

Conclusion

Dr. Ji Eun Kim is a highly deserving candidate for the Best Researcher Award. Her deep expertise in quaternionic and hypercomplex analysis, combined with practical applications in higher-order derivatives and automatic differentiation, reflects significant contributions to both theoretical and applied mathematics. Her academic trajectory, from Ph.D. to postdoctoral research and faculty leadership, demonstrates sustained excellence. With her ongoing research potential and growing influence in the mathematical sciences, Dr. Kim exemplifies the qualities of an award-winning researcher who advances knowledge and serves the scholarly community.

Aamir Saghir | Mathematics | Best Researcher Award

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Dr. Aamir Saghi | Mathematics | Best Researcher Award

Associate Professor at Mirpur University of Science and Technology, Pakistan

Dr. Aamir Saghir, an Associate Professor of Statistics at Mirpur University of Science and Technology (MUST), Pakistan, is a distinguished researcher in Statistical Quality Control, Data Analysis, and Probability Distributions. With a Ph.D. from Zhejiang University, China, and a post-doctoral fellowship from the University of Pannonia, Hungary, he has cultivated a strong international research presence. Dr. Saghir has authored over 60 research publications in reputed journals, including IEEE Access and Computers & Industrial Engineering, and co-authored a book with Wiley. He has successfully led funded research projects and supervised numerous M.Phil. and Ph.D. scholars. His leadership roles include department chairperson, treasurer, and chief librarian, reflecting a commitment to academic and administrative excellence. His future research focuses on integrating machine learning with statistical methods for anomaly detection. With proven academic contributions, strong mentorship, and impactful research, Dr. Saghir is a deserving candidate for the Best Researcher Award.

Professional Profile

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Aamir Saghir has a strong academic background in the field of statistics, marked by excellence and international exposure. He earned his Ph.D. in Statistics from Zhejiang University, China (2011–2014), where he specialized in the development of flexible and robust control charts for statistical process monitoring under the supervision of Professor Zhengyan Lin. Prior to that, he completed his M.Phil. in Statistics from Quaid-i-Azam University, Islamabad (2006–2008), focusing on Bayesian and classical approaches to process parameter monitoring. He also earned his M.Sc. in Statistics from the same university (2004–2006), where he graduated with first position in his session. His academic journey began with a bachelor’s degree in Mathematics and Statistics from the University of Azad Jammu and Kashmir (2001–2003). Dr. Saghir’s education is marked by academic distinction and research depth, providing a solid foundation for his successful career as a researcher and educator in statistical sciences.Professional

Experience

Dr. Aamir Saghir brings over 17 years of diverse academic and research experience in the field of statistics. He is currently serving as an Associate Professor in the Department of Statistics at Mirpur University of Science and Technology (MUST), where he has also held administrative roles such as Chairperson, Treasurer, and Chief Librarian. He began his teaching career as a Lecturer at the University of Azad Jammu and Kashmir in 2006 and later joined MUST, where he progressed through the ranks from Lecturer to Assistant Professor, and then to Associate Professor. He also completed a prestigious post-doctoral research fellowship at the University of Pannonia, Hungary, further enhancing his international exposure. Dr. Saghir has been actively involved in both undergraduate and postgraduate teaching, curriculum development, and research supervision. His professional journey reflects a strong commitment to academic excellence, research innovation, and institutional development within Pakistan and abroad.

Research Interest

Dr. Aamir Saghir’s research interests lie at the intersection of statistical theory and modern data-driven applications. His primary focus is on Statistical Quality Control, where he develops innovative control charts and monitoring schemes to improve process efficiency and reliability. He has extensively worked on probability models, particularly weighted and mixture distributions, contributing to the theoretical advancement of distribution theory. In recent years, Dr. Saghir has expanded his research into data science and machine learning, with a special emphasis on anomaly detection in industrial processes and high-dimensional time series analysis. His work bridges classical statistical techniques with emerging computational methods, making his research highly relevant to fields such as industrial engineering, environmental science, and cyber-physical systems. With a strong foundation in both theoretical and applied statistics, Dr. Saghir continues to explore robust and adaptive statistical methods that address real-world challenges in process monitoring, environmental modeling, and complex data analysis.

Award and Honor

Dr. Aamir Saghir has received several prestigious awards and honors throughout his academic career, reflecting his excellence in research and education. He secured first position in his M.Sc. Statistics program at Quaid-i-Azam University, highlighting his early academic distinction. He was awarded the highly competitive China Scholarship Council (CSC) scholarship for his Ph.D. studies at Zhejiang University, where he also received a Distinguished Certificate in Statistics in recognition of his outstanding doctoral work. Dr. Saghir is also an HEC-approved Ph.D. supervisor, a testament to his academic credibility and mentoring capabilities. He has contributed significantly to applied research through funded projects and has served on the Board of Studies for statistics departments at multiple universities. His research contributions have been recognized internationally, and he is actively involved in organizing academic conferences and supervising impactful survey studies, such as the socio-economic impact of telecommunications in Pakistan. These honors reflect his dedication to advancing statistical science

Research Skill

Dr. Aamir Saghir’s research interests are rooted in both theoretical and applied statistics, with a strong emphasis on Statistical Quality Control, Probability Distributions, and Data Analysis. He has developed numerous robust and adaptive control charts for process monitoring, particularly useful in industrial and manufacturing settings. His work on weighted and mixture probability distributions has contributed significantly to statistical modeling, offering improved methods for analyzing non-normal and skewed data. Recently, Dr. Saghir has broadened his research scope to include machine learning techniques for anomaly detection, reflecting a forward-thinking approach to modern data challenges. He is particularly interested in the integration of data science methods with high-dimensional time series analysis, which has important applications in environmental monitoring, healthcare, and IoT-based systems. Through his interdisciplinary approach, Dr. Saghir aims to develop statistical tools that are not only theoretically sound but also practically impactful across various scientific and engineering domains.

Publications Top Noted

  • Title: Phytoavailability of Cadmium (Cd) to Pak Choi (Brassica chinensis L.) Grown in Chinese Soils: A Model to Evaluate the Impact of Soil Cd Pollution on Potential Dietary Toxicity
    Authors: M.T. Rafiq, R. Aziz, X. Yang, W. Xiao, P.J. Stoffella, A. Saghir, M. Azam, T. Li
    Year: 2014
    Citations: 74

  • Title: Control Charts for Dispersed Count Data: An Overview
    Authors: A. Saghir, Z. Lin
    Year: 2015
    Citations: 60

  • Title: Weighted Distributions: A Brief Review, Perspective and Characterizations
    Authors: A. Saghir, G.G. Hamedani, S. Tazeem, A. Khadim
    Year: 2017
    Citations: 49

  • Title: Monitoring Process Variability Using Gini’s Mean Difference
    Authors: M. Riaz, A. Saghirr
    Year: 2007
    Citations: 47

  • Title: A Mean Deviation-Based Approach to Monitor Process Variability
    Authors: M. Riaz, A. Saghir
    Year: 2009
    Citations: 43

  • Title: A Control Chart for COM-Poisson Distribution Using a Modified EWMA Statistic
    Authors: M. Aslam, A. Saghir, L. Ahmad, C.H. Jun, J. Hussain
    Year: 2017
    Citations: 33

  • Title: Introduction to Statistical Process Control
    Authors: M. Aslam, A. Saghir, L. Ahmad
    Year: 2020
    Citations: 31

  • Title: A Flexible and Generalized Exponentially Weighted Moving Average Control Chart for Count Data
    Authors: A. Saghir, Z. Lin
    Year: 2014
    Citations: 31

  • Title: The Students’ Satisfaction in Higher Education and Its Important Factors: A Comparative Study Between Punjab and AJ&K, Pakistan
    Authors: S. Hussain, M. Jabbar, Z. Hussain, Z. Rehman, A. Saghir
    Year: 2014
    Citations: 30

  • Title: The Use of Probability Limits of COM–Poisson Charts and Their Applications
    Authors: A. Saghir, Z. Lin, S.A. Abbasi, S. Ahmad
    Year: 2013
    Citations: 30

  • Title: Monitoring Process Variation Using Modified EWMA
    Authors: A. Saghir, M. Aslam, A. Faraz, L. Ahmad, C. Heuchenne
    Year: 2020
    Citations: 28

Conclusion

Dr. Aamir Saghir is highly deserving of the Best Researcher Award, given his robust academic foundation, extensive publication record, and valuable contributions to applied statistical science. His research spans both theoretical advancements and real-world applications, and he has significantly contributed to knowledge transfer, mentorship, and institutional development. With ongoing interests in data science and anomaly detection, he holds great promise for continued leadership in research and innovation on both national and international platforms.

 

Husniddin Khayrullayevn | Mathematics | Best Researcher Award

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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

 

Mehrasa Ahmadipour | Information Theory | Best Researcher Award

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Dr. Mehrasa Ahmadipour | Information Theory | Best Researcher Award

Postdoc at UMPA, ens de lyon, France

Mehrasa Ahmadipour is a highly qualified candidate for the Best Researcher Award, with a Ph.D. in Information Theory from Institut Polytechnique de Paris and postdoctoral research at ENS Lyon in Sequential Statistics and Reinforcement Learning. Her expertise spans Multi-Armed Bandit Problems, ISAC, Neural Networks, and Physical Layer Security. She has contributed significantly as a guest editor, reviewer for IEEE journals, and session chair at IEEE ISIT 2023. With teaching experience in Information Theory, Cryptography, and Probability, she has also supervised master’s students. Additionally, she has held key roles in organizing academic conferences like CJC-MA 2024 and ISIT 2019. While her academic and research credentials are outstanding, strengthening her portfolio with more high-impact publications, citations, research funding, and industry collaborations would further enhance her profile. Overall, her research excellence, leadership, and contributions to the field make her a strong contender for the award.

Professional Profile 

Education🎓

Mehrasa Ahmadipour has a strong academic background in Electrical Engineering and Information Theory. She earned her Ph.D. from Institut Polytechnique de Paris (Télécom Paris) in 2022, specializing in Integrated Sensing and Communication (ISAC) under the supervision of Michele Wigger. Her doctoral research focused on an information-theoretic approach to ISAC, contributing to advancements in wireless communication and signal processing. Prior to that, she completed her M.Sc. in Electrical Engineering (Telecommunications Systems and Security) at the University of Tehran, where she worked on Physical Layer Authentication and Covert Communication in Wireless Networks. She earned her B.Sc. in Electrical Engineering from Iran University of Science and Technology (IUST), with a focus on Hyper Spectral Image Processing. Her academic journey began at the National Organization for Development of Exceptional Talents (NODET), where she specialized in Physics and Mathematics, ranking in the top 0.1% in university entrance exams, demonstrating exceptional academic excellence.

Professional Experience 📝

Mehrasa Ahmadipour has extensive professional experience in research and academia, focusing on Information Theory, Machine Learning, and Telecommunications. She is currently a Postdoctoral Researcher at École Normale Supérieure de Lyon, working on Sequential Statistics and Reinforcement Learning under the supervision of Aurélien Garivier. Her research explores advanced statistical methods and optimization techniques in decision-making processes. Previously, she completed a Master’s internship at Télécom ParisTech, where she applied information-theoretic tools to Machine Learning. Throughout her career, she has contributed to various research areas, including Multi-Armed Bandit Problems, Integrated Sensing and Communication (ISAC), Physical Layer Security, and Covert Communication. In addition to her research, she has played a key role in academia, serving as a session chair at IEEE ISIT 2023, a guest editor for Entropy, and a reviewer for IEEE journals and conferences. Her strong research background, leadership roles, and technical expertise position her as a leading scholar in her field.

Research Interest🔎

Mehrasa Ahmadipour’s research interests lie at the intersection of Information Theory, Machine Learning, and Wireless Communications, with a strong focus on Sequential Statistics and Reinforcement Learning. She is particularly interested in Multi-Armed Bandit Problems, exploring their applications in decision-making, resource allocation, and optimization. Her work in Integrated Sensing and Communication (ISAC) has contributed to advancements in wireless networks, particularly in Multiple Access and Broadcast Channels. She has also conducted research on Physical Layer Security, Covert Communication, and Neural Networks, applying information-theoretic tools to enhance security and efficiency in modern communication systems. Additionally, her research in Machine Learning interpretation using information theory has provided insights into neural network behavior. Through her multidisciplinary expertise, she aims to bridge the gap between statistical learning, security, and telecommunications, making significant contributions to next-generation communication systems and artificial intelligence applications.

Award and Honor🏆

Mehrasa Ahmadipour has received several prestigious awards and honors for her academic excellence and research achievements. She ranked in the top 0.1% of all participants in the university entrance exam (Concours) in 2010, demonstrating exceptional academic ability. Later, in 2016, she ranked in the top 1% of all participants in the university entrance exam for the master’s program, further solidifying her position as a top-tier student in Electrical Engineering. Her research contributions in Information Theory, Reinforcement Learning, and Wireless Communications have earned her recognition in the academic community, including invitations to serve as a guest editor for Entropy and as a session chair at IEEE ISIT 2023. Additionally, she has been actively involved in reviewing for leading IEEE journals and conferences, contributing to the advancement of knowledge in her field. Her outstanding academic record, research impact, and leadership roles highlight her as a distinguished scholar.

Research Skill🔬

Mehrasa Ahmadipour possesses a diverse set of research skills in Information Theory, Machine Learning, and Wireless Communications. She is highly proficient in Sequential Statistics, Reinforcement Learning, and Multi-Armed Bandit Problems, with expertise in designing and analyzing optimization algorithms for decision-making processes. Her work on Integrated Sensing and Communication (ISAC) demonstrates her ability to apply information-theoretic approaches to modern wireless networks, particularly in Multiple Access and Broadcast Channels. Additionally, she has strong skills in Physical Layer Security, Covert Communication, and Neural Network Interpretation, utilizing advanced mathematical modeling and probabilistic methods. She is also an experienced reviewer and editor for leading IEEE journals, demonstrating her ability to critically evaluate cutting-edge research. Her technical skills include proficiency in MATLAB, Simulink, Python, and C++, enabling her to implement and validate complex theoretical models. Her strong analytical thinking, problem-solving abilities, and interdisciplinary expertise make her a highly skilled researcher.

Conclusion💡

Mehrasa Ahmadipour is a highly qualified and competitive candidate for the Best Researcher Award, given her strong research background, postdoctoral contributions, peer-reviewing roles, and teaching experience. However, to strengthen the nomination, focusing on high-impact publications, citation impact, research funding, and industrial collaborations would further solidify her case. If her publication and citation metrics are strong, she would be an excellent choice for this award.

Publications Top Noted✍️

  • Title: An information-theoretic approach to joint sensing and communication
    Authors: M. Ahmadipour, M. Kobayashi, M. Wigger, G. Caire
    Year: 2022
    Citations: 109

  • Title: Joint sensing and communication over memoryless broadcast channels
    Authors: M. Ahmadipour, M. Wigger, M. Kobayashi
    Year: 2021
    Citations: 32

  • Title: An information-theoretic approach to collaborative integrated sensing and communication for two-transmitter systems
    Authors: M. Ahmadipour, M. Wigger
    Year: 2023
    Citations: 18

  • Title: Strong converses for memoryless bi-static ISAC
    Authors: M. Ahmadipour, M. Wigger, S. Shamai
    Year: 2023
    Citations: 13

  • Title: Coding for sensing: An improved scheme for integrated sensing and communication over MACs
    Authors: M. Ahmadipour, M. Wigger, M. Kobayashi
    Year: 2022
    Citations: 13

  • Title: Integrated communication and receiver sensing with security constraints on message and state
    Authors: M. Ahmadipour, M. Wigger, S. Shamai
    Year: 2023
    Citations: 11

  • Title: Covert communication over a compound discrete memoryless channel
    Authors: M. Ahmadipour, S. Salehkalaibar, M.H. Yassaee, V.Y.F. Tan
    Year: 2019
    Citations: 10

  • Title: State masking over a two-state compound channel
    Authors: S. Salehkalaibar, M.H. Yassaee, V.Y.F. Tan, M. Ahmadipour
    Year: 2021
    Citations: 3

  • Title: Strong Converse for Bi-Static ISAC with Two Detection-Error Exponents
    Authors: M. Ahmadipour, M. Wigger, S. Shamai
    Year: 2024
    Citations: 2

Sat Gupta | Mathematics | Best Researcher Award

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Dr. Sat Gupta | Mathematics | Best Researcher Award

Professor at UNC Greensboro, United States

Dr. Sat Narain Gupta, a Professor of Statistics at the University of North Carolina at Greensboro, is a distinguished researcher specializing in sampling techniques, time series analysis, and biostatistics. Holding dual Ph.D. degrees in Statistics and Mathematics, he has made significant contributions to statistical theory and interdisciplinary research. A Fellow of the American Statistical Association, he has received numerous accolades, including the UNCG Undergraduate Research Mentoring Award (2024) and the Lifetime Achievement Award from the India Association of Statistics and Reliability (2023). He has secured multiple NSF grants, led statistical research initiatives, and served as Editor-in-Chief for the Journal of Statistical Theory and Practice. With extensive academic leadership experience, including department head and graduate program director roles, he has also mentored students at various levels. His impact on statistical education, research, and professional service makes him a strong candidate for the Best Researcher Award, though continued large-scale funded research could further solidify his case.

Professional Profile 

Education

Dr. Sat Narain Gupta holds dual Ph.D. degrees in Statistics and Mathematics, reflecting his deep expertise in theoretical and applied statistical research. He earned his Ph.D. in Statistics from a prestigious institution, where he specialized in advanced sampling techniques, time series analysis, and biostatistics. His second Ph.D. in Mathematics further strengthened his analytical foundation, allowing him to bridge the gap between statistical theory and mathematical applications. Throughout his academic journey, Dr. Gupta has pursued rigorous research, contributing significantly to statistical methodologies and interdisciplinary studies. His educational background has not only shaped his research but also positioned him as a respected mentor and educator. As a Professor of Statistics at the University of North Carolina at Greensboro, his advanced training continues to influence his teaching and mentoring, guiding students and researchers in statistical sciences. His dual expertise uniquely qualifies him for leading statistical research initiatives and educational advancements.

Professional Experience

Dr. Sat Narain Gupta is a distinguished Professor of Statistics at the University of North Carolina at Greensboro, where he has been instrumental in advancing statistical research and education. With a strong academic foundation in statistics and mathematics, he has extensive experience in teaching, mentoring, and developing innovative statistical methodologies. His professional career spans decades of contributions to theoretical and applied statistics, including sampling techniques, time series analysis, and biostatistics. Dr. Gupta has actively collaborated with researchers across disciplines, applying statistical models to solve complex real-world problems. He has also supervised numerous graduate students, guiding them in cutting-edge statistical research. Beyond academia, he has served as a consultant for various organizations, leveraging his expertise to enhance data-driven decision-making. His commitment to research, education, and interdisciplinary collaboration has solidified his reputation as a leading figure in statistical sciences, making significant contributions to both academia and industry.

Research Interest

Dr. Sat Narain Gupta’s research interests lie at the intersection of theoretical and applied statistics, focusing on areas such as sampling techniques, time series analysis, biostatistics, and statistical inference. He is particularly interested in developing innovative statistical methodologies that enhance data analysis across diverse fields, including healthcare, economics, and environmental sciences. His work delves into robust estimation techniques, predictive modeling, and the application of statistical algorithms to large and complex datasets. Dr. Gupta is also deeply engaged in interdisciplinary research, collaborating with experts from various domains to apply statistical tools for solving real-world challenges. His contributions have been widely recognized in academic circles, with numerous publications in high-impact journals. He continues to explore emerging trends in machine learning and data science, integrating statistical principles to improve model accuracy and reliability. Through his research, Dr. Gupta aims to advance statistical knowledge and its practical applications in decision-making processes.

Award and Honor

Dr. Sat Narain Gupta has received numerous awards and honors in recognition of his outstanding contributions to the field of statistics and data science. His accolades include prestigious research excellence awards, best paper awards in international conferences, and recognition from esteemed statistical organizations. He has been honored for his significant advancements in statistical methodologies, particularly in sampling techniques, time series analysis, and biostatistics. Dr. Gupta has also received fellowships from renowned academic institutions, acknowledging his dedication to innovative research and education. His contributions to interdisciplinary collaborations have earned him accolades from various scientific communities. In addition to his research achievements, he has been recognized for his mentorship and leadership in academia, guiding students and researchers toward excellence. Through his work, Dr. Gupta has made a lasting impact on the field, and his awards serve as a testament to his dedication, expertise, and influence in statistical research and applications.

Research Skill

Dr. Sat Narain Gupta possesses exceptional research skills in statistical analysis, data modeling, and computational techniques. His expertise spans diverse areas, including sampling methods, time series analysis, biostatistics, and predictive modeling. He is proficient in applying advanced statistical tools and programming languages such as R, Python, and SAS to analyze complex datasets. His strong analytical thinking enables him to develop innovative methodologies for real-world problems in various domains, including healthcare, finance, and environmental sciences. Dr. Gupta excels in designing rigorous research studies, interpreting data with precision, and deriving meaningful insights that contribute to scientific advancements. His ability to integrate theoretical knowledge with practical applications has led to numerous impactful publications and collaborations. Additionally, his expertise in statistical software and machine learning techniques enhances his ability to work on interdisciplinary projects. His research skills reflect his commitment to excellence, innovation, and the advancement of statistical science in modern applications.

Conclusion

Dr. Sat Narain Gupta is an outstanding candidate for the Best Researcher Award. His long-standing contributions to statistical research, mentorship, and leadership make him highly deserving. His fellowships, awards, and research funding demonstrate an exceptional career in advancing the field of statistics. While some areas like high-impact publications and industry collaborations could be further developed, his academic impact and leadership in statistical sciences are exemplary.

Publications Top Noted

  • Title: Nurses’ presenteeism and its effects on self-reported quality of care and costs
    Authors: SA Letvak, CJ Ruhm, SN Gupta
    Year: 2012
    Citations: 461

  • Title: Estimation of sensitivity level of personal interview survey questions
    Authors: S Gupta, B Gupta, S Singh
    Year: 2002
    Citations: 247

  • Title: On improvement in estimating the population mean in simple random sampling
    Authors: S Gupta, J Shabbir
    Year: 2008
    Citations: 204

  • Title: Pressure ulcers: factors contributing to their development in the OR
    Authors: D Engels, M Austin, L McNichol, J Fencl, S Gupta, H Kazi
    Year: 2016
    Citations: 166

  • Title: Mean and sensitivity estimation in optional randomized response models
    Authors: S Gupta, J Shabbir, S Sehra
    Year: 2010
    Citations: 138

  • Title: On estimating finite population mean in simple and stratified random sampling
    Authors: J Shabbir, S Gupta
    Year: 2010
    Citations: 105

  • Title: Variance estimation in simple random sampling using auxiliary information
    Authors: S Gupta, J Shabbir
    Year: 2008
    Citations: 100

  • Title: Predictors of Student Success in Entry-Level Undergraduate Mathematics Courses
    Authors: S Gupta, DE Harris, NM Carrier, P Caron
    Year: 2006
    Citations: 94

  • Title: Estimation of the mean of a sensitive variable in the presence of auxiliary information
    Authors: S Gupta, J Shabbir, R Sousa, P Corte-Real
    Year: 2012
    Citations: 89

  • Title: Differences in health, productivity and quality of care in younger and older nurses
    Authors: S Letvak, C Ruhm, S Gupta
    Year: 2013
    Citations: 88

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

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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