Assist Prof Dr. Elham Taghizadeh | Mathematics | Women Researcher Award

Computer science at Islamic Azad University of Central TehranBranch, Iran

Elham Taghizadeh, Ph.D., is an Assistant Professor in the Department of Mathematics at Islamic Azad University of Central Tehran Branch, specializing in numerical analysis, fractional calculus, and meshless methods. With a robust academic foundation and an interdisciplinary research approach, she bridges computational mathematics with data science and machine learning. Elham earned her Ph.D. from the University of Mazandaran, where she focused on solving functional equations with time delays using meshless methods. She has contributed to numerous high-impact journals and has participated in international collaborations, including a Ph.D. research fellowship at the University of Porto in Portugal. Her innovative work has garnered recognition, including top thesis awards and competitive selections in academic conferences. As a dedicated educator, Elham has mentored students in applied mathematics, data science, and programming, shaping the next generation of researchers.

Professional Profile

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Education

Elham Taghizadeh began her academic journey at the University of Arak, where she obtained her Bachelor of Science in Mathematics from 2002 to 2006. She continued her studies at Alzahra University in Tehran, earning a Master of Science (MSc) degree from 2007 to 2010. Her master’s thesis focused on the stability of linear time-varying multiple delays and their applications in control problems, supervised by Prof. Yadollah Ordokhani. Building on her passion for numerical analysis and computational methods, she pursued a Ph.D. at the University of Mazandaran from 2013 to 2019. Her Ph.D. thesis, under the supervision of Prof. Mashaallah Matinfar, was titled “Meshless methods based on moving least squares for solving functional equations with time delays.” This research laid the groundwork for her interdisciplinary studies, combining numerical methods with advanced computational tools, a field she continues to explore in her academic career.

Professional Experience

Elham Taghizadeh has a diverse range of academic and professional experiences. She currently serves as an Assistant Professor at the Islamic Azad University of Central Tehran Branch, where she has been teaching since 2020. She teaches a variety of mathematics courses, including calculus, differential equations, numerical methods, data mining, and machine learning. She also has teaching experience at the University of Mazandaran (2016-2021) and Rouzbahan University (2015-2016), where she taught topics like advanced programming and numerical analysis. In research, Elham held a Ph.D. research fellowship at the Faculty of Engineering at the University of Porto in Portugal, where she worked on solving time-delay equations using radial point interpolation methods. Additionally, she serves as a peer reviewer for prestigious journals, such as the Journal of Applied Mathematics and Computing and Mathematical Methods in the Applied Sciences, contributing to the advancement of the mathematical research community.

Research Interest

Elham Taghizadeh’s research interests span a wide range of topics within applied mathematics, focusing on numerical analysis, fractional partial differential equations, and meshless methods. She is particularly interested in developing computational solutions for time-delay systems and functional equations, using innovative techniques such as the Moving Least Squares Method and Radial Point Interpolation. Her research also integrates machine learning and data science to enhance the accuracy and efficiency of numerical simulations in complex systems. Elham has worked on interdisciplinary projects, including SEIR modeling for predicting the spread of COVID-19 and applying meshless methods to solve Volterra integral equations. Her work is not only theoretical but also highly practical, with applications in control theory, dynamical systems, and epidemic modeling. She continues to explore cutting-edge methods that push the boundaries of computational mathematics, with the goal of solving real-world problems through advanced mathematical techniques.

Award and Honor

Elham Taghizadeh’s dedication to research and academic excellence has been recognized through numerous awards and honors. She ranked third out of 100 students in the Applied Mathematics Ph.D. entrance exam, marking her as a top candidate for her doctoral studies. During her Ph.D., she received the Top Thesis Award at the 2nd National Festival on Research Thesis Khayyam Award, highlighting the innovative contributions of her work in numerical methods. In 2021, Elham was selected for the student competition at the 10th International Conference on Acoustics and Vibrations in Iran, reflecting her expertise in applying computational techniques to complex mathematical problems. Additionally, she has been recognized for her impactful research, particularly in the fields of meshless methods and fractional calculus, through competitive conference selections and journal publications. Her ongoing contributions to applied mathematics and interdisciplinary research make her a standout figure in the academic community.

Conclusion

Elham Taghizadeh is a strong candidate for the Women Researcher Award due to her interdisciplinary research approach, impactful publications, and contributions to computational mathematics and data science. Her work on meshless methods and fractional differential equations is both innovative and aligned with modern research needs. With a focus on expanding her research impact and international collaborations, she has the potential to further elevate her academic and societal contributions, making her highly suitable for recognition in this award.

Publication top noted

• Taghizadeh, E., Mohammad-Djafari, A. (2022). SEIR Modeling, Simulation, Parameter Estimation, and Their Application for COVID-19 Epidemic Prediction. Physical Sciences Forum, 5(1), 18.
  Citations: 9
• Taghizadeh, E., Matinfar, M. (2019). Modified Numerical Approaches for a Class of Volterra Integral Equations with Proportional Delays. Computational and Applied Mathematics, 38, 1-19.
  Citations: 8
• Matinfar, M., Taghizadeh, E., Pourabd, M. (2021). Application of Moving Least Squares Algorithm for Solving Systems of Volterra Integral Equations. International Journal of Nonlinear Sciences and Numerical Simulation, 22(3-4).
  Citations: 4
• Nategh, M., Baleanu, D., Taghizadeh, E., Gilani, Z.G. (2017). Almost Local Stability in Discrete Delayed Chaotic Systems. Nonlinear Dynamics, 89, 2393-2402.
  Citations: 3
• El Majouti, Z., Taghizadeh, E., El Jid, R. (2023). A Meshless Method for the Numerical Solution of Fractional Stochastic Integro-Differential Equations Based on the Moving Least Square Approach. International Journal of Applied and Computational Mathematics, 9(3), 27.
  Citations: 2
• Taghizadeh, E., Ordokhani, Y., Behmardi, D. (2011). Delay-Dependent α-Stable Linear Systems with Multiple Time Delays. Contemporary Engineering Sciences, 4(4), 165-176.
  Citations: 2
• Dabiri, A., Moghaddam, B.P., Taghizadeh, E., Galhano, A. (2024). A Meshless Radial Point Interpolation Method for Solving Fractional Navier–Stokes Equations. Axioms, 13(10), 695.
  Citations: 0
• Pourabda, M., Taghizadeh, E. (2016). Solving the Model of the Risk of Microcephaly Induced by the Zika Virus (ZIKV) by a Modified Moving Least Squares Method. arXiv preprint, arXiv:1611.06330.
  Citations: 0
• Ordokhani, Y., Taghizadeh, E., Behmardi, D., Matinfar, M. (2016). Exponential Stability of Linear Systems with Multiple Time Delays. Mathematical Researches, 2(1), 69-78.
  Citations: 0
• Taghizadeh, E., Matinfar, M. (Year N/A). α-Stability Criterion on Linear System with Multiple Time Varying Delays.

Mr. Xufeng Zhang | Mathematics | Best Researcher Award

Master at School of Mathematics and Physics, North China Electric Power University, China

Xufeng Zhang is a highly accomplished researcher with a strong academic background in applied mathematics and statistics. Graduating from Inner Mongolia University with a GPA of 4.0/5 and earning first-class scholarships, Zhang has consistently excelled in both studies and competitive mathematics, winning multiple national awards. His research experience includes working on high-dimensional data compression and nonlinear dimensionality reduction, where he developed platforms and algorithms such as LE, LLE, and T-SNE, demonstrating a solid understanding of computational methods. Zhang also possesses strong programming skills in languages like C++, Python, and Matlab, with a focus on artificial intelligence and algorithm design. Additionally, he has contributed to education through math tutoring and Olympiad coaching. While Zhang’s potential is evident, with further experience in publishing research and leading projects, he could solidify his position as a leading researcher in his field.

Professional Profile

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Education

Xufeng Zhang has a solid academic background in mathematics and statistics, beginning with his undergraduate education at Inner Mongolia University, where he graduated with a degree in statistics. He excelled academically, achieving a GPA of 4.0/5 and earning the distinction of being an excellent graduate, which underscores his strong grasp of mathematical principles. Zhang is currently pursuing a master’s degree in applied mathematics at North China Electric Power University, where he has been recognized with first-class scholarships in both 2022 and 2023 for his outstanding academic performance. Throughout his academic journey, Zhang has also actively participated in prestigious mathematics competitions, earning top positions in events such as the China College Students Mathematics Competition and the Alibaba Global Mathematics Competition. His educational achievements reflect a deep commitment to mathematical research and a foundation that equips him with the knowledge and skills necessary for advanced work in data science and applied mathematics.

Professional Experience

Xufeng Zhang’s professional experience is centered around his work in data compression and algorithm development. He was a core member of a high-dimensional data compression project at a computer software company, where he developed a platform for nonlinear dimensionality reduction. Zhang designed and implemented algorithms such as LE, LLE, and T-SNE to reduce the dimensionality of handwritten digital data and compared their results. His work included creating classifiers using dimensionality-reduced data, showcasing his ability to apply theoretical concepts to real-world data problems. In addition to his research and technical development work, Zhang has experience in education, having tutored middle school students in math, including preparing materials for plane geometry problem-solving and providing one-on-one training for math Olympiads. His combined experience in research, algorithm design, and education demonstrates a versatile skill set that bridges both theoretical and practical aspects of mathematics and computer science.

Research Interest

Xufeng Zhang’s research interests lie primarily in the areas of applied mathematics, data science, and algorithm design, with a focus on high-dimensional data compression and nonlinear dimensionality reduction. He is particularly interested in developing and optimizing algorithms such as LE, LLE, and T-SNE, which are used to process and analyze complex datasets by reducing their dimensionality while preserving key features. This interest extends to applications in fields such as image processing and artificial intelligence, where dimensionality reduction can significantly enhance computational efficiency and accuracy. Zhang is also fascinated by the integration of these algorithms into practical machine learning models, including the creation of classifiers that use reduced data for improved performance. His passion for theoretical derivation and problem-solving, combined with his strong background in computational methods, drives his pursuit of innovative solutions to challenges in data analysis and artificial intelligence, making these fields central to his research endeavors.

Award and Honor

Xufeng Zhang has received numerous awards and honors that reflect his exceptional capabilities in mathematics and academic excellence. During his undergraduate studies at Inner Mongolia University, he was recognized as an excellent graduate with a GPA of 4.0/5, showcasing his dedication to academic pursuits. Zhang’s mathematical skills have been highlighted through his achievements in national and international competitions. In 2017, he won first place in the 9th China College Students Mathematics Competition for the Inner Mongolia Autonomous Region. In 2018, he earned a second prize in the Mathematics Competition at Xi’an Jiaotong University and ranked 256th in the preliminary round of the prestigious Alibaba Global Mathematics Competition. Additionally, he was awarded the Second-Class Academic Excellence Scholar award at Inner Mongolia University in 2019. These accolades, combined with his first-class scholarships at North China Electric Power University, underscore his commitment to excellence in both academics and competitive mathematics.

Conclusion

Xufeng Zhang shows strong potential for becoming a top researcher, particularly in applied mathematics and data science, with notable academic achievements and technical expertise. However, the lack of published research and limited professional exposure in high-profile or international research environments may limit their candidacy for the Best Researcher Award at this time. Strengthening these aspects, especially through publication efforts and leadership development, would greatly enhance their standing for future nominations.

Publication top noted

• “Onion Lattices and an Answer to an Open Problem on Convolution Lattices”
  🖋 Authors: Zhang, X., Wang, A.
  📅 Year: 2024
  📕 Journal: Fuzzy Sets and Systems
  🔢 Article No.: 484, 108939
  📊 Citations: 0
• “Distributed Constrained Optimization for Multi-Agent Networks with Communication Delays under Time-Varying Topologies”
  🖋 Authors: An, Y., Wang, A., Zhang, X., Xiao, F.
  📅 Year: 2024
  📕 Journal: Systems and Control Letters
  🔢 Article No.: 185, 105733
  📊 Citations: 0