Felix Sadyrbaev | Applied Mathematics | Best Researcher Award

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Prof. Felix Sadyrbaev | Applied Mathematics | Best Researcher Award

Researcher at Institute of Mathematics and Computer Science, University of Latvia (LU MII abbreviated), Latvia

Professor Felix Sadyrbaev is a distinguished mathematician specializing in dynamical systems, boundary value problems, and mathematical modeling, particularly in network theory and gene regulatory networks. He earned his Ph.D. from Belorussian State University (1982) and completed his habilitation at Latvian State University (1995). Currently, he serves as the Head of Laboratory at the Institute of Mathematics and Computer Science, University of Latvia, and as a Professor and Director of the Doctorate Program in Mathematics at Daugavpils University. With over 190 scholarly publications and active participation in multiple International Congresses of Mathematicians, he has significantly contributed to mathematical research and education. A member of the Latvian and American Mathematical Societies, he also serves on editorial boards of international mathematical journals. Recognized for his contributions, he was elected a Full Member of the Academy of Sciences of Latvia in 2021, further solidifying his impact on the global mathematical community.

Professional Profile 

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Education

Professor Felix Sadyrbaev completed his undergraduate studies at Latvian State University (Riga, former USSR) and later pursued his Ph.D. in Mathematics at Belorussian State University (Minsk) in 1982. His doctoral research focused on dynamical systems, particularly boundary value problems and qualitative theory. In 1995, he earned his habilitation from Latvian State University, further advancing his expertise in mathematical modeling and optimization theory. His academic journey reflects a strong foundation in both theoretical and applied mathematics, enabling him to contribute significantly to various research domains. His education in leading institutions of the former USSR provided him with rigorous training in mathematical analysis, which has been instrumental in shaping his research career. Over the years, his academic background has allowed him to bridge different areas of mathematics, making significant contributions to network theory, gene regulatory networks, and mathematical optimization. His expertise continues to drive innovative research in applied and theoretical mathematics.

Professional Experience

Professor Sadyrbaev has had a distinguished career in academia and research spanning over four decades. Since 1978, he has been affiliated with the Institute of Mathematics and Computer Science at the University of Latvia, where he currently serves as the Head of Laboratory. In 1999, he joined Daugavpils University as a Professor and Director of the Doctorate Program in Mathematics, contributing significantly to the academic development of future researchers. His leadership roles have involved mentoring Ph.D. students, directing mathematical research initiatives, and fostering collaborations with international institutions. As an expert in dynamical systems and mathematical modeling, he has played a key role in advancing the field both locally and globally. His participation in international awards and research projects underscores his commitment to academic excellence. His long-standing association with multiple institutions highlights his dedication to fostering innovation, research collaboration, and the advancement of mathematical sciences.

Research Interest

Professor Sadyrbaev’s research interests lie in the areas of dynamical systems, boundary value problems, and mathematical modeling, with a strong focus on network theory and gene regulatory networks. His work in qualitative theory and optimization has been instrumental in advancing mathematical methods for solving complex real-world problems. He has contributed significantly to differential equations, stability analysis, and nonlinear dynamics, providing insights into critical mathematical frameworks. His interdisciplinary approach bridges applied mathematics, computational techniques, and theoretical modeling, making his research highly relevant across various scientific domains. His contributions to mathematical modeling in biology and engineering have led to significant applications, particularly in understanding complex network systems. With over 190 publications and numerous plenary talks, his research has influenced both academia and industry. His ongoing work continues to explore innovative mathematical methods for solving contemporary challenges, reinforcing his impact on the global mathematical community.

Awards and Honors

Professor Sadyrbaev has received prestigious recognition for his outstanding contributions to mathematics. In 2021, he was elected a Full Member of the Academy of Sciences of Latvia, a testament to his significant impact on mathematical research and education. His participation in major International Congresses of Mathematicians (ICM) across different countries, including Berlin, Beijing, Bangalore, Seoul, and São Paulo, highlights his global academic influence. He has also served as a delegate to the International Mathematical Union (IMU) General Assembly, representing the Latvian Mathematical Society in key international discussions. Additionally, he is a member of the Latvian Mathematical Society and the American Mathematical Society, further cementing his standing in the international mathematical community. His editorial board memberships in several international mathematical journals reflect his role in shaping contemporary mathematical research. His numerous honors underscore his dedication to advancing mathematical sciences through research, mentorship, and academic leadership.

Conclusion

Professor Felix Sadyrbaev is a highly accomplished mathematician with extensive contributions to dynamical systems, mathematical modeling, and network theory. His distinguished career spans over four decades, with significant roles in research, academic leadership, and international collaborations. His election as a Full Member of the Academy of Sciences of Latvia, numerous publications, and participation in prestigious international congresses solidify his reputation as a leading expert in his field. His influence extends beyond research, as he plays a key role in mentoring future mathematicians and fostering interdisciplinary collaborations. As a respected figure in the mathematical community, his work continues to shape contemporary mathematical theory and applications. Through his editorial roles, award participation, and research impact, he remains a driving force in the advancement of mathematical sciences. His remarkable career serves as an inspiration for young researchers and highlights the importance of mathematics in solving real-world challenges.

Publications Top Noted

  • On differential equations with exponential nonlinearities

    • Authors: Armands Gritsans, Felix Sadyrbaev
    • Year: 2025
    • Source: Applied Numerical Mathematics

  • Remarks on Modeling of Neural Networks

    • Authors: Felix Sadyrbaev
    • Year: [No year mentioned]
    • Source: [No source information available]

  • In Search of Chaos in Genetic Systems

    • Authors: Olga Kozlovska, Felix Sadyrbaev
    • Year: 2024
    • Source: Chaos Theory and Applications

  • Comparative Analysis of Models of Genetic and Neuronal Networks

    • Authors: Diana Ogorelova, Felix Sadyrbaev
    • Year: 2024
    • Source: Mathematical Modelling and Analysis

  • Editorial: Mathematical modeling of gene networks

    • Authors: Jacques François Demongeot, Felix Sadyrbaev, Inna Samuilik
    • Year: 2024
    • Source: Frontiers in Applied Mathematics and Statistics

  • On Period Annuli and Induced Chaos

    • Authors: Svetlana Atslega, Olga Kozlovska, Felix Sadyrbaev
    • Year: 2024
    • Source: WSEAS Transactions on Systems

  • A New 3D Chaotic Attractor in Gene Regulatory Network

    • Authors: Olga Kozlovska, Felix Sadyrbaev, Inna Samuilik
    • Year: 2024
    • Source: Mathematics

  • On Solutions of the Third-Order Ordinary Differential Equations of Emden-Fowler Type

    • Authors: Felix Sadyrbaev
    • Year: 2023
    • Source: Dynamics

  • On Coexistence of Inhibition and Activation in Genetic Regulatory Networks

    • Authors: Felix Sadyrbaev, Valentin Sengileyev, Albert Silvans
    • Year: [No year mentioned]

 

Halima Bensmail | Applied Mathematics | Best Researcher Award

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Prof. Dr. Halima Bensmail | Applied Mathematics | Best Researcher Award

Principal scientist at Qatar Computing Research Institute, Qatar

Dr. Halima Bensmail is a distinguished Principal Scientist at the Qatar Computing Research Institute, specializing in machine learning, bioinformatics, biostatistics, and statistical modeling. With a Ph.D. in Statistics (Summa Cum Laude) from the University Pierre & Marie Curie, she has made significant contributions to Bayesian inference, multivariate analysis, and precision medicine. She has an impressive research record with an H-index of 31, i10-index of 54, and around 140 publications in prestigious journals such as Nature Communications, JASA, and IEEE TNNLS. As the founder of the Statistical Machine Learning and Bioinformatics group at QCRI, she has led groundbreaking projects, including the development of open-source data-driven tools like the PRISQ pre-diabetes screening model and MCLUST clustering algorithm. With extensive academic experience in the USA, France, and the Netherlands, she has mentored numerous postdocs and students, shaping the next generation of researchers. Her expertise and leadership make her a key figure in data science and precision health.

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Education

Dr. Halima Bensmail holds a Ph.D. in Statistical Machine Learning (Summa Cum Laude) from the University Pierre & Marie Curie (Paris 6), where she specialized in Bayesian inference, spectral decomposition, and mixture models. Her thesis focused on deterministic and Bayesian model-based clustering and classification for data science applications. Prior to that, she earned an M.S. in Machine Learning from the same university, with a focus on probability, financial modeling, and stochastic processes. She also holds a Bachelor’s degree in Applied Mathematics and Statistics from the University Mohammed V in Morocco, where she gained expertise in numerical analysis, stochastic processes, topology, and mathematical programming. Throughout her academic journey, she was mentored by esteemed professors and developed a strong foundation in theoretical and applied statistics. Her educational background has laid the groundwork for her pioneering research in machine learning, bioinformatics, and data-driven modeling for real-world applications.

Professional Experience

Dr. Bensmail is currently a Principal Scientist at the Qatar Computing Research Institute (QCRI), where she leads research in bioinformatics, statistical machine learning, and artificial intelligence. She also serves as a Full Professor in the College of Science and Engineering at Hamad Bin Khalifa University and a Visiting Full Professor at Texas A&M University at Qatar. Previously, she held tenured faculty positions at Virginia Medical School and the University of Tennessee, where she contributed significantly to public health and business administration research. She has also worked as a Research Scientist at the University of Leiden, a scientist at the Fred Hutchinson Cancer Research Center, and a postdoctoral researcher at the University of Washington. With decades of experience across academia and research institutions in the U.S., Europe, and the Middle East, she has built expertise in developing statistical and AI-driven solutions for biomedical and computational challenges.

Research Interests

Dr. Bensmail’s research spans statistical machine learning, bioinformatics, and precision medicine. She has developed novel clustering algorithms, such as an advanced Bayesian clustering model implemented in the MCLUST package, and statistical methods for analyzing Next-Generation Sequencing (NGS) data. She is also interested in computational biology, specifically protein-protein interactions, protein solubility, and structural biology. Her work includes dimensionality reduction techniques like nonnegative matrix factorization and discriminative sparse coding for domain adaptation. In the field of precision medicine, she has designed PRISQ, a statistical model for pre-diabetes screening. Her broader interests include Bayesian statistics, functional data analysis, information theory, and high-dimensional data modeling. With a strong focus on developing real-world data-driven tools, she actively contributes to statistical methodologies that enhance decision-making in medicine, genomics, and artificial intelligence applications.

Awards and Honors

Dr. Bensmail has received numerous accolades for her contributions to machine learning, bioinformatics, and statistical modeling. Her work has been widely recognized, with over 140 peer-reviewed publications and an H-index of 31, demonstrating the impact of her research. She has secured research grants and led major projects in AI-driven healthcare solutions. Her contributions to the field have been acknowledged through invitations to serve as a keynote speaker at international awards and as an editorial board member for high-impact journals. She has also been instrumental in mentoring young researchers, postdoctoral fellows, and doctoral students, fostering the next generation of scientists in AI, statistics, and bioinformatics. Additionally, her work on statistical methods for precision medicine and biomedical informatics has gained international recognition, positioning her as a leading expert in the field of data science for healthcare and computational biology.

Conclusion

Dr. Halima Bensmail is a pioneering researcher in machine learning, statistical modeling, and bioinformatics, with a career spanning leading institutions in the U.S., Europe, and the Middle East. Her contributions to clustering algorithms, high-dimensional data analysis, and precision medicine have made a lasting impact on the fields of AI and computational biology. As a mentor and leader, she has shaped numerous young scientists and postdocs, driving innovation in data science applications. With a robust publication record, influential research projects, and a dedication to developing real-world AI-driven solutions, she stands as a leading figure in statistical machine learning. Her expertise and contributions continue to push the boundaries of knowledge in bioinformatics, artificial intelligence, and healthcare analytics, making her a strong candidate for prestigious research awards and recognition in scientific communities worldwide.

Publications Top Noted