Jiangjun Peng | Mathematics | Best Researcher Award – 1961

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Assoc. Prof. Jiangjun Peng | Mathematics | Best Researcher Award 

Associate Professor at Northwestern Polytechnical University | China

Asso. Prof. Jiangjun Peng is a distinguished scholar at the School of Mathematics and Statistics, Northwestern Polytechnical University, whose academic journey has been defined by dedication to advancing high-dimensional data analysis and intelligent algorithms. With a career spanning both academia and industry, he has significantly contributed to the areas of tensor data analysis, deep learning, and hyperspectral image processing. His research outcomes have received recognition in leading international journals, often being cited widely across disciplines. Beyond his technical expertise, he has actively engaged with the academic community as a reviewer, speaker, and contributor to professional associations.

Profile:

Orcid | Google Scholar

Education:

Asso. Prof. Jiangjun Peng’s educational background is deeply rooted in applied mathematics and statistics. He completed his bachelor’s degree in computational mathematics at Northwestern University, securing a strong foundation in analytical and numerical methods. His postgraduate studies at Xi’an Jiaotong University included both master’s and doctoral degrees, where he trained under the guidance of eminent professors. His doctoral research emphasized robust mathematical models for image processing, supported by rigorous statistical frameworks. This academic pathway equipped him with the technical knowledge, critical thinking, and problem-solving skills that now shape his innovative approaches to high-dimensional data representation.

Experience:

Asso. Prof. Jiangjun Peng’s professional experience bridges academia, industry, and collaborative research institutions. He began his career contributing as a researcher at the Tencent Video Search Center, where he developed advanced algorithms for large-scale video analysis. Later, he expanded his expertise as an assistant researcher at the Chinese University of Hong Kong, focusing on smart city applications and computational methods. Currently serving as an associate professor at Northwestern Polytechnical University, he leads multiple funded projects in collaboration with government, academic, and industrial bodies. His cross-sectoral experience underscores his ability to translate theoretical models into impactful real-world applications.

Research Interest:

Asso. Prof. Jiangjun Peng’s research interests lie at the intersection of applied mathematics, artificial intelligence, and remote sensing. He is particularly focused on tensor data analysis, hyperspectral image processing, and deep learning methods for high-dimensional data. His work involves developing robust algorithms that improve data recovery, denoising, and representation under noisy or incomplete conditions. These contributions have advanced the fields of image security, medical imaging, and environmental monitoring. He is also passionate about bridging model-driven and data-driven methodologies, enabling new solutions that integrate theoretical mathematics with cutting-edge machine learning for scientific and industrial innovation.

Awards and Honors:

Asso. Prof. Jiangjun Peng has earned recognition through prestigious institutional and industry awards that reflect both academic excellence and applied research contributions. His early research was honored with multiple scholarships and university-level accolades, highlighting his scholarly potential. His innovative work at Tencent earned him an industry award for advancing video data applications, while his collaborative project with Huawei received the Huawei Spark Award, showcasing successful academia-industry synergy. More recently, he was selected among the Rising Stars of Northwestern Polytechnical University, a distinction reserved for outstanding young faculty whose work shapes the future of scientific and technological advancement.

Publications:

Title: Hyperspectral image restoration via total variation regularized low-rank tensor decomposition
Citation: 474
Year of Publication: 2017

Title: Enhanced 3DTV regularization and its applications on HSI denoising and compressed sensing
Citation: 188
Year of Publication: 2020

Title: Guaranteed tensor recovery fused low-rankness and smoothness
Citation: 108
Year of Publication: 2023

Title: Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices
Citation: 93
Year of Publication: 2022

Title: Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data
Citation: 90
Year of Publication: 2019

Title: Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation
Citation: 65
Year of Publication: 2022

Title: Learnable representative coefficient image denoiser for hyperspectral image
Citation: 16
Year of Publication: 2024

Conclusion:

Asso. Prof. Jiangjun Peng stands as a highly impactful researcher, educator, and innovator whose career seamlessly integrates theoretical mathematics with practical technological applications. His contributions to tensor analysis, hyperspectral imaging, and deep learning have not only advanced scientific understanding but also enabled real-world breakthroughs in security, healthcare, and environmental monitoring. Through prestigious awards, widely cited publications, and influential collaborations with industry leaders, he has demonstrated both academic brilliance and societal relevance. His nomination for this award is a recognition of his exceptional potential to continue shaping the future of data science and applied mathematics.

Shantia Yarahmadian | Mathematics | Best Researcher Award

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Dr. Shantia Yarahmadian | Mathematics | Best Researcher Award 

Applied Mathematics at Mississippi State University | United States

Dr. Shantia Yarahmadian is an Associate Professor of Mathematics whose career bridges applied mathematics, biology, quantum computing, and engineering sciences. His professional journey reflects a strong dedication to interdisciplinary problem-solving, focusing on challenges that require innovative mathematical modeling. With extensive teaching, research, and mentoring experience, Dr. Shantia Yarahmadian has contributed significantly to advancing knowledge in mathematical biology, stochastic modeling, and computational sciences. His work has also extended into emerging fields such as quantum-resistant blockchain and artificial intelligence applications. He continues to shape the future of mathematics through impactful publications, editorial roles, and leadership in both academic and industry settings.

Profile:

Orcid | Google Shcolar

Education:

Dr. Shantia Yarahmadian’s academic foundation is firmly rooted in applied mathematics and engineering. He earned his Ph.D. in Applied Mathematics from Indiana University, where he developed expertise in nonlinear analysis and boundary layer theory. Before this, he completed a Master’s degree in Mathematics at Isfahan University of Technology, followed by a Bachelor’s degree in Electrical Engineering at the same institution. This progression from engineering fundamentals to advanced mathematical research equipped him with a versatile skill set, allowing him to navigate both theoretical frameworks and practical applications. His educational background laid the groundwork for his diverse research portfolio across mathematics, biology, and computing.

Experience:

Dr. Shantia Yarahmadian has held prominent academic and research positions across universities and industry. As Associate Professor at Mississippi State University, he has developed innovative teaching methods, supervised doctoral and master’s students, and advanced interdisciplinary collaborations. Previously, he served as Assistant Professor in the same department and undertook a Postdoctoral Fellowship in Biology, where he applied mathematical modeling to biomedical challenges. His earlier career included roles as Research Assistant and Associate Instructor, shaping his dual strengths in pedagogy and research. In addition to academia, he also contributes as an industry advisor in cyber security, emphasizing blockchain and quantum-safe technologies.

Research Interests:

Dr. Shantia Yarahmadian’s research spans multiple disciplines, unified by the application of mathematics to real-world problems. In mathematical biology, he models microtubules, tumor radiotherapy, and Alzheimer’s disease progression, contributing to healthcare innovation. His expertise in nonlinear partial differential equations and stochastic processes supports advanced simulations and predictive modeling. In signal processing, his work with wavelets and compressed sensing improves data analysis across engineering and biomedical applications. His contributions to quantum computing and quantum-resistant blockchain represent forward-thinking approaches to cybersecurity and data integrity. Dr. Shantia Yarahmadian’s research highlights the powerful role of mathematical modeling in solving pressing challenges in science and technology.

Awards and Honors:

Dr. Shantia Yarahmadian has received recognition for his excellence in research, teaching, and mentorship throughout his career. He has been honored with awards for outstanding graduate student mentorship and faculty contributions, demonstrating his dedication to fostering future scholars. His innovative research projects in mathematical biology and engineering applications have attracted competitive funding and institutional support. He has also received prestigious fellowships and teaching awards that highlight his dual strengths in research and education. These honors reflect his international reputation as a thought leader in applied mathematics and his commitment to advancing knowledge through impactful academic and professional service.

Publications:

Title: An Ito stochastic differential equations model for the dynamics of the MCF-7 breast cancer cell line treated by radiotherapy
Citation: 27
Year of Publication: 2016

Title: On the use of the genetic algorithm filter-based feature selection technique for satellite precipitation estimation
Citation: 26
Year of Publication: 2012

Title: On the use of a cluster ensemble cloud classification technique in satellite precipitation estimation
Citation: 22
Year of Publication: 2012

Title: Existence and stability of steady states of a reaction convection diffusion equation modeling microtubule formation
Citation: 20
Year of Publication: 2011

Title: The use of compressive sensing and peak detection in the reconstruction of microtubules length time series in the process of dynamic instability
Citation: 16
Year of Publication: 2015

Title: A Hybrid Differential Equations Model for the Dynamics of Single and Double Strand Breaks of Cancer Cells Treated by Radiotherapy: A Meaning for Tumor Life-Span
Citation: 14
Year of Publication: 2022

Title: A Mathematical Model for Amyloid-β Aggregation in the Presence of Metal Ions: A Timescale Analysis for the Progress of Alzheimer Disease
Citation: 14
Year of Publication: 2019

Conclusion:

Dr. Shantia Yarahmadian stands out as a distinguished scholar whose career demonstrates a perfect balance between research innovation, academic mentorship, and cross-disciplinary collaboration. His contributions to mathematical biology, stochastic modeling, quantum computing, and engineering highlight his versatility and forward-thinking approach. By bridging theoretical mathematics with applied sciences, he has produced insights that benefit healthcare, cybersecurity, and data science. His impactful publications, global collaborations, and record of mentorship illustrate a sustained commitment to advancing knowledge and training the next generation of researchers. Dr. Shantia Yarahmadian is an exceptional candidate for award recognition in mathematics and interdisciplinary research.