Fathalla Rihan | Statistical Analysis | Best Scholar Award

Posted on by ScienceFather

Prof. Fathalla Rihan | Statistical Analysis | Best Scholar Award

United Arab Emirates University | United Arab Emirates

Publication Profile

Orcid

Scopus

Google scholar

Professor Fathalla A. Rihan: A Distinguished Expert in Mathematical Sciences 📚✨

Current Position(s) & Employment History 🏫

  • Professor of Mathematics (2017–present)
    Department of Mathematical Sciences, College of Science, UAE University, UAE 🇦🇪
  • Professor of Mathematics (2011–present)
    Department of Mathematics (on leave), Faculty of Science, Helwan University, Egypt 🇪🇬
  • Associate Professor (2012–2017)
    Department of Mathematical Sciences, College of Science, UAE University, UAE
  • Assistant Professor (2008–2012)
    Department of Mathematical Sciences, College of Science, UAE University, UAE
  • Associate Professor of Mathematics (2005–2011)
    Department of Mathematics, Faculty of Science, Helwan University, Egypt
  • Honorary Research Fellow (2000–2010)
    School of Mathematics, University of Manchester, UK 🇬🇧
  • Assistant Professor (2000–2005)
    Department of Mathematics, Faculty of Science, Helwan University, Egypt
    (On leave for Post-Doctoral research in the UK)
  • Senior Research Fellow (2003–2006)
    Salford University, UK, in collaboration with Met Office (Reading University)
  • Research Fellow (2001–2003)
    School of Mathematics, Manchester University, UK
  • Postgraduate Studentship and Lecturer Assistant (1996–2000)
    Department of Mathematics, Manchester University, UK
  • Lecturer (1992–1996)
    Department of Mathematics, Helwan University, Egypt
  • Instructor (1987–1992)
    Department of Mathematics, Faculty of Science, Helwan University, Egypt

Education 🎓

  • Doctor of Science (DSc) in Mathematics (2019)
    National University of Uzbekistan
    Dissertation: “Qualitative and Quantitative Features of Delay Differential Equations and Their Applications”
  • Ph.D. in Mathematics (2000)
    University of Manchester, UK
    Thesis: “Numerical Treatment of Delay Differential Equations in the Biosciences”
  • M.Sc. in Numerical Analysis (1992)
    Helwan University, Egypt
    Thesis: “Spectral and Pseudo-Spectral Method for Solving Partial Differential Equations Using Orthogonal Polynomials”
  • B.Sc. in Mathematics (1985)
    Alexandria University, Egypt, Graduated with Honours

Research Interests  On Statistical Analysis🔬

Prof. Fathalla A. Rihan leads two primary research groups:

  • Numerical Analysis & Mathematical Biology
    • Mathematical modeling of phenomena with time-lags (cell division, population dynamics, infectious diseases, etc.)
    • Numerical treatments for Ordinary, Partial, and Delay Differential Equations (DDEs), as well as Fractional Order Differential Equations
    • Parameter estimation and sensitivity analysis
  • Data Assimilation & Numerical Weather Prediction (NWP)
    • Advanced data assimilation techniques like 3DVar/4D-Var systems
    • Impact of Doppler radial velocities on analysis and forecasting

Research Projects 📊

  • 2024–2026: PI, AUA-UAEU Project: “Continuous Runge-Kutta Methods for Pantograph Delay Differential Equations”
  • 2022–2024: PI, ZU-UAEU Project: “Impact of Stochastic Noise on COVID-19 Dynamics in the UAE”
  • 2022: PI, SURE+ 2022 Program: “Mathematical Modeling of COVID-19 in the UAE”
  • 2021–2023: PI, UPAR-UAEU Project: “Quantitative Features of Delay Differential Equations in Biological Systems”
  • 2017–2019: PI, SQU/UAEU Project: “Delay Differential Models of Immune Response with Viral/Bacterial Infections”

Awards & Recognition 🏆

  • Resignation Award for Publications in Top 5% Journals (2023)
  • Member of Mohammed Bin Rashid Academy of Scientists (2021–present)
  • Top 2% World Scientist (Stanford University 2019–2022)
  • University Excellence Merit Allowance (2019–2020)
  • College Excellence Award for Service (2019–2020)
  • Distinguished Research Awards (2017–2019)
  • College Award for Scholarly Research (2014–2015)

Visitor ships 🌍

  • Research Visitor (2000–2001): Manchester University, UK
  • Research Visitor (2001): University of Hertfordshire, UK
  • Research Visitor (2003–2004): NCAR & Oklahoma Weather Center, USA

Publication Top Notes

Optimal control of glucose-insulin dynamics via delay differential model with fractional-order
    • Authors: Fathalla A. Rihan, K. Udhayakumar
    • Journal: Alexandria Engineering Journal
    • Year: 2025-02 🗓️
Modeling Hybrid Crossover Dynamics of Immuno‐Chemotherapy and Gene Therapy: A Numerical Approach
    • Authors: Muner M. Abou Hasan, Seham M. AL‐Mekhlafi, Fathala A. Rihan, Hannah A. Al‐Ali
    • Journal: Mathematical Methods in the Applied Sciences
    • Year: 2025-02-03 🗓️
Stabilization of bilinear systems with distributed delays using the Banach state space decomposition method
    • Authors: Ayoub Cheddour, Abdelhai Elazzouzi, Fathalla A. Rihan
    • Journal: IMA Journal of Mathematical Control and Information
    • Year: 2024-12-19 🗓️
Continuous Runge–Kutta schemes for pantograph type delay differential equations
    • Authors: Fathalla A. Rihan
    • Journal: Partial Differential Equations in Applied Mathematics
    • Year: 2024-09 🗓️
Fractional order delay differential model of a tumor-immune system with vaccine efficacy: Stability, bifurcation and control
    • Authors: Fathalla A. Rihan, K. Udhayakumar
    • Journal: Chaos, Solitons & Fractals
    • Year: 2023-08 🗓️

 

Niansheng Tang | Statistical Methods | Best Researcher Award

Posted on by ScienceFather

Prof Dr. Niansheng Tang | Statistical Methods | Best Researcher Award

Yunan University | China

Publication Profile

Scopus

Orcid

👨‍🏫 Early Academic Pursuits

Prof. Dr. Niansheng Tang is a distinguished scholar affiliated with Yunnan University, China. His academic journey started with a series of significant achievements and certifications, culminating in his specialization in statistical and nonlinear models. His academic foundation includes attending key international training sessions and engaging with vital academic societies that contributed to his development as a respected academic in his field.

💼 Professional Endeavors and Career

Dr. Tang’s career has been characterized by a series of high-level academic positions. From 2000 to 2020, he held various teaching and research roles, including significant appointments at Yunnan University. He has been deeply involved in statistical research, with a focus on nonlinear models, reproductive dispersion, and statistical inference for incomplete data. His roles have also included administrative and leadership positions within academic organizations and societies.

🔬 Research Focus and Contributions

Dr. Tang’s primary research focuses on statistical modeling, particularly in areas like nonlinear regression, incomplete data inference, and applications in social, economic, and biomedical studies. He has been the principal investigator for multiple funded projects from the Natural Science Foundation of China, Yunnan Province, and the National Social Science Foundation of China. His work has contributed to the understanding of complex data analysis and its application in various fields.

🌍 Impact and Influence

Dr. Tang has made significant contributions to the field of statistics, with his work influencing various sectors including social sciences, economics, and biomedical research. He has been involved with several prominent academic journals and has contributed to the dissemination of knowledge in his field. His research has impacted both theoretical and applied aspects of statistical modeling, and his efforts have been acknowledged globally.

📚 Academic Cites and Recognition

Dr. Tang’s research is widely cited, with his work featured in leading international journals. He has received multiple awards for his contributions to statistical theory and its application in diverse fields. His impact in the statistical community is reflected through various accolades, including recognition as a member of several esteemed academic bodies such as the ISI and the IMS.

💻 Technical Skills and Expertise

With expertise in statistical theory and computational methods, Dr. Tang has advanced the understanding of complex data structures through his work on generalized nonlinear models. His technical proficiency extends to research in incomplete data models and the development of inference methods applicable to small sample sizes, particularly in biomedical studies.

🎓 Teaching Experience

Throughout his career, Dr. Tang has played an important role in mentoring and educating the next generation of statisticians. He has taught various courses at Yunnan University and has supervised numerous graduate students. His teaching style combines theoretical knowledge with practical application, making him a highly regarded educator.

🏛️ Legacy and Future Contributions

Dr. Tang’s legacy in the field of statistics is built on his groundbreaking research, dedication to education, and service to academic societies. Moving forward, he aims to continue influencing the development of statistical methodologies and applying them to emerging areas such as big data analysis and artificial intelligence. His future contributions will likely continue to shape both academic and practical advancements in his field.

🔍 Ongoing Research and Projects

Dr. Tang’s recent and ongoing projects focus on the statistical analysis of reproductive dispersion models, social sciences, and biomedical research. His work continues to garner support from national funding bodies and academic institutions, ensuring the continued impact of his research.

Publication Top Notes

Federated Learning based on Kernel Local Differential Privacy and Low Gradient Sampling
    • Authors: Yi Chen, Dan Chen, Niansheng Tang
    • Journal: IEEE Access 📡
    • Year: 2025 🗓️
The impact of green digital economy on carbon emission efficiency of financial industry: evidence from 284 cities in China
    • Authors: Zhichuan Zhu, Yuxin Wan, Niansheng Tang
    • Journal: Environment, Development and Sustainability 🌍💡
    • Year: 2025 🗓️
Tuning-free sparse clustering via alternating hard-thresholding
    • Authors: Wei Dong, Chen Xu, Jinhan Xie, Niansheng Tang
    • Journal: Journal of Multivariate Analysis 📊
    • Year: 2024 🗓️
Variational Bayesian approach for analyzing interval-censored data under the proportional hazards model
    • Authors: Wenting Liu, Huiqiong Li, Niansheng Tang, Jun Lyu
    • Journal: Computational Statistics and Data Analysis 💻📉
    • Year: 2024 🗓️
Semiparametric Bayesian approach to assess non-inferiority with assay sensitivity in a three-arm trial with normally distributed endpoints
    • Authors: Niansheng Tang, Fan Liang, Depeng Jiang
    • Journal: Computational Statistics 💻📊
    • Year: 2024 🗓️

Tesfay Gidey | Statistical Modeling | Best Researcher Award

Posted on by ScienceFather

Dr. Tesfay Gidey | Statistical Modeling | Best Researcher Award

Dr. Tesfay Gidey at Addis Ababa Science and Technology University, Ethiopia

👨‍🎓 Professional Profile

 📚 Early Academic Pursuits

Dr. Tesfay Gidey Hailu’s academic journey began with a strong foundation in Statistics at Addis Ababa University, where he earned his BSc with a minor in Computer Science. This rigorous curriculum equipped him with vital skills in statistical methods and computer programming, laying the groundwork for his future studies. He furthered his education with an MSc in Health Informatics and Biostatistics at Mekelle University, delving into advanced topics such as epidemiology and public health, which ignited his passion for applying data-driven solutions in healthcare.

💼 Professional Endeavors

Dr. Gidey has demonstrated remarkable leadership in academia, serving as Associate Dean at Addis Ababa Science and Technology University (AASTU) for multiple departments. His responsibilities included driving research initiatives, enhancing curriculum quality, and overseeing student services. Additionally, his role as Head of Department at Jimma University showcased his commitment to improving departmental operations and student satisfaction. His extensive experience in project management underscores his ability to lead technology projects effectively.

🔬 Contributions and Research Focus

Dr. Gidey’s research is centered on critical areas such as signal processing, indoor localization, and machine learning. His Ph.D. work at the University of Electronic Science and Technology of China focused on applying advanced algorithms to real-world problems, particularly in indoor positioning systems. His contributions to quality control in manufacturing industries and the modeling of public health data further highlight his dedication to leveraging data for impactful solutions.

🌍 Impact and Influence

Through his academic and professional endeavors, Dr. Gidey has significantly influenced the fields of Information and Communication Engineering and data science. His ability to bridge theory and practical application positions him as a valuable asset in technology-driven environments. His commitment to mentoring students and faculty alike has fostered a culture of innovation and continuous improvement within the institutions he has served.

📊 Academic Citations

Dr. Gidey’s scholarly work has gained recognition within academic circles, with several publications focusing on his research areas. His contributions to journals as a reviewer and author have enriched the body of knowledge in his fields, emphasizing the importance of interdisciplinary approaches to problem-solving.

🛠️ Technical Skills

Dr. Gidey is proficient in a variety of programming languages, including Python, R, Java, and C++. He is skilled in using statistical tools such as SAS, SPSS, and advanced machine learning packages. His expertise in data visualization and business intelligence tools, like Tableau and Power BI, equips him to extract meaningful insights from complex datasets.

👩‍🏫 Teaching Experience

With years of teaching experience, Dr. Gidey has effectively communicated complex subjects to students, ensuring they grasp fundamental concepts in information technology and data science. His ability to adapt his teaching methods to diverse learning styles has contributed to high levels of student engagement and success.

🌟 Legacy and Future Contributions

Dr. Gidey is dedicated to advancing the fields of data science and information engineering through research, teaching, and community engagement. His vision includes fostering collaborations between academia and industry, particularly in developing innovative solutions for public health and manufacturing. As he continues his career, Dr. Gidey aims to leave a lasting legacy that inspires future generations of engineers and data scientists.

📈 Conclusion

With a strong foundation in academic excellence, professional leadership, and research innovation, Dr. Tesfay Gidey Hailu stands poised to make significant contributions to the field of Information and Communication Engineering. His passion for data and commitment to actionable results ensure he will continue to drive progress and inspire those around him.

 

📖 Top Noted Publications

Theories and Methods for Indoor Positioning Systems: A Comparative Analysis, Challenges, and Prospective Measures
  • Author: Tesfay Gidey Hailu; Xiansheng Guo; Haonan Si; Lin Li; Yukun Zhang
    Journal: Sensors
    Year: 2024
Ada-LT IP: Functional Discriminant Analysis of Feature Extraction for Adaptive Long-Term Wi-Fi Indoor Localization in Evolving Environments
  • Author: Tesfay Gidey Hailu; Xiansheng Guo; Haonan Si; Lin Li; Yukun Zhang
    Journal: Sensors
    Year: 2024
Measurement Science and Technology
  • Author: Bright Awuku; Ying Huang; Nita Yodo; Eric Asa
    Journal: Measurement Science and Technology
    Year: 2024
Measurement Science and Technology
  • Author: Xun Zhang; Guanghua Xu; Xiaobi Chen; Ruiquan Chen; Jieren Xie; Peiyuan Tian; Sicong Zhang; Qingqiang Wu
    Journal: Measurement Science and Technology
    Year: 2024
Machine Learning: Science and Technology
  • Author: Omer Subasi; Sayan Ghosh; Joseph Manzano; Bruce Palmer; Andrés Marquez
    Journal: Machine Learning: Science and Technology
    Year: 2024
 MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection
  • Author: Tesfay Gidey Hailu; Taye Abdulkadir Edris
    Journal: Intelligent Information Management
    Year: 2023

Jinlu Liu | Bayesian Analysis | Best Researcher Award

Posted on by ScienceFather

Mr. Jinlu Liu, Bayesian Analysis, Best Researcher Award

Mr. Jinlu Liu at University of Edinburgh, United Kingdom

Professional Profile

Orcid Profile
Scopus Profile

Summary

Jinlu Liu is an accomplished statistician and researcher specializing in Bayesian nonparametric mixture models and their applications in single-cell sequencing data. With a PhD in Statistics and Artificial Intelligence from the University of Edinburgh, Jinlu has developed innovative methods for cell subtype detection and neuronal connectivity modeling. His expertise spans advanced statistical modeling, data analysis, and computational techniques, and he has made significant contributions through publications, poster presentations, and awards.

Education

  • PhD in Statistics and Artificial Intelligence, University of Edinburgh (Sep 2019 – Feb 2024)
    Research focused on constructing Bayesian nonparametric mixture models for single-cell sequencing data, with applications in detecting cell subtypes and genetic markers.
  • MSc in Data Science (Distinction), University of Edinburgh (Sep 2018 – Sep 2019)
    Specialized in Biomedical Data Science, Incomplete Data Analysis, Bayesian Theory, and Natural Language Processing.
  • BSc in Mathematics with Statistical Science (First), University College London (Sep 2015 – Aug 2018)
    Studied Mathematical Methods, Real Analysis, Bayesian Statistics, and Optimization Algorithms.

Professional Experience

  • Postdoctoral Researcher, Centre for Discovery Brain Sciences, University of Edinburgh (Feb 2024 – Present)
    Modeling neuronal connectivity using MAPseq data and Bayesian mixture models.
  • Mathematics Tutor, University of Edinburgh (Sep 2019 – Feb 2024)
    Tutored various courses, including Multivariate Analysis, Nonparametric Regression Models, and Statistical Computing.
  • Information Analyst, Public Health Scotland, National Health Service (Jan 2022 – Jul 2022)
    Developed time series algorithms and optimized R code for patient waiting times.

Research Interests

Jinlu Liu’s research interests include Bayesian nonparametric methods, statistical modeling of biological data, and computational algorithms for data analysis. He is particularly focused on applications in single-cell sequencing, neuronal connectivity, and healthcare data analysis. His work aims to develop efficient and reliable statistical methods to advance understanding in these fields.

Key Skills

  • Advanced proficiency in R, LaTeX, OpenBUGS, JAGS, and Stan
  • Intermediate skills in Python, SQL, and SAS
  • Expertise in Bayesian modeling, statistical computing, and data analysis
  • Proficient in using Slack, Zoom, and Microsoft Teams

📖 Publication Top Noted