John Rowen Miano | Statistics | Best Researcher Award

Published on by Academic Achievements

Mr. John Rowen Miano | Statistics | Best Researcher Award

Mr. John Rowen Miano | Statistics – Student at Cebu Technological University, Philippines

John Rowen Miano is an aspiring early-career researcher whose work sits at the intersection of computational biology and agricultural science. Based at Cebu Technological University, he is known for applying mathematical and computational tools to explore natural product chemistry, particularly in the field of agrochemical development. His independent research using molecular docking techniques to investigate plant-derived inhibitors has drawn academic interest and showcases his potential as a young innovator in sustainable agriculture. His initiative, curiosity, and analytical mindset distinguish him among his peers, making him a promising candidate for future academic and scientific excellence.

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Education:

John is currently pursuing his studies at Cebu Technological University under the Department of Mathematics and Statistics. His academic focus blends quantitative analysis with biological research, giving him a unique edge in computational studies. Through coursework and project-based learning, he has developed strong foundations in mathematics, statistics, and bioinformatics—skills that are critical for in silico research and predictive modeling. His education emphasizes both theoretical understanding and practical application, which is evident in his recent research outputs.

Experience:

John’s primary experience comes from his involvement as a student researcher at his university. During this time, he has conducted independent and guided research focused on plant-based antimicrobial agents. He has experience in molecular docking, virtual screening, database preparation, and software tools such as AutoDock and PyRx. His work has been presented at conferences and shared on academic platforms like Zenodo. He has collaborated with faculty for project feedback and scientific validation, and he is gradually building a network of fellow researchers within his institution.

Research Interests:

John’s research interests include molecular docking, phytochemistry, plant pathology, and the computational screening of bioactive compounds. He is particularly focused on identifying eco-friendly alternatives to synthetic agrochemicals by analyzing the inhibitory effects of natural phytochemicals against plant pathogens. His current study involves the evaluation of Euphorbia tirucalli compounds against Xanthomonas oryzae, the causative agent of bacterial leaf blight in rice. His broader interests also include artificial intelligence applications in drug discovery, sustainable agriculture, and the use of statistical models to predict pathogen resistance.

Awards:

As an emerging researcher, John has not yet received formal awards; however, he has been recognized at the university level for research presentation and participation. His poster presentation at a recent academic conference has gained early citations, demonstrating the relevance and growing academic attention toward his work. His nomination for the “Best Researcher Award” reflects both his existing achievements and the future potential that he holds as a developing scientific contributor.

Publications 📚:

  1. 🧪 Phytochemicals of Euphorbia tirucalli and their Inhibitory Potential against Xanthomonas oryzae Ddl Enzyme: An In silico Evaluation for Potential Agrochemical
    📅 Published: 2024 | Platform: Zenodo
    🔗 DOI: 10.5281/ZENODO.12183931
    📌 Cited by 2 articles

Conclusion:

John Rowen Miano is a highly motivated and intellectually capable young researcher. His contributions—although still at the early stage—exemplify innovation, relevance, and commitment to solving real-world agricultural problems. With a strong foundation in mathematical sciences and a growing body of work in computational biology, he is poised to become a key contributor to sustainable agrochemical discovery. His single-author research, proactive approach, and dedication to scientific exploration make him a strong nominee for the “Best Researcher Award” under an early-career or emerging talent category. He represents the next generation of researchers who merge computational power with natural science to address urgent agricultural and environmental challenges.

 

 

Sarra Leulmi | Probability and Statistics | Best Researcher Award

Published on by Academic Achievements

Dr. Sarra Leulmi | Probability and Statistics | Best Researcher Award

Class A lecturer | Université frères Mentouri, Constantine-1, Algeria | Algeria

Based on the detailed curriculum vitae provided for Mme Sarra Leulmi, here is an analysis of her strengths, areas for improvement, and a conclusion regarding her suitability for the Best Researcher Award:

Strengths

  1. Extensive Research Experience: Mme Leulmi has an impressive track record of research in the field of mathematics, particularly in nonparametric estimation and functional data. Her work is published in reputable journals such as Communications in Statistics-Theory and Methods and Journal of Siberian Federal University. This indicates a solid reputation in her field and substantial contribution to the academic community.
  2. Diverse Publications: Her extensive list of publications, including peer-reviewed journal articles and conference proceedings, highlights her active engagement in research and knowledge dissemination. This breadth of work showcases her commitment to advancing the field of applied mathematics and statistics.
  3. International and National Recognition: Mme Leulmi has participated in numerous international and national conferences, reflecting her recognition and involvement in the global research community. Her presentations cover a wide range of topics within her field, demonstrating her versatility and broad expertise.
  4. Supervision and Teaching Experience: She has supervised multiple master’s and doctoral theses, contributing to the development of future researchers. Her teaching roles span various levels, from high school to doctoral supervision, indicating her strong pedagogical skills and commitment to education.
  5. Research Projects: Mme Leulmi is involved in significant research projects, such as the PRFU project at the University of Constantine 1, which emphasizes her role in leading and contributing to impactful research initiatives.

Areas for Improvement

  1. Broader Impact Metrics: While Mme Leulmi’s publications and conference presentations are extensive, it would be beneficial to include metrics such as citation indices or impact factors of her published work. These metrics can provide a clearer picture of the impact and influence of her research.
  2. Interdisciplinary Research: Expanding her research to include interdisciplinary approaches or collaborations with other fields might enhance the applicability and relevance of her work. This could open new avenues for research and increase the broader impact of her contributions.
  3. Research Innovation: Emphasizing novel and cutting-edge research methods or applications could strengthen her profile. While her work is thorough and valuable, showcasing innovative approaches or breakthroughs might bolster her candidacy for prestigious awards.
  4. Public Engagement and Outreach: Increasing efforts in public outreach or engaging with broader audiences outside of academia could further highlight the societal impact of her research. This might include public lectures, science communication, or involvement in community-based projects.

Conclusion

Mme Sarra Leulmi appears to be a highly qualified candidate for the Best Researcher Award. Her extensive research background, significant publications, active participation in conferences, and supervisory roles illustrate a deep commitment to her field. Her work on nonparametric estimation and functional data has clearly made a substantial contribution to mathematics.

However, for an award of this nature, enhancing the visibility of her research impact and exploring interdisciplinary or innovative research opportunities could further strengthen her application. Overall, her strong academic credentials and substantial contributions to her field make her a strong contender for the award.

Short Biography 📚

Dr. Sarra Leulmi is a prominent mathematician specializing in nonparametric statistics and functional data analysis. Born on December 17, 1987, in Skikda, Algeria, Dr. Leulmi has made significant contributions to the field of statistical estimation, particularly in the context of censored and functional data. Her academic career is distinguished by her extensive research, numerous publications, and her role in advancing mathematical education.

Profile

SCOPUS

Education 🎓

Dr. Leulmi completed her Baccalauréat in Exact Sciences with a focus on Mathematics in 2005. She earned her Diplôme d’Études Supérieures (D.E.S.) in Mathematics with high honors in 2009 from Université Frères Mentouri, Constantine. She pursued further studies in Applied Mathematics, completing her Magistère with distinction in 2012. Dr. Leulmi achieved her Doctorate in Mathematics, specializing in Probability and Statistics, in 2018, with the thesis titled “Nonparametric Estimation for Functional Data”. She was awarded Habilitation Universitaire in Mathematics in 2021.

Experience 🏫

Dr. Leulmi has held various academic positions, starting as a Mathematics Teacher at Lycée Mustafa Ben Boulaid (2010-2012). She then served as a Maître Assistante in Bioinformatics and Sciences and Techniques Departments at Université Frères Mentouri. From 2012 to 2021, she progressed from Maître Assistante to Maître-Conférence classe ‘B’. Since 2021, she has been a Maître-Conférence classe ‘A’ at the same institution, where she teaches a range of courses in statistics and mathematics.

Research Interests 🔬

Dr. Leulmi’s research focuses on nonparametric estimation methods for functional and censored data, local linear regression, and statistical modeling of heterogeneous data. Her work aims to advance the understanding of statistical estimation techniques in complex data environments, including functional data and models with truncation and censoring.

Awards 🏆

Dr. Leulmi has been recognized for her contributions to mathematics and statistics through various academic accolades. Her research has been featured in numerous prestigious journals, highlighting her impactful work in the field.

Publications 📑

Leulmi, S., & Messaci, F. (2018). Local linear estimation of a generalized regression function with functional dependent data. Communications in Statistics-Theory and Methods, 47(23), 5795-5811. Link

Leulmi, S., & Messaci, F. (2019). A Class of Local Linear Estimators with Functional Data. Journal of Siberian Federal University. Mathematics & Physics, 12(3), 379-391. Link

Leulmi, S. (2019). Local linear estimation of the conditional quantile for censored data and functional regressors. Communications in Statistics-Theory and Methods, 1-15. Link

Leulmi, S. (2020). Nonparametric local linear regression estimation for censored data and functional regressors. Journal of the Korean Statistical Society, 49(1), 1-22. Link

Boudada, H., & Leulmi, S., Kharfouch, S. (2020). Rate of the Almost Sure Convergence of a Generalized Regression Estimate Based on Truncated and Functional Data. Journal of Siberian Federal University. Mathematics & Physics, 13(4), 1-12. Link

Leulmi, F., & Leulmi, S., Kharfouch, S. (2022). On the nonparametric estimation of the functional regression based on censored data under strong mixing condition. Journal of Siberian Federal University. Mathematics & Physics, 15(4), 523-536. Link

Boudada, H., & Leulmi, S. (2023). Local linear estimation of the conditional mode under left truncation for functional regressors. Kybernetika, 59(4), 548-574. Link

Leulmi, S. (2024). Asymptotic normality of local linear functional regression estimator based upon censored data. Communications in Statistics – Theory and Methods. DOI: 10.1080/03610926.2024.2378376