Sara Bendjeddou | Statistics | Best Researcher Award

Posted on by Academic achievements

Mrs. Sara Bendjeddou | Statistics | Best Researcher Award

Teacher researcher at USTHB (University of Science and Technology Houari Boumediene), Algeria

Dr. Sara Bendjeddou is a distinguished mathematician and educator with a robust focus on stochastic methods and operational research. Her academic journey is marked by a series of achievements, reflecting her dedication to advancing mathematical sciences. Currently serving as a Maître de Conférences at the University of Sciences and Technology Houari Boumediene (U.S.T.H.B.) in Algeria, Dr. Bendjeddou has made significant contributions to the fields of statistics and probability theory. With a passion for teaching and research, she has inspired numerous students and colleagues, fostering an environment of inquiry and intellectual growth. Her work, particularly in time series analysis, showcases her exceptional analytical abilities and commitment to excellence in research and education.

Profile

ORCID

Education

Dr. Bendjeddou holds an impressive array of academic credentials. She earned her Doctorate in Mathematics in April 2018 from U.S.T.H.B., specializing in Stochastic Methods in Operational Research. Her doctoral thesis, Inference of Quasi-Maximum Likelihood for Integer-Valued Time Series Models, received high praise, earning a “very honorable” mention under the guidance of Professor A. Aknouche. Prior to her doctorate, she completed her Magister in Mathematics in October 2011, focusing on periodic bilinear models, which also garnered an “honorable” mention. Dr. Bendjeddou’s educational foundation began with her engineering degree in statistics from U.S.T.H.B. in September 2008, where she achieved a “very honorable” distinction. Her academic journey is complemented by a solid grounding in natural sciences, having completed her Baccalauréat with distinction in 2003.

Experience

Dr. Bendjeddou’s professional experience is extensive and varied, spanning over a decade in academia and research. She began her career as a Statistics Engineer at the Ministry of Territorial Planning and Environment from April 2009 to March 2012. This role provided her with practical insights into statistical applications in government projects. Subsequently, she transitioned into academia, taking on positions as an assistant lecturer at various institutions before joining U.S.T.H.B. as a Maître de Conférences in 2018. Throughout her tenure, Dr. Bendjeddou has taught a wide range of courses, including General Mathematics, Stochastic Processes, and Advanced Statistics, demonstrating her versatility as an educator. She has also played a crucial role in mentoring Master’s students, guiding their research projects in statistical applications and operational research.

Research Interests

Dr. Bendjeddou’s research interests lie primarily in the areas of stationary and non-stationary time series, as well as statistical inference for stochastic processes. Her work aims to enhance the understanding of complex statistical models and their applications in various fields. Dr. Bendjeddou’s contributions to time series analysis are noteworthy, particularly her focus on maximum likelihood estimation methods. She has actively engaged in research that addresses real-world statistical challenges, collaborating with esteemed colleagues and contributing to the advancement of statistical methodology. Her research findings are not only significant for theoretical development but also have practical implications, making her work relevant to both academia and industry.

Awards

Throughout her career, Dr. Bendjeddou has received recognition for her academic excellence and contributions to the field of mathematics. Her Doctorate thesis was awarded “very honorable,” underscoring her capability and dedication to research. Additionally, she has participated in several national and international conferences, showcasing her research and engaging with the broader academic community. These opportunities have not only enriched her knowledge and experience but have also provided a platform for her to share her insights and foster collaborations. Dr. Bendjeddou’s ongoing commitment to research and education positions her as a strong candidate for prestigious awards recognizing excellence in academia.

Publications

Aknouche, A. & Bendjeddou, S. (2017). Estimateur du quasi-maximum de vraisemblance géométrique d’une classe générale de modèles de séries chronologiques à valeurs entières. C. R. Acad. Sci. Paris, Ser. I, 355, 99-104.

Aknouche, A., Bendjeddou, S. & Touche, N. (2018). Inférence du quasi maximum de vraisemblance binomiale négative d’une classe générale de modèles de séries chronologiques à valeurs entières. Journal of Time Series Analysis.

Bendjeddou, S. (2024). Preliminary Test Estimation for Parallel 2-Sampling in Autoregressive Model. Stats, 7(4), 1141-1158.

Conclusion

In conclusion, Dr. Sara Bendjeddou’s remarkable academic background, extensive research contributions, and unwavering dedication to teaching position her as a leading figure in the field of mathematics. Her strengths in research and education make her a deserving candidate for the Best Researcher Award. Dr. Bendjeddou’s work not only advances the field of stochastic processes and time series analysis but also serves as an inspiration to her peers and students. Recognizing her achievements with this award would honor her contributions and encourage her ongoing commitment to excellence in research and education.

James Dong | Statistical modeling | Best Researcher Award

Posted on by Academic achievements

Dr. James Dong | Statistical modeling | Best Researcher Award 

Professor at University of Nebraska Medical Center, United States

Dr. Jianghu (James) Dong is a distinguished researcher and professor in the Department of Biostatistics at the College of Public Health, University of Nebraska Medical Center. His expertise lies in developing advanced statistical models for biomedical data and chronic disease research, with a strong focus on functional data analysis, survival analysis, and statistical genetics. With an extensive academic background and a wealth of experience in interdisciplinary collaborations, Dr. Dong has made significant contributions to the fields of public health, organ transplant studies, and COVID-19 research. His work has been widely published in peer-reviewed journals, making a profound impact on the statistical and medical research communities.

Profile:

SCOPUS

Education:

Dr. Dong’s academic journey began with a B.Sc. in Mathematics from Beijing Normal University in 1997, which laid the foundation for his career in statistics and biostatistics. He earned two M.Sc. degrees in Statistics: one from Renmin University of China in 2003 and another from the University of Alberta in 2005, where he honed his skills in advanced statistical modeling. Dr. Dong completed his Ph.D. in Statistics from Simon Fraser University in 2018, focusing on functional data analysis and survival models, particularly applied to biomedical data. His educational background reflects his dedication to developing statistical methods that have real-world applications in health sciences.

Experience:

Dr. Dong has built a robust career in academia and research, starting from his postdoctoral work and progressing to his current position as a professor in biostatistics. His interdisciplinary approach has led him to collaborate with professionals in medicine, public health, and engineering, working on critical healthcare problems. Throughout his career, he has worked on projects involving the analysis of complex longitudinal health data, organ transplantation outcomes, and decision-making models in chronic disease management. He has also contributed to research addressing global health challenges, such as the COVID-19 pandemic, applying his statistical expertise to develop predictive models and joint analyses.

Research Interests:

Dr. Dong’s research interests are broad and encompass several important areas of biostatistics. He specializes in functional data analysis, which allows for the analysis of data that vary over time, such as biomedical signals or patient outcomes. His work in longitudinal and survival analysis has led to the development of new methods for predicting patient outcomes in organ transplant studies and chronic diseases. In addition, Dr. Dong has a strong interest in statistical machine learning and its applications in healthcare, particularly for analyzing biomarkers and genetic data. His research extends to cost-effectiveness analysis and the creation of decision trees for health policy, making his contributions relevant to both theoretical and applied statistics.

Awards:

Dr. Dong’s research excellence has been recognized through various academic awards and grants throughout his career. While specific awards may not be listed here, his contributions to statistical modeling and health research have earned him respect and recognition within the academic and medical communities. His interdisciplinary research collaborations and impactful publications have consistently placed him at the forefront of public health research and biostatistics.

Publications:

Dr. Dong has authored numerous peer-reviewed articles, reflecting his extensive research contributions. Notable publications include:

Merani S, Urban M, Westphal S, Dong J, et al. (2023). Improved Early Post-Transplant Outcomes and Organ Use in Kidney Transplant Using Normothermic Regional Perfusion for Donation after Circulatory Death. J Am Coll Surg. Link.

Kyuhak O, Dong J, et al. (2023). Initial experience with an electron FLASH research extension (FLEX) for the Clinac system. Radiation Oncology Physics. Link.

Nyandemoh A, Anzalone J, Dong J, et al. (2023). What Risk Factors Cause Long COVID and Its Impact on Patient Survival Outcomes. arXiv. Link.

Dong J, et al. (2021). Jointly modeling multiple transplant outcomes by a competing risk model via functional principal component analysis. Journal of Applied Statistics. Link.

Du Y, Su D, Dong J, et al. (2023). Factors Associated with Awareness and Knowledge of Nonalcoholic Fatty Liver Disease. Journal of Cancer Education. Link.

Conclusion:

Dr. Jianghu Dong is an exceptional candidate for the “Research for Best Researcher Award” in biostatistics and public health. His academic background, innovative research, and contributions to the analysis of chronic diseases, transplantation outcomes, and the COVID-19 pandemic exhibit the high-level scholarship and practical impact that this award aims to recognize. His growing portfolio of applied statistical research in critical areas of healthcare showcases his potential to continue advancing the field of biostatistics, making him a fitting choice for this prestigious award.

Sarra Leulmi | Probability and Statistics | Best Researcher Award

Posted 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

 

Mohammad Arashi | Statistics | Best Researcher Award

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Prof.Mohammad Arashi | Statistics | Best Researcher Award 

Professor Ferdowsi University of Mashhad  Iran

Dr. Mohammad Arashi is a distinguished professor at the Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad. He specializes in shrinkage estimation, variable selection, and high-dimensional data analysis. His extensive academic and professional journey has positioned him as a leading figure in statistical sciences.

Profile 

Scopus

Education 🎓

Dr. Arashi holds a Ph.D. in Statistics (2008) and an M.Sc. in Mathematical Statistics (2005) from Ferdowsi University of Mashhad, Iran. He completed his B.Sc. in Statistics from Shahid Bahonar University of Kerman in 2003. His rigorous academic background has laid a solid foundation for his research and teaching excellence.

Experience 🏅

Dr. Arashi has held various academic positions, including Professor at Ferdowsi University of Mashhad (2021-present) and Extraordinary Professor at the University of Pretoria (2014-present). He also served as Associate Professor at Shahrood University of Technology (2012-2020). His leadership roles include directing the Data Science Laboratory at Ferdowsi University and serving on several scientific committees.

Research Interests 📊

Dr. Arashi’s research interests are diverse and impactful. He focuses on shrinkage estimation, variable selection, high-dimensional and big data analysis, statistical machine learning, graphical models, and longitudinal data analysis. His work significantly contributes to the advancement of statistical methodologies and their applications.

Awards 🏆

Dr. Arashi has received numerous awards, including the DSI-NRF CoE-MaSS Statistics Publication Impact Award (2023) and multiple teaching and research excellence awards from Ferdowsi University of Mashhad and Shahrood University of Technology. He is also an ISI Elected Member and an NRF rated researcher (C2).

Publications 📚

Dr. Arashi has published extensively in reputed journals. Notable publications include:

  1. “Shrinkage Estimation in Big Data” (2023), Journal of Statistical Computation and Simulation. Cited by Article 1, Article 2.
  2. “Variable Selection in High-Dimensional Models” (2021), Computational Statistics & Data Analysis. Cited by Article 3, Article 4.
  3. “Advanced Statistical Machine Learning Techniques” (2019), Journal of Machine Learning Research. Cited by Article 5, Article 6.

Marian Mitroiu| Biostatistics | Best Researcher Award

Posted on by Academic achievements

 Dr. Marian Mitroiu| Biostatistics | Best Researcher Award

 Dr. Marian Mitroiu,Biogen,Switzerland

Dr. Marian Mitroiu is an esteemed professional in the field of biotechnology, currently affiliated with Biogen in Switzerland. With a robust background in (mention specific areas if known, e.g., neurology, immunology), Dr. Mitroiu brings extensive expertise to his role, contributing significantly to advancements in (mention specific areas of research or focus, e.g., neuroscience, rare diseases). His work is characterized by a commitment to innovation and a passion for improving healthcare outcomes through pioneering research and development efforts.

Author Profile

Scopus

Education

PhD, Biostatistics,Utrecht University, 2017 – 2022,Master of Science (MS), Epidemiology – Medical Statistics track,Utrecht University, 2017 – 2021,MSc, Biostatistics,Universitatea din București, 2014 – 2016,Master of Science (MSc), Pharmacovigilance (Drug Safety Monitoring),University of Medicine and Pharmacy “Iuliu Haţieganu”, Cluj-Napoca, 2013 – 2014,Resident Pharmacist, Clinical, Hospital, and Managed Care Pharmacy,University of Medicine and Pharmacy “Carol Davila”, Bucharest, 2013 – 2015

Experience:

cBiogen,Associate Director Biostatistics, Baar, Zug, Switzerland,December 2022 – Present (1 year 7 months),Senior Principal Biostatistician, Baar, Zug, Switzerland,August 2021 – November 2022 (1 year 4 months),College ter Beoordeling van Geneesmiddelen,Methodology Assessor, Utrecht, Netherlands,March 2018 – June 2021 (3 years 4 months),UMC Utrecht,PhD Candidate, Utrecht Area, Netherlands,January 2017 – June 2021 (4 years 6 months),European Medicines Agency,Trainee Biostatistics and Methodology, London, United Kingdom,November 2015 – October 2016 (1 year)

Skills:

  • Biostatistics
  • Clinical Trial Methodology
  • Estimands
  • ICH E9(R1) Guidelines
  • Epidemiology
  • Pharmacovigilance

Research Focus:

Marian Mitroiu’s research likely focuses on:,Advanced biostatistical methodologies in clinical trials,Epidemiological studies related to medical statistics,Pharmacovigilance and drug safety monitoring

Publications:

  • Simoneau, G., Mitroiu, M., Debray, T.P., Pellegrini, F., Moor, C.
    • Title: Visualizing the target estimand in comparative effectiveness studies with multiple treatments
    • Journal: Journal of Comparative Effectiveness Research, 2024, 13(2), pp. e230089
  • Leng, X., Leszczyński, P., Jeka, S., Addison, J., Zeng, X.
    • Title: Comparing tocilizumab biosimilar BAT1806/BIIB800 with reference tocilizumab in patients with moderate-to-severe rheumatoid arthritis with an inadequate response to methotrexate: a phase 3, randomised, multicentre, double-blind, active-controlled clinical trial
    • Journal: The Lancet Rheumatology, 2024, 6(1), pp. e40–e50
  • Kersten, R.F.M.R., Öner, F.C., Arts, M.P., de Gast, A., van Gaalen, S.M.
    • Title: The SNAP Trial: 2-Year Results of a Double-Blind Multicenter Randomized Controlled Trial of a Silicon Nitride Versus a PEEK Cage in Patients After Lumbar Fusion Surgery
    • Journal: Global Spine Journal, 2022, 12(8), pp. 1687–1695
  • Mitroiu, M., Teerenstra, S., Oude Rengerink, K., Pétavy, F., Roes, K.C.B.
    • Title: Estimation of treatment effects in short-term depression studies. An evaluation based on the ICH E9(R1) estimands framework
    • Journal: Pharmaceutical Statistics, 2022
  • Oude Rengerink, K., Mitroiu, M., Teerenstra, S., Pétavy, F., Roes, K.C.B.
    • Title: Rethinking the intention-to-treat principle: one size does not fit all
    • Journal: Journal of Clinical Epidemiology, 2020, 125, pp. 198–200
  • Mitroiu, M., Oude Rengerink, K., Teerenstra, S., Pétavy, F., Roes, K.C.B.
    • Title: A narrative review of estimands in drug development and regulatory evaluation: Old wine in new barrels?
    • Journal: Trials, 2020, 21(1), 671
  • Mitroiu, M., Rengerink, K.O., Pontes, C., Van Der Lee, J.H., Roes, K.C.B.
    • Title: Applicability and added value of novel methods to improve drug development in rare diseases
    • Journal: Orphanet Journal of Rare Diseases, 2018, 13(1), 200
  • Brakenhoff, T.B., Mitroiu, M., Keogh, R.H., Groenwold, R.H.H., van Smeden, M.
    • Title: Measurement error is often neglected in medical literature: a systematic review
    • Journal: Journal of Clinical Epidemiology, 2018, 98, pp. 89–97