Anyiam Kizito | Statistics | Best Researcher Award

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Dr. Anyiam Kizito | Statistics | Best Researcher Award

Senior Lecturer at Federal University Of Technology, Owerri, Nigeria.

Kizito Ebere Anyiam is a Senior Lecturer in the Department of Statistics at the Federal University of Technology, Owerri, Nigeria. He holds a PhD in Statistics from Nnamdi Azikiwe University, where his dissertation focused on new generalized exponentiated Weibull families of lifetime distributions. With a strong background in distribution theory, multivariate statistics, and statistical computing, Anyiam has made significant contributions to the field through numerous peer-reviewed publications, including works on statistical modeling and reliability analysis. He has also supervised over 30 undergraduate projects, many of which have been published in reputable journals. Beyond teaching, he has implemented innovative methods to enhance student engagement and practical experience in statistics. His professional affiliations include membership in the Nigerian Statistical Association and the Nigerian Mathematical Society, showcasing his active involvement in the academic community. With proficiency in data analysis software and a commitment to advancing statistical education, Anyiam is a respected figure in his field.

Profile:

Education

Kizito Ebere Anyiam holds a Ph.D. in Statistics from Nnamdi Azikiwe University, Awka, Nigeria, completed in 2023. His dissertation focused on the development of “New generalized Exponentiated Weibull Families of Lifetime Distributions with Unimodal and Bimodal Properties,” showcasing his research expertise in statistical distribution theory. Prior to his doctoral studies, he earned a Master’s degree in Statistics from Imo State University, Owerri, Nigeria, in 2008, which laid a solid foundation for his statistical knowledge and skills. In 2011, he completed a Postgraduate Diploma in Information Technology at the Federal University of Technology, Owerri, further enhancing his technical capabilities in data analysis. His academic journey began with a Bachelor’s degree in Statistics from Abia State University, Uturu, Nigeria, in 1995, where he first developed a passion for the field. This strong educational background equips him with the theoretical and practical skills essential for his current role as a Senior Lecturer and researcher.

Professional Experiences 

Kizito Ebere Anyiam is a Senior Lecturer in the Department of Statistics at the Federal University of Technology, Owerri, where he has been instrumental in enhancing the academic experience since 2009. He has developed and taught courses in Probability and Distribution Theory at both undergraduate and graduate levels, employing innovative teaching methods that actively engage students in fieldwork and industrial experiences. In his role as a project advisor, he has supervised over 30 undergraduate research projects, many of which have been published in peer-reviewed journals, showcasing his commitment to fostering research excellence. Additionally, Anyiam has served as the Undergraduate Industrial Experience Coordinator and Class Adviser, further contributing to the academic and professional development of his students. His earlier teaching experience as a Teaching Assistant at Federal Polytechnic Nekede laid the foundation for his strong educational background, which includes a PhD in Statistics and multiple publications in esteemed journals.

Research Interests

Kizito Ebere Anyiam’s research interests primarily revolve around distribution theory and reliability analysis, where he explores the properties and applications of various statistical distributions. His work includes developing new generalized families of lifetime distributions, particularly focusing on unimodal and bimodal properties. Additionally, he engages in multivariate statistics, investigating the complexities of multiple variables and their interactions. Anyiam is also keen on mathematical statistics and statistical computing, utilizing advanced statistical software to enhance data analysis and simulation techniques. His interest in applied probability further complements his research, allowing him to apply theoretical statistical concepts to real-world scenarios. Through his innovative research, Anyiam aims to contribute to the advancement of statistical methodologies and their practical applications across various fields, including engineering and biomedical sciences. His commitment to statistical education and research fosters a deeper understanding of statistical principles and their relevance in addressing contemporary challenges.

Research Skills 

Kizito Ebere Anyiam possesses a robust skill set in statistical research, encompassing distribution theory, reliability analysis, and multivariate statistics. His proficiency in statistical computing and simulation enables him to effectively analyze complex data sets using software such as R, Stata, and SPSS. Anyiam has demonstrated his expertise through the successful supervision of over 30 undergraduate projects, many of which have been published in peer-reviewed journals. His research focuses on new generalized lifetime distributions and their applications, showcasing his ability to innovate within the field. Moreover, he actively engages in collaborative research efforts, evidenced by his numerous publications in reputable journals. His strong foundation in mathematical statistics allows him to tackle real-world problems through applied probability and statistical modeling. Overall, Anyiam’s research skills, combined with his commitment to teaching and mentoring, position him as a valuable contributor to the field of statistics.

Award and Recognition 

Kizito Ebere Anyiam, a Senior Lecturer in the Department of Statistics at the Federal University of Technology, Owerri, has garnered significant recognition for his contributions to the field of statistics. He earned his PhD in 2023 from Nnamdi Azikiwe University, where his dissertation on generalized exponentiated Weibull families showcased innovative approaches to lifetime distributions. His research interests encompass distribution theory, reliability analysis, and statistical computing, leading to multiple peer-reviewed publications in reputable journals, such as Heliyon and the Journal of Modern and Applied Statistical Methods. Additionally, Anyiam’s commitment to education is evident through his development of innovative teaching methods, supervising over 30 undergraduate projects, many of which have been published. His active involvement in professional organizations, including the Nigeria Statistical Association and the Nigerian Mathematical Society, further emphasizes his dedication to advancing statistical research and education in Nigeria.

Conclusion

Kizito Ebere Anyiam is a strong candidate for the Best Researcher Award due to his impressive academic background, extensive teaching and supervisory experience, and significant contributions to statistical research. His commitment to student engagement and professional development further underscores his suitability for this recognition. By addressing areas for improvement, such as expanding his research scope and increasing his visibility in the academic community, Anyiam can further enhance his impact as a researcher and educator. Awarding him this honor would not only recognize his past achievements but also encourage his continued growth and contributions to the field of statistics.

Publication Top Notes
  1. A novel feature selection framework for incomplete data
  2. Iterative missing value imputation based on feature importance
  3. KNCFS: Feature selection for high-dimensional datasets based on improved random multi-subspace learning

 

 

Haike Lei | Health Statistics | Best Researcher Award

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Prof. Haike Lei | Health Statistics | Best Researcher Award

Offices director, Chongqing university cancer hospital, China.

Professor Haike Lei is a distinguished academic and researcher, currently serving as Deputy Chief Physician, Director of the Big Data Center, and Master’s Program Supervisor. His expertise spans data mining, statistical modeling, and big data management, evidenced by his nearly 40 published papers and over 10 patents and software copyrights. Professor Lei has played a pivotal role in establishing a comprehensive oncology big data research platform, which aggregates extensive patient data for in-depth medical research. His leadership is further demonstrated through his involvement in more than 10 national and municipal scientific research projects and his contributions as co-editor of a statistics textbook. His innovative approach and practical experience have significantly advanced the field, showcasing his profound impact and influence in both domestic and international research communities.

Profile
Education

Professor Haike Lei holds a distinguished educational background that has significantly contributed to his expertise in statistics, data mining, and big data management. He earned his Bachelor’s degree in Statistics from a prestigious institution, where he developed a strong foundation in statistical theories and methodologies. Building on this, he pursued a Master’s degree in the same field, deepening his knowledge in advanced statistical modeling and data analysis techniques. His academic journey culminated in a Doctorate, where he specialized in big data and its applications in healthcare. During his doctoral studies, Professor Lei conducted pioneering research that laid the groundwork for his later contributions to the establishment of oncology big data platforms and innovative statistical methods. This robust educational background has equipped him with the skills and insights necessary to excel in his current roles as Deputy Chief Physician and Director of the Big Data Center.

Professional Experience

Prof. Haike Lei is a distinguished academic and leader in the field of statistics and big data management. Currently serving as the Deputy Chief Physician and Director of the Big Data Center, he has spearheaded the development of a comprehensive oncology big data research platform, integrating nearly ten million pieces of patient data for advanced medical analysis. As a Master’s Program Supervisor, Prof. Lei is also dedicated to shaping the next generation of statisticians and data scientists. Over the past five years, he has significantly impacted the field through the publication of nearly 40 academic papers in prestigious journals and the acquisition of more than 10 invention patents and software copyrights. His leadership extends to presiding over numerous national and municipal scientific research projects and co-editing a professional textbook on statistics. Prof. Lei’s career exemplifies exceptional academic achievement, innovation, and a commitment to advancing research in data science and statistics.

Research Interest

Professor Haike Lei’s research interests lie at the intersection of data science and healthcare, with a particular focus on big data analytics, statistical modeling, and data mining. His work is centered on leveraging advanced statistical techniques to extract meaningful insights from large datasets, particularly in the context of oncology and medical research. Professor Lei is renowned for his innovative approach to managing and analyzing extensive patient data, having spearheaded the development of a comprehensive big data research platform for oncology. His research aims to improve diagnostic accuracy, treatment efficacy, and patient outcomes through sophisticated data-driven methods. Additionally, Professor Lei is deeply involved in exploring the latest technological advancements in data science, continuously integrating new methodologies to enhance the quality and impact of his research. His contributions significantly advance both theoretical and applied aspects of statistical science and big data management in the medical field.

Research Skills

Prof. Haike Lei demonstrates exceptional research skills in the domains of data mining, statistical modeling, and big data management. His expertise is reflected in his ability to innovate and apply cutting-edge statistical techniques to real-world problems. Prof. Lei has successfully led the development of a comprehensive oncology big data research platform, effectively managing and analyzing extensive patient data to drive forward medical research. His proficiency in statistical modeling and data analysis is further evidenced by his prolific publication record, with nearly 40 papers in esteemed journals. Additionally, his leadership in national and municipal scientific research projects highlights his capacity to coordinate complex studies and contribute to significant advancements in the field. Prof. Lei’s practical experience is complemented by his achievements in securing over 10 patents and software copyrights, showcasing his ability to translate theoretical research into tangible technological innovations.

Awards and Recognition

Yibo Wang possesses a robust set of research skills, particularly in the field of electrical engineering and energy systems. His expertise in stability analysis of distributed generation in cyber-energy systems is evidenced by his contributions to high-impact publications. Yibo is proficient in advanced analytical techniques, such as the Guardian Map Method, which he has applied to optimize parameter selection in complex energy systems. His ability to collaborate effectively with leading researchers and contribute to significant studies on virtual energy storage and multi-inverter systems demonstrates his strong teamwork and communication skills. Additionally, Yibo’s research is grounded in a deep understanding of both theoretical principles and practical applications, allowing him to develop innovative solutions for contemporary challenges in energy infrastructure. His technical proficiency, coupled with a commitment to advancing knowledge in his field, makes him a valuable asset in any research setting.

Conclusion

Yibo Wang is a promising candidate for the Best Researcher Award, particularly in the context of early-career researchers. His contributions to the field of electrical engineering, particularly in stability analysis and cyber-energy systems, are commendable. However, to strengthen his case for such an award, focusing on broadening his research impact, pursuing further professional development, and demonstrating independent research leadership would be beneficial. Overall, he is a strong contender with significant potential for future recognition.

Publications Top Notes

  1. Development and validation of a nomogram model for predicting venous thromboembolism risk in lung cancer patients treated with immune checkpoint inhibitors: A cohort study in China
    • Authors: Liang, G., Hu, Z., Xu, Q., Zhang, W., Lei, H.
    • Year: 2024
  2. The development of a prediction model based on random survival forest for the prognosis of non-Hodgkin lymphoma: A prospective cohort study in China
    • Authors: Li, X., Yang, Z., Li, J., Liu, Y., Lei, H.
    • Year: 2024
  3. A nomogram to predict the risk of venous thromboembolism in patients with colon cancer in China
    • Authors: Yang, Y., Zhan, J., Li, X., Lei, H., Chen, X.
    • Year: 2024
    • Citations: 1
  4. Development and validation of a multi-parameter nomogram for venous thromboembolism in gastric cancer patients: a retrospective analysis
    • Authors: Zhou, H., Lei, H., Zhao, H., Luo, L., Li, F.
    • Year: 2024
  5. Treatment-Related Lymphopenia is Possibly a Marker of Good Prognosis in Nasopharyngeal Carcinoma: a Propensity-Score Matching Analysis
    • Authors: Weng, K.-G., Lei, H.-K., Shen, D.-S., Wang, Y., Zhu, X.-D.
    • Year: 2024
  6. Antibody responses to SARS-CoV-2 Omicron infection in patients with hematological malignancies: A multicenter, prospective cohort study
    • Authors: Li, J., Liu, Y., Wei, X., Wu, Y., Liu, Y.
    • Year: 2023
    • Citations: 1
  7. Comparison of survival outcomes between clinical trial participants and non-participants of patients with advanced non-small cell lung cancer: A retrospective cohort study
    • Authors: Jiang, Q., Yue, X., Lei, H., Li, Y., Chen, X.
    • Year: 2023
  8. Development and validation of nomogram prognostic model for predicting OS in patients with diffuse large B-cell lymphoma: a cohort study in China
    • Authors: Li, X., Xu, Q., Gao, C., Wang, Y., Lei, H.
    • Year: 2023
    • Citations: 1
  9. Development and validation of prediction model for overall survival in patients with lymphoma: a prospective cohort study in China
    • Authors: Li, X., Chen, Y., Sun, A., Liu, Y., Lei, H.
    • Year: 2023
    • Citations: 2
  10. Nomogram prediction for the risk of venous thromboembolism in patients with lung cancer
    • Authors: Lei, H., Tao, D., Zhang, N., Xie, Y., Wang, Y.
    • Year: 2023
    • Citations: 6

 

 

Dian Kusumaningrum | Statistics | Best Researcher Award

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Mrs. Dian Kusumaningrum | Statistics | Best Researcher Award

Distinguished Professor of Biomaterials of Tecnologico de Monterrey, Mexico.

Dian Kusumaningrum is a distinguished researcher and lecturer specializing in statistics and data science. She holds a Bachelor’s and Master’s degree in Statistics from IPB University and is currently pursuing a PhD in Statistics and Data Science at the same institution. Her impressive achievements include multiple research grants and scholarships, such as the SEARCA PhD Full Scholarship and SEARCA Conference Grants. Dian’s research focuses on crop insurance, Bayesian methods, and agricultural statistics, contributing significantly to the development of innovative models for agricultural sustainability. Her extensive experience includes roles as a researcher, consultant, and lecturer at prominent institutions. She has also presented at numerous international conferences and published extensively in respected journals. Dian’s dedication to advancing statistical methods and their applications in agriculture underscores her suitability for the Best Researcher Award.

Profile
Education

Dian Kusumaningrum’s educational background is marked by a strong foundation in statistics and data science. She earned her Bachelor’s degree in Statistics from IPB University, Faculty of Mathematics and Natural Sciences, in 2004. Continuing her academic journey, she completed her Master’s degree in Statistics from the same institution in 2010, supported by a scholarship from the Ministry of Education, Indonesia. Currently, she is pursuing her PhD in the Department of Statistics and Data Science at IPB University, a program which she started in 2020. Throughout her academic career, she has been recognized for her dedication and excellence, receiving various scholarships and research grants, including the SEARCA PhD Full Scholarship and SEARCA Research Grant. Her studies have consistently focused on advancing statistical methodologies and their applications, contributing to her expertise in actuarial science and data analysis.

Professional Experience

Dian Kusumaningrum has extensive professional experience in both academia and research. She began her career as a Co-Broadcaster at RRI PRO 1 Bogor and later served as a private English teacher and back data cleaner at AC Nielsen. Her expertise in statistics and data science was honed through various roles, including as a Research Assistant at UNESCAP-CAPSA and a lecturer at multiple institutions, such as Bogor Agriculture University and Prasetiya Mulya University. Kusumaningrum also held significant positions as a Statistician Lead at DAFEP Research and for various research collaborations with organizations like the World Bank and USAID. She has managed numerous research projects, including crop insurance policy development and statistical modeling for energy and financial sectors. Her contributions extend to leading statistical and actuarial product development, reflecting her significant impact on statistical research and applied data science.

Research Interest

Dian Kusumaningrum’s research interests are centered around the application of statistical methodologies to address complex problems in agriculture, economics, and risk management. Her work extensively explores the development and optimization of crop insurance policies, particularly focusing on Bayesian approaches and generalized linear mixed models to enhance agricultural productivity and farmer income sustainability. Kusumaningrum’s research also delves into the integration of climate change and smart agriculture into educational curricula, aiming to improve understanding and adaptation strategies. She has a keen interest in applying statistical and actuarial models to analyze and mitigate risks associated with agricultural practices and economic sustainability. Her commitment to advancing knowledge in these areas is demonstrated through her involvement in various national and international research projects and conferences, contributing to the development of innovative solutions for pressing challenges in agriculture and risk management.

Research Skills

Dian Kusumaningrum demonstrates exceptional research skills across a variety of statistical and data science domains. Her expertise spans Bayesian methods, small area estimation, and actuarial modeling, particularly in the context of crop insurance and food security. With extensive experience in data analysis and statistical consulting, Dian has successfully led numerous research projects, including those with international collaborations such as the World Bank and UNESCAP-CAPSA. Her proficiency in developing and applying complex models, such as the Bayesian Beta mixed regression model and generalized linear mixed models, highlights her advanced analytical capabilities. Additionally, Dian’s ability to present and publish her findings in reputable journals and conferences showcases her strong communication skills and her commitment to advancing knowledge in her field. Her diverse experience in teaching, research mentorship, and consultancy further underscores her comprehensive skill set and dedication to impactful research.

Awards and Recognition

Dian Kusumaningrum has garnered notable recognition throughout her academic and professional career. Her achievements include being awarded the Master Degree Program Scholarship and Research Grant Awardee from the Ministry of Education Indonesia, showcasing her commitment to advancing research in statistics. She has also received the SEARCA PhD Full Scholarship and multiple SEARCA Conference Grants, reflecting her excellence in academia and research. Her contributions to crop insurance development and statistical methodologies have been recognized through grants and awards from READI and STEM Prasetiya Mulya. Additionally, her international engagements, including the JASSO SUIJI Exchange Program and participation in various prestigious conferences, underline her global impact. Kusumaningrum’s extensive involvement in research and education is further highlighted by her numerous presentations and publications, cementing her reputation as a leading figure in her field.

Conclusion

Dian Kusumaningrum is a highly qualified candidate for the Research for Best Researcher Award. Her extensive educational background, notable achievements, and substantial contributions to research and teaching make her a standout candidate. By broadening her research scope and increasing international collaborations, she could further strengthen her position as a leading researcher in her field. Her commitment to both academic excellence and practical applications in statistics and data science reflects a well-rounded and impactful career.

Publications Top Notes

  1. Beta four parameter GLMM approach to evaluate paddy productivity
    • Authors: Kusumaningrum, D., Wijayanto, H., Notodiputro, K.A., Ardiansyah, M., Kurnia, A.
    • Year: 2024
  2. Comparison of Multi-satellite Rainfall Data in Runoff Model
    • Authors: Harsanto, P., Kusumaningrum, D., Legono, D., Rahardjo, A.P., Jayadi, R.
    • Year: 2024
  3. Area Yield Index and Multi-peril Crop Insurance Model Profitability Analysis
    • Authors: Suprajetno, R.I., Kusumaningrum, D., Sutomo, V.A., Anisa, R.
    • Year: 2023
  4. Pure Premium Calculation of Dry Weather-Based Insurance for Wonogiri Farmers
    • Authors: Paramita, A., Sari, F., Kusumaningrum, D., Sutomo, V.A.
    • Year: 2023
  5. Net Premium Determination of Reversionary Annuity Using Markovian Approach
    • Authors: Suardijaya, I.K.A., Kusumaningrum, D., Tobing, P.L., Tauryawati, M.L.
    • Year: 2023
  6. Premium Calculation for Paddy Plant Business Insurance (PPBI) and Microcredit Integration Program
    • Authors: Aldyan, K., Kusumaningrum, D., Hidayat, A.S.E., Sutomo, V.A.
    • Year: 2023
  7. Paddy Farmers Profiling and Estimation of Willingness to Pay Towards the AUTP and KUR Integration Program
    • Authors: Novita, L., Kusumaningrum, D., Saraswati, D.
    • Year: 2023
  8. Bayesian Premium Calculations of Multiperil Crop Insurance (MPCI) Based on Bayesian Beta Mixed Regression Model
    • Authors: Kusumaningrum, D., Sundari, M., Kurnia, A.
    • Year: 2022
  9. Alternative area yield index based crop insurance policies in Indonesia
    • Authors: Kusumaningrum, D., Anisa, R., Sutomo, V.A., Tan, K.S.
    • Year: 2021
    • Citations: 3