Waqas Munir | Statistics | Best Researcher Award

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Waqas Munir | Statistics | Best Researcher Award

Mr Waqas Munir, Quaid-i-Azam University, Islamabad , Pakistan

Based on Mr. Waqas Munir’s academic and professional profile, he stands out as a suitable candidate for the Best Researcher Award. Here’s a breakdown of his qualifications and achievements, structured in a title-paragraph format:

Publication profile

scopus

Educational Background

Mr. Waqas Munir holds a Master of Philosophy in Statistics from Quaid-i-Azam University, completed between 2014 and 2017. His thesis, titled “New Cumulative Sum Control Charts for Monitoring Process Mean and Process Dispersion,” showcases his ability to contribute significantly to statistical methodologies. Additionally, he completed his Master of Science in Statistics at the same institution from 2011 to 2013, providing him with a strong foundational knowledge in various statistical concepts.

Life Philosophy

Mr. Munir embodies a profound enthusiasm for research and continuous learning. He recently completed his postgraduate studies and is now pursuing a Ph.D. program in Statistics. His strong foundation in statistical methodologies and programming fuels his ambition to uncover insights within the realm of Statistics and elevate his educational attainment.

Teaching Experience

His teaching experience is notable, having served at Fast University, Islamabad since Fall 2019, and at Quaid-i-Azam University, Islamabad since Spring 2023. This experience not only demonstrates his commitment to education but also highlights his role in shaping the next generation of statisticians.

Research Interests

Mr. Munir’s research interests encompass a range of statistical fields, including Statistics Process Control, Machine Learning, and Applied Statistics. His focus on practical applications of statistics positions him as a forward-thinking researcher in the field.

Publications

Mr. Munir has made substantial contributions to academic literature, with several publications in reputable journals. Notable articles include:

  • “New cumulative sum charts for monitoring process variability” (2017) – This publication explores innovative approaches to process control, demonstrating Mr. Munir’s expertise in cumulative sum (CUSUM) charts.
  • “Improved CUSUM charts for monitoring process mean” (2018) – Co-authored with Haq A, this work enhances existing methodologies in process monitoring, reflecting his ability to improve statistical tools.
  • “New CUSUM and Shwhart-CUSUM charts for monitoring the process mean” – This research further establishes his focus on improving statistical methodologies, contributing to advancements in quality control.

Publication Top Notes

New CUSUM and EWMA charts with simple post signal diagnostics for two-parameter exponential distribution

New CUSUM and Shewhart-CUSUM charts for monitoring the process mean

Improved CUSUM charts for monitoring process mean

New cumulative sum control charts for monitoring process variability

Conclusion

In summary, Mr. Waqas Munir’s academic qualifications, teaching experience, research interests, and impactful publications position him as a strong candidate for the Best Researcher Award. His commitment to advancing the field of Statistics through rigorous research and education exemplifies the qualities sought in an award recipient.

 

Ilya Lipkovich | Statistics/Real world analytics | Best Researcher Award

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Ilya Lipkovich | Statistics/Real world analytics | Best Researcher Award

Sr Research Advisor at Eli Lilly and Company,United States.

Dr. Ilya Lipkovich is a distinguished statistician and Sr. Research Advisor at Eli Lilly and Company. With over 20 years of experience in statistical consulting and pharmaceutical research, he has significantly contributed to the fields of missing data, subgroup identification, and observational data analysis. He has authored numerous papers, tutorials, and book chapters, and developed innovative statistical methodologies.

Profile:

orcid

Education:

  • Ph.D. in Statistics – Virginia Polytechnic Institute and State University, USA, 2002
  • M.S. in Statistics – University of Delaware, USA, 1998
  • B.S. in Statistics and Economics – Almaty Institute of National Economy, Kazakhstan, 1985

Experience :

  • Eli Lilly and Company (2018–Present): Sr. Research Advisor in the Real World Analytics team, leading the development of analytic solutions and best practices for value-based contracting, predictive analytics, and bias control in observational research.
  • IQVIA (2012–2018): Principal Scientific Advisor, leading the Data Mining and Statistical Analysis group.
  • Eli Lilly and Company (2002–2012): Principal Research Scientist, project/team lead for Data Mining of Advanced Analytics group.
  • Virginia Polytechnic Institute and State University (1998–2002): PhD student, consultant, and instructor.
  • DuPont Company (1997–1998): Intern at Quality Management and Technology Center.
  • University of Delaware (1995–1997): Graduate student and consultant.
  • World Bank (1997–1999): Short-term consultant, developed statistical software for risk analysis.
  • ICMA (1994–1995): Data analyst and software expert in Kazakhstan.
  • StatEx Ltd. (1990–1993): Statistical programmer in Kazakhstan.
  • Institute of National Economy (1989–1995): Data analyst and research assistant in Kazakhstan.

Research Interest :

  • Causal Inference: Developing methods to infer causal relationships from data.
  • Subgroup Identification: Creating tools to identify patient subgroups with enhanced treatment effects.
  • Observational Data Analysis: Analyzing real-world data to draw meaningful conclusions.
  • Missing Data Analysis: Addressing challenges in data analysis with incomplete datasets.
  • Advanced Analytics in Healthcare: Applying innovative statistical methods to improve healthcare outcomes.

Awards :

  • Outstanding Statistical Application Award from the American Statistical Association.
  • Excellence in Research Award from Eli Lilly and Company.

Publications :

Dr. Lipkovich has authored numerous papers, tutorials, and book chapters on statistical methodologies. Some of his notable publications include:

Muhammad Shakir Khan | Statistics Award | Best Researcher Award

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Dr. Muhammad Shakir Khan | Statistics Award | Best Researcher Award

Dr. Muhammad Shakir Khan, Islamia College Peshawar, Pakistan

Dr. Muhammad Shakir Khan is a seasoned Statistician, Researcher, and Data Analyst with over 15 years of professional experience. He holds a PhD in Statistics from Islamia College Peshawar and specializes in regression analysis, statistical modeling, and machine learning. As the Deputy Director (Statistics & Economics) at the Livestock & Dairy Development Department, Khyber Pakhtunkhwa, he oversees data management, research guidance, and project planning. Dr. Khan has published extensively in reputed journals and led numerous research projects in livestock and genetics. Proficient in R, Python, SPSS, and other analytical tools, he is passionate about advancing his skills and knowledge.

Publication Profile

Orcid

Professional Experience 🏢

Muhammad Shakir Khan, a seasoned Statistician, Researcher, and Data Analyst, boasts 15 years of experience in statistical analysis and research. He currently serves as Deputy Director (Statistics & Economics) at the Livestock & Dairy Development Department, Khyber Pakhtunkhwa, Pakistan, where he oversees the statistics section, guides researchers, and manages data for policy formulation. His previous roles include Statistical Officer and Statistical Assistant, focusing on data analysis, research, and project management.

Academic Qualifications 🎓

Ph.D. in Statistics (2018-2024): Islamia College Peshawar

M.Phil in Applied Statistics (2015-2017): Islamia College Peshawar

M.Sc. in Statistics (2007-2008): University of Peshawar

B.Sc. (2004-2006): University of the Punjab

Research Focus

Muhammad Shakir Khan’s research focuses on statistical modeling and data analysis, particularly in linear regression and its applications. He specializes in handling multicollinearity through ridge estimators and penalized regression techniques. His work includes applying these methods to medical, financial, and demographic data. He has contributed significantly to understanding statistical methodologies through simulations and practical applications. His expertise extends to machine learning, bootstrap methods, and advanced data analytics. His publications span various topics, including comparative performance analysis in zoology and food safety, showcasing his versatile application of statistical tools.

Publication Top Notes

  1. “On the estimation of ridge penalty in linear regression: Simulation and application” (2024, Kuwait Journal of Science, DOI: 10.1016/j.kjs.2024.100273) 📊🔬
  2. “On some two parameter estimators for the linear regression models with correlated predictors: Simulation and application” (2024, Communications in Statistics – Simulation and Computation, DOI: 10.1080/03610918.2024.2369809) 📈📚
  3. “On the performance of two-parameter ridge estimators for handling multicollinearity problem in linear regression: Simulation and application” (2023, AIP Advances, DOI: 10.1063/5.0175494) 📉🧮
  4. “Comparative Performance of Jersey Sired Calves from Achai Dams and Azakheli Buffalo Calves Fed with Milk Replacer” (2018, Pakistan Journal of Zoology, DOI: 10.17582/journal.pjz/2018.50.5.sc7) 🐄🍼
  5. “Determination of Aflatoxin M1 in Raw Milk for Human Consumption in Peshawar, Pakistan” (2015, Pakistan Journal of Zoology, WOS:000357140700037) 🥛⚠️