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:

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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

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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) 🥛⚠️

Sunita Sharma | Statistics Award | Best Researcher Award

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Ms. Sunita Sharma | Statistics Award | Best Researcher Award

Ms. Sunita Sharma, G.B. Pant University of Agriculture and Technlogy Pantnagar, Uttarakhand, India

Dr. Sunita Sharma is a dedicated statistician specializing in Bayesian analysis and reliability engineering. She holds a Ph.D. in Statistics from GB Pant University, with expertise in the Weighted Exponential-Lindley distribution. With teaching experience at Surajmal Agarwal Girls Degree College and as a Teaching Assistant at GB Pant University, she has honed her skills in imparting statistical knowledge. Her research, focused on repairable multi-component systems, has been published in esteemed journals. Dr. Sharma’s commitment to academic excellence is underscored by her participation in conferences and workshops. She is passionate about advancing statistical methodologies for real-world applications.

 

Publication Top Notes

🎓 Education

Dr. Sunita Sharma earned her Ph.D. in Statistics from GB Pant University, focusing on Bayesian Statistics and Reliability Engineering. She holds a Master’s and Bachelor’s degree in Statistics from Kumaun University, excelling with high grades.👩‍🏫

Teaching Experience

As a Lecturer at Surajmal Agarwal Girls Degree College and a Teaching Assistant at GB Pant University, Dr. Sharma taught various Statistics courses, ensuring student understanding and progress.

🔬 Research Experience

Her Ph.D. thesis delved into Bayesian approaches for analyzing the reliability of repairable multi-component systems, showcasing her expertise in statistical modeling and computational methods.

🏅 Honors and Certificates

Dr. Sharma’s academic achievements include prestigious scholarships and certifications in data programming and statistical software.

Research Focus 📊

Dr. Sunita Sharma’s research primarily centers on Bayesian analysis and reliability engineering, particularly in the context of repairable multi-component systems. She specializes in utilizing the Weighted Exponential-Lindley distribution to model reliability characteristics, aiming to enhance accuracy and uncertainty quantification in estimation. Through her work, she explores the practical implications of Bayesian methods, comparing them with classical approaches. Her contributions span various domains, including the estimation of reliability in linear/circular k-out-of-n systems and multicomponent stress-strength models. Dr. Sharma’s dedication to advancing statistical methodologies reflects her commitment to addressing real-world challenges in system reliability assessment.

Rizwan Yousuf | Statistics Award | Best Researcher Award

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Dr. Rizwan Yousuf | Statistics Award | Best Researcher Award

Dr. Rizwan Yousuf, CHANDIGARH UNIVERSITY MOHALI PUNJAB INDIA, India

Dr. Rizwan Yousuf, a Ph.D. holder in Statistics, boasts expertise in statistical modeling, analysis, and regression techniques 📊. With a robust academic background from Sher-e-Kashmir University and University of Kashmir, he has delved into diverse statistical realms, including bio statistics and multivariate analysis 🧮. Driven by a passion for research, he has investigated structural properties and estimation techniques in his dissertations. Formerly a Field Investigator, he now imparts knowledge as an Assistant Professor at Chandigarh University, bringing his extensive experience to the forefront of academia 🎓

Publication Profile

Google Scholar

Education

Dr. Rizwan Yousuf’s academic journey reflects a steadfast dedication to statistical sciences 📊. He earned his Ph.D. in Statistics from Sher-e-Kashmir University of Agricultural Sciences & Technology of Jammu (SKUAST-J) in December 2021, preceded by an M.Sc. from the University of Kashmir, Srinagar, in January 2018. His scholarly pursuits began with a B.Sc. (3 years) degree from the University of Kashmir in May 2015. Dr. Yousuf’s research endeavors culminated in dissertations exploring diverse statistical domains, notably his Ph.D. thesis delving into the estimation and validation of linear and non-linear production functions through robust regression techniques 🎓.

Working Experience

Dr. Rizwan Yousuf embarked on his professional journey as a Field Investigator, traversing through the intricacies of data collection and analysis at Degree College Beerwah Budgam Kashmir from September 2022 to March 2023 📋. Transitioning seamlessly, he currently serves as an Assistant Professor of Mathematics at Chandigarh University since May 2023, contributing his statistical expertise to academia 🎓. His experiences span from fieldwork to classroom instruction, enriching his understanding of statistical methodologies and their practical applications. With a dedication to fostering knowledge and research, Dr. Yousuf continues to inspire and guide students in their academic pursuits.

Research Focus

Dr. Rizwan Yousuf’s research primarily focuses on statistical modeling and analysis 📊, with a particular emphasis on robust regression techniques. His work encompasses a wide range of applications, including the assessment of stress levels and coping mechanisms among married working women, economic ramifications of the COVID-19 pandemic, and modeling production functions in agricultural contexts. Dr. Yousuf’s expertise extends to outlier detection, estimation of productivity, and exploring novel regression estimators. Through his diverse research endeavors, he contributes significantly to advancing statistical methodologies and their practical implications across various domains.

 

Publication Top Notes 

  1. Assess the level of stress and coping level among married working women in Kashmir 📄 Cited by: 6 Year: 2022
  2. Unexpected Ramifications of Corona Pandemic Economic and Statistical Issues📄 Cited by: 6 Year: 2020
  3. Robust Model for the Quadratic Production Function in Presence of High Leverage Points📄 Cited by: 4 Year: 2021
  4. Modified Ratio-cum-Exponential Estimator for Appraising the Population Mean using Conventional and non-Conventional Parameters📄 Cited by: 3 Year: 2022
  5. ROBUST MODELS AND THEIR VALIDATION FOR MAIZE PRODUCTION IN JAMMU REGION OF JAMMU AND KASHMIR UNION TERRITORY 📄 Cited by: 1 Year: 2021
  6. Applications of Mitscherlich Baule function: a robust regression approach 📄 Year: 2024
  7. Detection of High Leverage Points in Regression Model in Apple Data 📄 Year: 2022
  8. Outlier Detection and Identification in Square root Production Function using Robust Regression 📄 Year: 2022
  9. Estimation and Validation of Robust Model for Productivity of Apple in UT of J&K using Cobb Douglas Method 📄 Year: 2021
  10. A New form of Ratio Estimator under Rank set Sampling📄 Year: 2021

 

Alberto Brini | Statistics | Best Researcher Award

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Dr. Alberto Brini | Statistics | Best Researcher Award

Dr. AlbertoBrini, Eindhoven University of Technology, Netherlands

Dr. Alberto Brini is a skilled biostatistician with a diverse research background in health outcomes and data analysis. With a PhD in Statistics/Data Science, he has collaborated internationally, including roles at McMaster University and Radboud University. 📊 His expertise spans patient-reported outcomes, omics data analysis, and statistical modeling for healthcare applications. Dr. Brini has taught and supervised numerous projects, demonstrating his commitment to education and mentorship. His contributions to statistical methods in (onco-)hematology and food safety research showcase his dedication to improving public health. 🩺🔬

Publication profile:

Scopus

Orcid

Google Scholar

 

Education:

Dr. Alberto Brini’s academic journey showcases a dedication to statistical excellence and interdisciplinary learning. 📚 He earned his PhD in Statistics/Data Science from Technische Universiteit Eindhoven, specializing in high-dimensional data analysis. Under the guidance of Prof. Edwin R. van den Heuvel and Dr. Jasper Engel, his thesis focused on innovative statistical methodologies. Prior to this, he obtained an MSc in Industrial and Applied Mathematics, delving into stochastic processes and longitudinal data analysis. His educational background also includes a joint MSc in Mathematical Modelling for Engineering from Politecnico di Torino, emphasizing networks and optimization. Dr. Brini’s stellar academic record culminated from his early education at Liceo Scientifico Statale Gregorio Ricci Curbastro in Lugo, Italy. 🌟

 

Experience:

Dr. Alberto Brini is an accomplished biostatistician with a rich portfolio spanning various international research projects. 🌍 Currently, he serves as a Biostatistician at Fondazione GIMEMA Franco Mandelli Onlus in Rome, Italy, leading statistical design and analysis in (onco-)hematology. As a Volunteer Researcher at McMaster University, Canada, he contributes to the analysis of the Canadian Longitudinal Study of Ageing (CLSA) within a global consortium. Previously, he provided statistical consultancy at Maxima Medisch Centrum, Netherlands, and conducted research at Radboud University and Wageningen Food Safety Research center. Dr. Brini’s expertise extends from industrial consortia to healthcare and food safety domains, reflecting his versatile skill set. 📊🔬

Honors and Awards:

Dr. Alberto Brini’s exceptional achievements extend beyond academia, reflecting his excellence in athletics and extracurricular endeavors. 🏅 He received grants and honors for academic excellence from institutions like Politecnico di Torino and Fondazione Alemanno Fantini e Margherita Orselli. Notably, he secured “ALSP Scholarships” at Eindhoven University of Technology. His prowess in athletics earned him numerous accolades, including multiple 1st place finishes in the Club National Championships for Combined Events. Dr. Brini’s diverse accomplishments also include participation in prestigious events such as the Enterprise European Business Games and the Jean Humbert Memorial World Cup for Schools. 🌟

 

Research Focus:

Dr. Alberto Brini’s research focus spans diverse areas, with a primary emphasis on statistical analysis of high-dimensional data in biomedical and healthcare contexts. 📊 His work includes studies on patient-reported outcomes in cardiac telerehabilitation programs, determinants of information needs in coronary artery disease patients, and financial toxicity in patients with hematologic malignancies. He also contributes to optimizing medical procedures, such as the surgical shortening of lengthened iliac arteries in endurance athletes. Dr. Brini’s expertise extends to missing data imputation, lifestyle behaviors, and multimorbidity patterns, reflecting his commitment to enhancing healthcare outcomes through advanced statistical methodologies. 🩺

 

Publication Top Notes:

  1. Predictors of non-participation in a cardiac telerehabilitation programme: a prospective analysis by HMCK R W M Brouwers, A Brini, R W F H Kuijpers, J J Kraal 📊 Cited by: 14* (2021)
  2. Short-and long-term results of operative iliac artery release in endurance athletesby M van Hooff, MMJM Hegge, MHM Bender, MJA Loos, A Brini, … 🏃‍♂️ Cited by: 4 (2022)
  3. Improved One-Class Modeling of High-Dimensional Metabolomics Data via Eigenvalue-Shrinkage by ERHJE A Brini, V Avagyan, RCH de Vos, JH Vossen 💡 Cited by: 3 (2021)
  4. The t linear mixed model: model formulation, identifiability and estimation by M Regis, A Brini, N Nooraee, R Haakma, ER van den Heuvel 🔍 Cited by: 3 (2019)
  5. Short-and long-term outcomes after endarterectomy with autologous patching in endurance athletes with iliac artery endofibrosis. by M van Hooff, FFC Colenbrander, MHM Bender, MMJA Loos, A Brini, … 🏃‍♀️ Cited by: 2 (2023)
  6. Determinants of information needs in patients with coronary artery disease receiving cardiac rehabilitation: a prospective observational study
  7. Surgical shortening of lengthened iliac arteries in endurance athletes: Short-term and long-term satisfaction
  8. Financial Toxicity and Health-Related Quality of Life Profile of Patients With Hematologic Malignancies Treated in a Universal Health Care System
  9. USING NETWORK ANALYSIS METHODS TO STUDY MULTIMORBIDITY PATTERNS
  10. Association of Financial Toxicity and Health-Related Quality of Life in Long-Term Survivors of Acute Promyelocytic Leukemia Treated within a Universal Healthcare System