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:

Sadaf Khan | Probability and Statistics | Best Scholar Award

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Dr. Sadaf Khan | Probability and Statistics | Best Scholar Award

Dr. Sadaf Khan, The Islamia University of Bahawalpur, Pakistan

👩‍🏫 Dr. Sadaf Khan, a dedicated statistician, earned her PhD and MPhil in Statistics from Islamia University Bahawalpur. With a robust academic background including a Master’s and Bachelor’s in Statistics and Mathematics, she excels in statistical modeling and data analysis using tools like R, Mathematica, and SPSS. Dr. Khan has contributed significantly to research, publishing numerous articles in HEC-recognized journals. Her interests span Distribution Theory, Statistical Modeling, and Applied Statistics. Currently serving as a Visiting Lecturer at Islamia University Bahawalpur and Cholistan University of Veterinary & Animal Science, she aims to pursue a career in administration within an esteemed international organization. 📊

 

Publication profile

Scopus

Orcid

Education 🎓📚

PhD in Statistics, 2022
Islamia University Bahawalpur, Pakistan

Masters in Philosophy (Statistics), 2016
Islamia University Bahawalpur, Pakistan

Master’s in Statistics, 2010
Islamia University Bahawalpur, Pakistan

B.Sc (Mathematics, Statistics & Economics), 2008
Islamia University Bahawalpur, Pakistan

 

Research Focus

Sadaf Khan, a prolific researcher, focuses on statistical modeling and applications across various disciplines. Her research spans diverse areas such as climate-related data analysis using bivariate q-generalized extreme value distributions, COVID-19 and cancer data modeling with generalized Burr-Hatke models, and statistical inference under progressive type-II censoring schemes. She also contributes significantly to new lifetime models in biomedical and environmental data, including applications of new family distributions like the gamma power half-logistic model. Khan’s work combines theoretical developments with practical applications, showcasing her expertise in statistical methodologies and their real-world implications. 📊🔬

 

Publication Top Notes

Bivariate q- generalized extreme value distribution: A comparative approach with applications to climate related data

Modeling to COVID-19 and cancer data: Using the generalized Burr-Hatke model

KAVYA-MANOHARAN WEIBULL-G FAMILY OF DISTRIBUTIONS: STATISTICAL INFERENCE UNDER PROGRESSIVE TYPE-II CENSORING SCHEME

Analysis of COVID-19 and cancer data using new half-logistic generated family of distributions

The gamma power half-logistic distribution: theory and applications

A New Family of Lifetime Models: Theoretical Developments with Applications in Biomedical and Environmental Data

The Binomial–Natural Discrete Lindley Distribution: Properties and Application to Count Data

The Generalized Odd Linear Exponential Family of Distributions with Applications to Reliability Theory

A new extended gumbel distribution: Properties and application

The Minimum Lindley Lomax Distribution: Properties and Applications

An Extension of Karrup–King–Newton Index

New Modified Burr III Distribution, Properties and Applications

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.

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

 

Anu Sirohi | Survival Analysis | Best Researcher Award

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Dr. Anu Sirohi | Survival Analysis | Best Researcher Award

Dr. Anu Sirohi, Amity University, India

👩‍🏫 Dr. Anu Sirohi is an accomplished Assistant Professor in Statistics, with experience at prestigious institutions like Amity University and Sharda University. Holding a Ph.D. in Statistics, her research focuses on survival models for infant and child mortality in India. With expertise in Python, R, SPSS, and more, she excels in machine learning, regression, and statistical analysis. Passionate about education, she supervises Ph.D. candidates and imparts knowledge in advanced statistical techniques. Dr. Sirohi’s dedication to academia is matched only by her commitment to advancing statistical research and empowering future statisticians.

Publication Profile:

Scopus

Education:

👩‍🎓 Dr. Anu Sirohi completed her Ph.D. in Statistics from Banasthali University, Rajasthan, in September 2020. Her doctoral thesis, titled “Survival Models for Mortality Experience of Infant and Child Underfive: An Evidence from India,” showcases her dedication to understanding and addressing critical issues in public health. In June 2015, she achieved UGC-NET JRF accreditation in Population Studies, demonstrating her expertise in demographic research. Dr. Sirohi’s academic journey reflects her passion for exploring statistical methodologies to improve outcomes for vulnerable populations, particularly infants and children under five, in India.

Work Experience:

👩‍🏫 Dr. Anu Sirohi currently serves as an Assistant Professor in the Department of Statistics at Amity University, Noida, since May 10, 2023. Prior to this, she held a similar position at Sharda University, Greater Noida, starting from August 3, 2021, until May 9, 2023. With her expertise in statistics and commitment to academic excellence, Dr. Sirohi contributes significantly to shaping the minds of future statisticians. Her tenure at both institutions reflects her dedication to fostering learning environments where students can thrive and develop a profound understanding of statistical concepts and methodologies.

Research Focus:

🔬 Dr. Anu Sirohi’s research spans diverse areas within statistics and mathematical modeling, with a particular emphasis on public health and agricultural optimization. Her work delves into intricate topics such as proportional hazard regression models, frailty analysis, and dynamic programming approaches in agriculture. Dr. Sirohi’s expertise shines through in her exploration of collinearity effects on statistical estimators and the development of novel methodologies to address these challenges. With a keen focus on applications in infant mortality assessment, precision agriculture, and system reliability analysis, she contributes significantly to advancing knowledge and solutions in these critical domains, blending empirical evidence with mathematical rigor to drive impactful research outcomes.

Publication Top Notes:

Biased proportional hazard regression estimator in the existence of collinearity

AI-based Dynamic Programming Approach in Agriculture for Solving Intuitionistic Fuzzy Assignment Problems

Performance analysis of Precision Agriculture model using copula distribution and common cause Breakdown

Some r–k class proportional hazard regression models in the presence of collinearity: an evidence from Indian infant mortality

Parametric Frailty Analysis in Presence of Collinearity: An Application to Assessment of Infant Mortality

Stochastic Analysis of a Priority-Based Warm Standby System Working under k-out-of-n G Policy Using Multi-Dimensional Repair

Alternative approach to moments of order statistics from one-parameter Weibull distribution

On the structure of infant mortality using accelerated failure time (AFT) model: A comparative study based on national family health survey (NFHS) data in India