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

Neelesh Shankar Upadhye | Probability and Statistics Award | Best Researcher Award

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Prof Dr. Neelesh Shankar Upadhye | Probability and Statistics Award | Best Researcher Award

Prof Dr. Neelesh Shankar Upadhye, Indian Institite Of Technology Madras, India

🎓 Prof. Dr. Neelesh Shankar Upadhye, renowned mathematician, earned his Ph.D. in Mathematics from IIT Bombay in 2009, with a remarkable 9.67/10. With a rich academic background including an M.Sc. from IIT Bombay and a B.Sc. from Wilson College, Mumbai, he has excelled in teaching and research. Currently a Professor at IIT Madras, he has significantly contributed to various courses, enhancing student learning experiences. With expertise in areas like Applied Statistics, he’s garnered praise for his innovative teaching methodologies. Upadhye’s dedication to education and quantitative research reflects his commitment to academic excellence. 📚

 

Publication Profile

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Academic Achievements:

Dr. Neelesh Shankar Upadhye boasts an impressive academic journey, culminating in a Ph.D. in Mathematics from IIT Bombay in 2009, where he also earned his M.Sc. and B.Sc. degrees. His doctoral thesis, supervised by Professor P. Vellaisamy, focused on “Compound Negative Binomial Approximations to Sums of Random Variables.” 📚

🏆 Professional Experience

With over a decade of teaching experience, Upadhye has held prestigious positions at IIT Madras, currently serving as a Professor. His expertise extends beyond academia, having worked as a Quantitative Researcher at Dolat Investments Ltd. 🏛️

🏅 Awards and Recognitions

Upadhye’s scholarly contributions have been acknowledged with numerous awards, including Senior and Junior Research Fellowships from CSIR and IIT Bombay. His qualifications and dedication underscore his status as a distinguished academician and researcher in the field of mathematics. 🌟

 

Research Focus

Dr. Neelesh Shankar Upadhye’s research primarily centers around probabilistic methods and statistical approximations, with a particular emphasis on compound distributions and their applications in diverse contexts. His work delves into advanced topics such as Stein operators, discrete approximations, and tail behavior analysis of random variables. Upadhye’s contributions extend to developing novel techniques for estimating parameters in time series models and exploring the tail behavior of functions of random variables. Through his publications, he consistently explores and advances the boundaries of statistical theory and its practical applications, fostering a deeper understanding of complex probabilistic phenomena. 📊

Publication Top Notes

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