Sohail Ahmad | Applied Statistics | Best Researcher Award

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Dr. Sohail Ahmad | Applied Statistics | Best Researcher Award

Dr. Sohail Ahmad | Quaid e Azam University Islamabad | Pakistan

Dr. Sohail Ahmad is an emerging researcher specializing in causal inference and the application of deep learning techniques to statistical modeling. His work focuses on directed acyclic graphs (DAGs) for uncovering causal relationships in observational data, with applications in epidemiology, public health, and healthcare policy evaluation. He integrates transformer-based models and variational autoencoders for counterfactual reasoning, treatment effect estimation, and personalized medicine using electronic health records. Dr. Ahmad earned his Ph.D. in Applied Statistics from Central South University, China, and holds an MPhil and Bachelor’s degree in Statistics from Quaid-i-Azam University, Pakistan. He has published extensively in high-impact journals, including Applied Intelligence, Scientific Reports, and AIMS Mathematics. His professional experience includes roles as a visiting lecturer and data analyst, alongside participation in international conferences and workshops. He has earned multiple professional certifications in causal inference and data science and received scholarships and national awards for academic excellence, reflecting his dedication, innovation, and growing impact in statistical research.

Publication Profile

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Education

Dr. Sohail Ahmad has pursued a comprehensive academic journey in the field of statistics, demonstrating a strong foundation in both theoretical and applied aspects of the discipline. He earned his Ph.D. in Applied Statistics from Central South University, China, where his research focused on integrating deep learning and statistical approaches for causal inference, exploring innovative methods to model complex relationships in observational data. Prior to his doctoral studies, he completed an MPhil in Statistics at Quaid-i-Azam University, Islamabad, Pakistan, where his thesis addressed the estimation of distribution functions under various sampling schemes, contributing to methodological advancements in survey analysis. Dr. Ahmad began his academic career with a Bachelor’s degree in Statistics from the same university, developing a generalized exponential-type estimator for population mean using auxiliary attributes, which laid the groundwork for his expertise in sampling theory and statistical estimation. His education reflects a consistent trajectory of rigorous research training, analytical skills, and specialization in modern statistical and machine learning techniques.

Experience

Dr. Sohail Ahmad has gained valuable academic and professional experience in the field of statistics through roles in teaching, research, and data analysis. He served as a visiting and contract lecturer at the Statistics Department of Government Post Graduate Jahanzeb College in Swat, Pakistan, where he was involved in delivering lectures, guiding students, and supporting curriculum development, fostering both theoretical understanding and practical application of statistical concepts. Prior to this, he worked as a teaching assistant and data analyst at the Department of Statistics, Quaid-i-Azam University, Islamabad, under the supervision of Prof. Dr. Javid Shabbir. In this role, Dr. Ahmad contributed to academic research projects, assisted in data management and statistical analyses, and supported faculty in developing research methodologies and practical solutions for real-world problems. His experience demonstrates a combination of teaching excellence, research proficiency, and analytical skills, highlighting his capability to contribute meaningfully to both educational and research-oriented environments in statistics.

Professional Certifications

Dr. Sohail Ahmad has actively enhanced his expertise in statistics, data science, and causal inference through a series of professional certifications and online courses from prestigious institutions. He completed advanced courses in causal inference from Columbia University, equipping him with in-depth knowledge of graphical models, counterfactual reasoning, and modern techniques for analyzing observational data. In addition, he pursued data science certifications from IBM, including foundational and applied courses in data analysis, tools, and methodologies, which strengthened his practical skills in programming, data management, and predictive modeling. Dr. Ahmad also completed a COVID-19 contact tracing course through Johns Hopkins University, reflecting his commitment to applying statistical and analytical skills to real-world public health challenges. Complementing these achievements, he participated in the 90th IKECEST Training Program for Silk Road Engineering Science and Technology Development, and an advanced Excel course focusing on data analysis and dashboard creation. These certifications highlight his dedication to continuous learning and professional growth

Awards

Dr. Sohail Ahmad has been recognized for his academic excellence and active participation in extracurricular activities throughout his educational journey. He received a prestigious scholarship from Quaid-i-Azam University, Islamabad, in recognition of his outstanding academic performance and dedication to statistical research. He was awarded a Prime Minister Laptop on merit, honoring his achievements as a distinguished student. Beyond academics, Dr. Ahmad has actively engaged in extracurricular and leadership activities that demonstrate his broader interests and commitment to personal development. He participated in the National Youth Carnival-17 at Quaid-i-Azam University, which provided a platform for networking, collaboration, and skill enhancement among young leaders. Additionally, he took part in the Peace Model United Nations (MNU) and the Youth Counselling Summit, reflecting his interest in global issues, diplomacy, and youth development. These awards and participations highlight his well-rounded profile, combining academic rigor with social engagement and leadership potential.

Research Focus

Dr. Sohail Ahmad’s research primarily focuses on the intersection of statistical methodology and modern machine learning techniques, with a strong emphasis on causal inference and estimator development. His work on generalized exponential-type and unbiased ratio estimators, as well as population distribution function estimation using auxiliary information, reflects a deep expertise in survey sampling, finite population analysis, and statistical estimation. In parallel, he explores advanced causal inference frameworks by integrating transformer-based deep learning models and variational autoencoders to estimate individual treatment effects and model latent confounders, particularly for applications in health and social sciences. This combination of classical statistical theory and cutting-edge machine learning allows him to address complex real-world problems, including treatment effect estimation, predictive modeling using electronic health records, and methodological advancements for observational studies. Dr. Ahmad’s publications demonstrate a consistent focus on improving the accuracy, interpretability, and applicability of statistical models, positioning him at the forefront of research in applied statistics and causal machine learning.

Publication Top Notes

A generalized exponential-type estimator for population mean using auxiliary attributes
Years: 2021
Citations: 27

Finite population distribution function estimation using auxiliary information under simple random sampling
Years: 2021
Citations: 16

A simulation study: An enhanced generalized class of estimators for estimation of population proportion using twofold auxiliary attribute
Years: 2023
Citations: 11

An improved family of unbiased ratio estimators for a population distribution function
Years: 2025
Citations: 6

Evaluation of agricultural wastes as a sustainable carbon source for the production of β-glucosidase from Bacillus stercoris, its purification and characterization
Years: 2023
Citations: 4

TV-CCANM: a transformer variational inference in confounding cascade additive noise model for causal effect estimation
Years: 2025
Citations: 1

Conclusion

Dr. Sohail Ahmad exhibits significant strengths in both methodological development and real-world application of statistical and machine learning approaches for causal inference. His contributions to the integration of deep learning with causal frameworks demonstrate originality and forward-thinking vision, especially in health-related research. While there is room for growth in terms of leadership visibility and research independence, his trajectory suggests a strong upward path. Given his current achievements, academic promise, and global engagement, Dr. Ahmad is indeed a suitable candidate for the Research for Best Researcher Award, representing both current excellence and future potential in advancing causal inference and applied statistics.

Adam Kapelner | Statistics | Best Faculty Award

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Prof. Adam Kapelner | Statistics | Best Faculty Award

Prof. Adam Kapelner, Queens College CUNY, United States

📊 Prof. Adam Kapelner is an Associate Professor of Mathematics at Queens College, CUNY, where he also directs the Undergraduate Data Science and Statistics Program. He earned his Ph.D. in Statistics from the Wharton School, University of Pennsylvania (2014). His research focuses on experimental design, randomization, machine learning, and statistical software. He has been a visiting scholar at The Technion, Israel. Recognized for excellence in teaching and research, he received the President’s Award for Teaching (2023) and an NSF Graduate Fellowship. He actively publishes and speaks at international conferences. 🏆📈🎓

Publication Profile

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Academic Background

Prof. Adam Kapelner holds a Ph.D. in Statistics (2014) from the Wharton School, University of Pennsylvania, where he was advised by Abba Krieger and Edward George. He also earned an A.M. in Statistics (2012) from Wharton under the guidance of Dean Foster. His academic journey began at Stanford University, where he completed a B.S. in Mathematical & Computational Science (2006), with minors in Physics & Economics. 📊🔬 His strong foundation in statistics, mathematics, and computational science has significantly contributed to his expertise in data analysis and statistical modeling. 📈📚

Academic Employment 

Prof. Adam Kapelner is an Associate Professor of Mathematics at Queens College (since August 2021) and has been the Director of the Undergraduate Data Science and Statistics Program since 2019. Previously, he served as an Assistant Professor of Mathematics (2014–2021). 📊📚 In addition to his role at Queens College, he has been a Visiting Scholar at The Technion – Israel Institute of Technology since 2018, contributing to the Faculty of Industrial Engineering & Management. 🏫🔬 His expertise in statistics, data science, and mathematical modeling continues to shape the next generation of scholars. 🎯📈

Research Interest

Prof. Adam Kapelner’s research spans experimental design, randomization, and statistical software development. 🎲📊 He explores data science and machine learning, applying advanced statistical methods to real-world problems. 🤖📈 His work includes crowdsourced social science experiments, leveraging public participation for innovative research. 🌍🧠 Additionally, he focuses on biomedical applications, using statistical modeling to enhance healthcare analytics. 🏥🧬 Prof. Kapelner is also passionate about educational technology, integrating data-driven approaches to improve learning experiences. 🎓💡 His interdisciplinary expertise contributes significantly to advancing statistical methodologies and their applications across multiple domains. 🚀📉

Honors & Awards 

Prof. Adam Kapelner has received numerous accolades for his teaching, research, and academic contributions. 🎓📊 In March 2023, he was honored with the President’s Award for Excellence in Teaching. 👨‍🏫🏅 His research in economic behavior earned him a Highly Cited Research Certificate (2017). 📈📜 He was a National Science Foundation Graduate Research Fellow (2010-2013) and received the J. Parker Bursk Memorial Award for Excellence in Research (2013). 🏅🔬 His dedication to teaching was recognized with the Donald S. Murray Award (2012), and he was an Intel Science Talent Search Semifinalist early in his career. 🚀🎖️

Teaching Experience 

Prof. Adam Kapelner has extensive teaching experience in statistics, probability, and data science. 🎓📊 At Queens College, CUNY, he teaches courses such as Computational Statistics for Data Science, Probability Theory, Statistical Theory, and Machine Learning Fundamentals. 📈🤖 Since 2014, he has also instructed Bayesian Modeling, Statistical Inference, and Advanced Probability. 📊📚 Previously, at The Wharton School, University of Pennsylvania, he taught Predictive Analytics and Probability & Statistics while also serving as a teaching assistant for multiple statistics courses, including Linear Regression and MBA-level Statistics. 🎓📉 His expertise has shaped many aspiring statisticians and data scientists. 🚀📖

Industry Experience

Prof. Adam Kapelner has a diverse industry background in data science, software engineering, and consulting. 📊💻 Since 2014, he has provided private consulting in prediction modeling, data mining, and statistical testing for tech, real estate, and finance clients. 🏢📈 He worked as a Data Scientist at Coatue, optimizing algorithmic trading. 🤖📉 As Founder & CTO of DictionarySquared, he developed a web app for vocabulary learning, securing federal grant funding. 🚀📚 He was also Eventbrite’s first engineer, helping design its platform. 💡 At Stanford University, he developed image-processing software for biomedical research using machine learning. 🔬📊

Research Focus

Dr. Adam Kapelner specializes in statistical learning, Bayesian additive regression trees (BART), and data-driven decision-making. His work spans machine learning, causal inference, and predictive modeling 🎯. Notable contributions include BART-based predictive analytics, individual conditional expectation plots, and efficient experimental designs 📈. His interdisciplinary research extends to social media-based well-being predictions, crowdsourcing motivation, and personalized medicine 💡. He has also explored biostatistics, oncology-related immune analysis, and ketogenic therapies for cancer 🧬. His impactful research blends theoretical innovation with practical applications, advancing both statistics and computational methods 🔍.

Publication Top Notes

1️⃣ Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation – Journal of Computational and Graphical Statistics, 2015, Cited by: 1718 📊📈

2️⃣ Breaking Monotony with Meaning: Motivation in Crowdsourcing Markets – Journal of Economic Behavior & Organization, 2013, Cited by: 584 💡👥

3️⃣ bartMachine: Machine Learning with Bayesian Additive Regression Trees – Journal of Statistical Software, 2016, Cited by: 451 🤖📉

4️⃣ Predicting individual well-being through the language of social media – Biocomputing 2016 Proceedings, 2016, Cited by: 244 📱🧠

5️⃣ Variable selection for BART: an application to gene regulation – Journal of Statistical Software, 2014, Cited by: 205 🧬📊

6️⃣ Preventing Satisficing in Online Surveys – Proceedings of CrowdConf, 2010, Cited by: 143 📝📑

7️⃣ Prediction with missing data via Bayesian additive regression trees – Canadian Journal of Statistics, 2015, Cited by: 105 📉📈

8️⃣ Spatial organization of dendritic cells within tumor draining lymph nodes impacts clinical outcome in breast cancer patients – Journal of Translational Medicine, 2013, Cited by: 60 🧪🎗

9️⃣ Quantitative, architectural analysis of immune cell subsets in tumor-draining lymph nodes from breast cancer patients and healthy lymph nodes – PLOS ONE, 2010, Cited by: 60 🔬🦠

🔟 Nearly random designs with greatly improved balance – Biometrika, 2019, Cited by: 46 📊📏

1️⃣1️⃣ Matching on-the-fly: Sequential allocation with higher power and efficiency – Biometrics, 2014, Cited by: 40 🏹🎯

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:

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