Adam Kapelner | Statistics | Best Faculty Award

Published on by Global Academic Awards

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 🏹🎯

Sadaf Khan | Probability and Statistics | Best Scholar Award

Published on by Global Academic Awards

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

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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