New York City

Prof. Adam Kapelner

Elite
@kapelner

I'm an Assoc. Prof. of Math at Queens College (NYC) where I run the undergrad Data Science major, my PhD is in Statistics, my research is in experimental design

bartMachine. An R-Java Bayesian Additive Regression Trees implementation

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ICEbox. An R package for better visualizing a statistical learning model

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Wharton_Stat_422_722. The official class webpage for Statistics 422/722 taught at Wharton in the Spring of 2017

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QC_MATH_342W_Spring_2021. Course Homepage for Math 342W Intro to Data Science and Machine Learning at Queens College

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QC_Math_241_Fall_2017. This is the homepage for Fall 2017's Math 241 taught at Queens College by Professor Adam Kapelner

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QC_MATH_342W_Spring_2024. Course Homepage for Math 342W / 642 / RM 742 Fundamentals of Data Science at Queens College

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GemIdent. An image segmentation platform using Statistical Learning developed by Professor Susan Holmes and Adam Kapelner, under GPL2.

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QC_MATH_342W_Spring_2022. Course Homepage for Math 342W Intro to Data Science and Machine Learning at Queens College

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QC_Math_621_Fall_2019. Homepage for Fall 2019's intermediate probability class (Math 621) taught at Queens College by Professor Adam Kapelner

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QC_MATH_340_Fall_2023. Course Homepage for Math 340 / 640 Probability Theory for Data Science and Statistics at Queens College

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QC_Math_390.03-02_Spr_2016. Course homepage for Bayesian Modeling (Math 390.03-02) for the Spring, 2016 semester at Queens College, CUNY

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QC_Math_621_Fall_2020. TeX

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QC_Math_390.4_Spring_2019. Course Homepage for Math 390.4 Intro to Data Science and Machine Learning at Queens College in the Spring, 2019

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QC_Math_369_Fall_2020. TeX

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QC_MATH_342W_Spring_2025. TeX

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QC_Math_390.4_Spring_2018. Course Homepage for Math 390.4 Intro to Data Science and Machine Learning at Queens College in the Spring, 2018

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QC_Math_341_Spring_2019. The course homepage for Math 341 (and Masters level 650.3-02) for the Spring semester, 2019 at Queens College, City University of New York

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QC_MATH_341_Fall_2024. Course homepage for Math 341 / 641 at Queens College, CUNY

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QC_Math_390.4_Spring_2020. Course Homepage for Math 390.4 Intro to Data Science and Machine Learning at Queens College in the Spring, 2020

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QC_Math_241_Fall_2016. Math 241 course homepage for the Fall 2016 semester at Queens College

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QC_MATH_241_Fall_2021. The course homepage for Math 241 Fall 2021

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QC_Math_341_Spring_2021. TeX

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QC_Math_241_Fall_2015. Math 241 course homepage for the Fall 2015 semester at Queens College

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QC_MATH_341_Fall_2025. Course homepage for Math 341 / 641 at Queens College, CUNY

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QC_Math_341_Spring_2018. TeX

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QC_MATH_343_Spring_2025. About Course Homepage for Math 343 Computational Statistics for Data Science at Queens College

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QC_Math_341_Spring_2017. Course Homepage for Math 341 Bayesian Modeling at Queens College in the Spring, 2017

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QC_Math_341_Spring_2020. The course homepage for Math 341 (and Masters level 650.3) for the Spring semester, 2020 at Queens College, City University of New York

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QC_MATH_340_Fall_2024. Course homepage for Math 340 / 640 at Queens College, CUNY

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QC_Math_241_Spring_2015. Math 241 course homepage for the Spring 2015 semester at Queens College

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fastLogisticRegressionWrap. The public repository for the R package fastLogisticRegressionWrap on CRAN

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QC_MATH_343_Spring_2024. Course Homepage for Math 343 Computational Statistics for Data Science at Queens College

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QC_MATH_341_Fall_2023. Course Homepage for Math 341 / 641 Statistical Theory for Data Science at Queens College

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QC_MATH_340_Fall_2025. Course homepage for Math 340 / 640 at Queens College, CUNY

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YARF. Yet Another Random Forests [Implementation]

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QC_Math_241_Fall_2014_15. Course homepage for Math 241 for Fall 2014-15 at Queens College

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CovBalAndRandExpDesign. This is the home of the source code for R package "CovBalAndRandExpDesign".

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PTE. Personalized Treatment Evaluator

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breaking_monotony. Code and supplementary materials for the publication "Breaking Monotony with Meaning: Motivation in Crowdsourcing Markets"

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Stat101_Summer_2011. The course materials for my summer 2011 class in basic Statistics for undergraduates.

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GemVident. An video particle tracking program developed by Adam Kapelner, Adam Guetz, and Professor Susan Holmes. It is a natural extension of GemIdent, a statistical-learning based image segmentation platform.

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