I'm an Assistant Professor of Mathematics at Queens College in New York City. I recently graduated with a Ph.D. in Statistics from Wharton Business School. I was previously a software engineer building web applications in San Francisco and a mathematics & computational science undergraduate at Stanford University.
My talent lies in engineering creative solutions to problems using a toolbox built from my studies in statistics, mathematics, machine learning, computer science, crowdsourcing and natural language processing. I also have a special love for teaching and mentoring.
For a printable version of my CV, click here or use the buttons below to navigate.
I've published in a variety of fields. My interests loosely are statistical learning, randomized experimentation, crowdsourcing, and biomedical applications. Please choose among the keywords below to sort by topic.

For a list of my citations, visit my Google Scholar page or my ResearchGate page.
Kapelner, A. & Bleich, J. (2014) bartMachine: A Powerful Tool for Machine Learning. accepted, Journal of Statistical Software (free PDF)
We present a new R package implementation of Bayesian Additive Regression Trees (a new procedure for statistically learning non-parametric functional relationship between a set up input variables X and a response variable y). The package introduces many new features for data analysis using BART such as variable selection, interaction detection, model diagnostic plots, parallelization, incorporation of missing data and the ability to save trees for future prediction.
Bleich, J. & Kapelner, A. (2014) Bayesian Additive Regression Trees With Parametric Models of Heteroskedasticity. in revision, Bayesian Analysis (free PDF)
We adapt Bayesian Additive Regression Trees (a new procedure for statistically learning non-parametric functional relationship between a set up input variables X and a response variable y). This adaptation incorporates heteroskedasticity into the model by modeling the form of heteroskedasticity as a linear model of another set of covariates (may or may not be X). In simulations, we demonstrate a reduction in overfitting and more appropriate predictive intervals than homoskedastic BART.
Kapelner, A. & Bleich, J. (2014) Prediction with Missing Data via Bayesian Additive Regression Trees. accepted, Canadian Journal of Statistics (free PDF)
We develop an extension to Bayesian Additive Regression Trees (a new procedure for statistically learning non-parametric functional relationship between a set up input variables X and a response variable y). In the extension, we incorporate missing data without the need for imputation. Simulations using real data and generated models demonstrate high performance and stability over competitors.
Kapelner, A. & Krieger, A. (2014) Matching on-the-fly in Sequential Experiments for Higher Power and Efficiency. Biometrics, 70 (2), 378 - 388
Kapelner, A., Bleich, J., Cohen, Z. D., DeRubeis, R. J. & Berk, R. A. (2014) Inference for Treatment Regime Models in Personalized Medicine. submitted to Biometrics
Kapelner, A. & Vorsanger, M. (2014) Starvation of Cancer via Induced Ketogenesis and Severe Hypoglycemia. in press, Medical Hypotheses
Bleich, J., Kapelner, A., George, E. I. & Jensen, S. T. (2014) Variable Selection for BART: An Application to Gene Regulation. Annals of Applied Statistics, 8(3): 1750-1781
Goldstein, A., Kapelner, A., Bleich, J. & Pitkin, E. (2014) Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation. in press, Journal of Computational & Graphical Statistics
Berk, R. A., Bleich, J., Kapelner, A., Henderson, J., Kurtz, E. (2014) Using Regression Kernels to Forecast A Failure to Appear in Court. submitted to Journal of Quantitative Criminology
Chandler, D. & Kapelner, A. (2013) Breaking Monotony with Meaning: Motivation in Crowdsourcing Markets. Journal of Economic Behavior & Organization, 90: 123-133
Schwartz, H. A., Eichstaedt, J., Blanco, E., Agrawal, M., Dziurzynnski, L., Kern, M. L., Kapelner, A., Park, G., Jha, S., Stillwell, D., Kosinski, M. & Ungar, L. H. (2014) Predicting People's Well-Being in Social Media: Multi-level message and user models of language use. working paper
Kapelner, A., Kaliannan, K., Schwartz, H. A., Ungar, L. H. & Foster, D. P. (2012) New Insights from Coarse Word Sense Disambiguation in the Crowd. CoLING
Kapelner, A. & Chandler, D. (2010) Preventing Satisficing in Online Surveys. Proceedings of CrowdConf
Chang, A. Y., Bhattacharya, N., Mu, J., Setiadi, A. F., Carcamo-Cavazos, V., Lee, G. H., Simons, D. L., Yadegarynia, S., Hemati, K., Kapelner, A., Zheng, M., Krag, D. N., Schwartz, E. J., Chen, D. Z. & Lee, P. P. (2013) Spatial organization of dendritic cells within tumor draining lymph nodes impacts clinical outcome in breast cancer patients. Journal of translational medicine, 11(1): 242
Setiadi, A. F.; Ray, N. C., Kohrt, H. E., Kapelner, A., Carcamo-Cavazos, V., Levic, E. B., Yadegarynia, S., van der Loos, C. M., Schwartz, E. J., Holmes, S. & Lee, P. P. (2010) Quantitative, architectural analysis of immune cell subsets in tumor-draining lymph nodes from breast cancer patients and healthy lymph nodes. PloS one, 5(8): e12420
Holmes, S., Kapelner, A. & Lee, P. P. (2009) An interactive java statistical image segmentation system: Gemident. Journal of Statistical Software, 30(10): 1-20
Kapelner, A., Lee, P. P. & Holmes, S. (2007) An interactive statistical image segmentation and visualization system. in proceedings of IEEE, Medical Information Visualisation
Contact me by email: © 2017 Adam Kapelner
Consulting_button_rollover Tutoring_button_rollover'
© 2017 Adam Kapelner