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., Kaliannan, K., Schwartz, H. A., Ungar, L. H. & Foster, D. P. (2012) New Insights from Coarse Word Sense Disambiguation in the Crowd. CoLING (journal page)
We use crowdsourcing to disambiguate 1000 words from among coarse-grained senses. Using regression, we find surprising features which drive differential WSD accuracy: (a) the number of rephrasings within a sense definition is associated with higher accuracy; (b) as word frequency increases, accuracy decreases even if the number of senses is kept constant; and (c) spending more time is associated with a decrease in accuracy.
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
We presented the task of predicting well-being, as measured by the "satisfaction with life scale." Using Amazon's Mechanical Turk, we created a training set of textual examples properly rated. We then used machine learning to build a high-performance model and in addition, we identify textual features that characterize well-being.
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
Kapelner, A. & Bleich, J. (2014) Prediction with Missing Data via Bayesian Additive Regression Trees. accepted, Canadian Journal of Statistics
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
Kapelner, A. & Bleich, J. (2014) bartMachine: A Powerful Tool for Machine Learning. accepted, Journal of Statistical Software
Bleich, J. & Kapelner, A. (2014) Bayesian Additive Regression Trees With Parametric Models of Heteroskedasticity. in revision, Bayesian Analysis
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
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
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© 2017 Adam Kapelner