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Bigname
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., Cohen, Z. D., DeRubeis, R. J. & Berk, R. A. (2014) Inference for Treatment Regime Models in Personalized Medicine. submitted to Biometrics (free PDF)
Imagine you are a medical practitioner treating a disease by prescribing one of two possible drugs. Which drug do you assign to patients? Is your special assignment procedure beneficial versus a naive random assignment? How much better and is the improvement statistically significant?
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 (journal page)
We describe the spatial organization of dendritic cells within tumor-draining lymph nodes using the software gemident. We then describe the spatial organization's association with survival outcome in cancer patients. We also characterize specific changes in number, size, maturity, and T-cell co-localization of such clusters.
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 (journal page)
We present a novel, quantitative image analysis approach incorporating 1) multi-color tissue staining, 2) high-resolution, automated whole-section imaging, 3) the use of the "gemident" image analysis software to identify cell types and locations, and 4) spatial statistical analysis. We apply our integrative approach to compare the architectural patterns of T and B cells within tumor-draining lymph nodes from breast cancer patients versus healthy lymph nodes. We found that the spatial grouping patterns of T and B cells differed between healthy and breast cancer lymph nodes, and this could be attributed to the lack of B cell localization in the extrafollicular region of the TDLNs.
Holmes, S., Kapelner, A. & Lee, P. P. (2009) An interactive java statistical image segmentation system: Gemident. Journal of Statistical Software, 30(10): 1-20 (journal page)
We present a novel object identification algorithm developed in Java which locates objects of interest in images. Here, we apply the system to finding cells in images of immunohisto-chemically-stained lymph node tissue. The success of the method depends heavily on the use of color, the relative homogeneity of object appearance, the user's input, and the coupled statistical learning algorithm, random forests. Our system enables iterative improvements to the classification over many correction cycles, resulting in a highly accurate and user-friendly system.
Kapelner, A., Lee, P. P. & Holmes, S. (2007) An interactive statistical image segmentation and visualization system. in proceedings of IEEE, Medical Information Visualisation (journal page) (free PDF)
Supervised learning can be used to segment regions of interest in images making use of color and morphological information. We developed a novel object identification algorithm in Java which locates phenotypes of interest in images. Our main innovation is interactive feature extraction from color images by using sums over color similarities (as measured by the Mahalanobis distance) at various radii. These features are then fed into a statistical learning algorithm to classify pixels belonging to phenotypes of interest.
Kapelner, A. & Vorsanger, M. (2014) Starvation of Cancer via Induced Ketogenesis and Severe Hypoglycemia. in press, Medical Hypotheses (journal page) (free PDF)
It is well known that cancer cells are solely dependent on glucose as their substrate for metabolism and they are not able to utilize other fuel sources such as ketones and fatty acids. It is also known that humans under heavy ketosis do not experience symptoms of hypoglycemia. In our proposal for cancer therapy, we marry these two ideas over the long term.
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. (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
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
Contact me by email: kapelner@gmail.com © 2017 Adam Kapelner
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© 2017 Adam Kapelner