Research with Prof. Joseph K. Blitzstein concerning Respondent-Driven Sampling and Systems, related estimation problems, and goal-based process incentive optimization. Also, work on network visualization and node attribute-dependent network structures such as homophily.

Selected papers

  • Sergiy O. Nesterko and Joseph K. Blitzstein. Bias-Variance and Breadth-Depth Tradeoffs in Respondent-Driven Sampling. Journal of Statistical Computation and Simulation (2013).
  • Sergiy O. Nesterko and Joseph K. Blitzstein. Measuring Homophily in Network Data. Submitted to Social Networks (2012).
  • Joseph K. Blitzstein and Sergiy O. Nesterko. Model-based estimation for Respondent-Driven Sampling. Preprint.

Selected talks

Interactive Visualization

Interactive visualization is an effective tool for connecting analytic information to algorithms and data that generate it. As such, it is a powerful catalyst informing and accelerating data-driven strategy and innovation.

Examples of my work with interactive visualization: research on social networks, process and educational visualization, intersection of research, strategy, and infovis.

I am interested in conveying uncertainty in interactive user experiences, using interactive visualization for sensitivity analysis, and as an intermediate step in statistical model-building. I am also interested in studying the effectiveness of interactive displays of analytic information in aiding the strategic decision-making process and improving organizational communication.

Response surface methods in experimental design

Work on optimization and response surface methods with marketing applications. We consider applied example of a large consumer packaged goods company (CPG) with response surface optimization of dimensionality exceeding 300,000,000. To perform optimization, we use deterministic numerical algorithms to find local modes. Apart from computational aspects, we study the problem's properties from the perspective of modern experiment design, where such high dimensionality and related methods have not been considered so far. Research in collaboration with Tatsunori Hashimoto.

Cross-validation methods

Cross-validation methods are a useful tool for model selection and overfitting detection. This research aims at developing its theoretic foundations and optimality criteria further beyond jackknife and putting cross-validation in context together with other methods such as AIC and BIC, as well as Bayes Factor, which in many circumstances prove to be impractical.