
news
recent and upcoming talks
 TBD. MIT (Stochastics & Statistics), September 25, 2015, Cambridge, MA.
 TBD. Microsoft Research New England (Statistics & Data Science Symposium), June 1213, 2015, Cambridge, MA.
 TBD. University of Washington (Computer Science), June 2, 2015, Seattle, WA.
 TBD. University of Washington (Statistics), June 1, 2015, Seattle, WA.
 TBD. University of Montreal (Centre de Recherches Matematiques), May 4  8, 2015, Montreal, Canada.
 TBD. Harvard University (Center of Mathematical Sciences and Applications), April 30  May 2, 2015, Cambridge, MA.
 TBD. University of Chicago (Booth School of Business), April 2, 2015, Chicago, IL.
 Designing optimal experiments in the presence of social interference. National Academy of Sciences (Sackler Symposium on Drawing Causal Inference from Big Data), March 2627, 2015, Washington, DC.
 TBD. University of Connecticut (Statistics), March 11, 2015, Storrs, CT.
 Valid statistical analyses and reproducible science in the era of highthroughput biology. Harvard School of Public Health (PQG Short Course), March 10, 2015, Boston, MA.
 Statistical and machine learning challenges in the analysis of large networks. UC Berkeley (IEOR), February 13, 2015, Berkeley, CA.
 Statistical and machine learning challenges in the analysis of large networks. Northeastern University (Mathematics), February 10, 2015, Boston, MA.
 Design and analysis of experiments in the presence of network interference. Princeton University (Political Science), December 5, 2014, Princeton, NJ.
 Statistical and machine learning challenges in the analysis of large networks. Princeton University (Computer Science), December 2, 2014, Princeton, NJ.
 Design and analysis of experiments in the presence of network interference. Yale University (Statistics), December 2, 2013, New Haven, CT.
 Design and analysis of experiments with interfering units. Simons Institute for the Theory of Computing, November 1821, 2013, UC Berkeley, CA.
preprints
 Geometric representations of distributions on hypergraphs.
(pdf)
 Bayesian inference from nonignorable network sampling designs.
(pdf)
 Inference of network summary statistics through network denoising.
(pdf)
 Sharp total variation bounds for finitely exchangeable arrays.
(pdf)
 The geometry of 2x2 contingency tables.
(java app)
(source code)
 Estimating cellular pathways from an ensemble of heterogeneous data sources.
(pdf)
 Variable stoichiometry among core ribosomal proteins.
(pdf)
selected publications
(see my CV
or Google Scholar
for more publications and bibliographic details)
interface between statistics and computing
 Scalable estimation strategies based on stochastic approximations: Classical results and new insights.
Statistics and Computing, 2015.
 Implicit stochastic gradient methods for principled estimation with large data sets.
(pdf)
(a shorter version appeared at ICML 2014)
theory and methods for network data analysis
 A consistent total variation estimator for exchangeable graph models.
(pdf)
(a shorter version appeared at ICML 2014)
 Stochastic blockmodel approximation of a graphon: Theory and consistent estimation.
NIPS, 2013.
(pdf)
 Stochastic blockmodels with growing number of classes.
Biometrika, 2012.
(pdf)
 Confidence sets for network structure.
Statistical Analysis and Data Mining, 2011.
(pdf)
(a shorter version appeared at NIPS 2011)
 Graphlets decomposition of a weighted network.
Journal of Machine Learning Research, W&CP, 2011.
(pdf)
(MSR best student paper award, NESS 2012)
 Network sampling and classification: An investigation of network model representations.
Decision Support Systems, 2011.
(pdf)
 A survey of statistical network models.
Foundations and Trends in Machine Learning, 2010.
(pdf)
 Mixedmembership stochastic blockmodels.
Journal of Machine Learning Research, 2008.
(pdf)
(r code)
(fast code)
(John Van Ryzin award, 2006)
geometry and inference in illposed inverse problems
 Estimating latent processes on a network from indirect measurements.
Journal of the American Statistical Association, 2013.
(pdf)
(supp)
(r code)
(IBM best student paper award, NESS 2011)
 Polytope samplers for inference in illposed inverse problems.
Journal of Machine Learning Research, W&CP, 2011.
(pdf)
 Tree preserving embedding
Proceedings of the National Academy of Sciences, 2011.
(pdf)
(r code)
(a shorter version appeared at ICML 2011)
modeling and inference in highthroughput biology
 Estimating a structured covariance matrix from multilab measurements in highthroughput biology.
Journal of the American Statistical Association, in press.
(IBM best student paper award, NESS 2013)
 Generalized species sampling priors with latent beta reinforcements.
Journal of the American Statistical Association, 2014.
(pdf)
 Multiway blockmodels for analyzing coordinated highdimensional responses.
Annals of Applied Statistics, 2013.
(pdf)
(supp)
 Analysis and design of RNA sequencing experiments for identifying mRNA isoform regulation.
Nature Methods, 2010.
(pdf)
(supp)
(code)
 Ranking relations using analogies in biological and information networks.
Annals of Applied Statistics, 2010.
(pdf)
(code)
 Predicting cellular growth from gene expression signatures.
PLoS Computational Biology, 2009.
(pdf)
(code & data)
(a shorter version appeared at NIPS 2008)
 Getting started in probabilistic graphical models.
PLoS Computational Biology, 2007.
(pdf)
applications in molecular biology
 Musashi proteins are posttranscriptional regulators of the epithelialluminal cell state.
eLife, 2014.
(pdf)
(editor's choice in Science)
 Quantifying conditiondependent intracellular protein levels enables highprecision fitness estimates.
PLoS One, 2013.
(pdf)
 A conserved cell growth cycle can account for the environmental stress responses of divergent eukaryotes.
Molecular Biology of the Cell, 2012.
(pdf)
 Systemslevel dynamic analyses of fate change in murine embryonic stem cells.
Nature, 2009.
(pdf)
(supp)
(F1000)
(news & views, Nat BT)
(editor's choice, Sci Sig)
 Coordination of growth rate, cell cycle, stress response and metabolic activity in yeast.
Molecular Biology of the Cell, 2008.
(pdf)
(code & data)
modeling and inference in computational social science
 Causal inference for ordinal outcomes.
(pdf)
 A model of text for experimentation in the social sciences.
(pdf)
(a shorter version appeared at NIPS 2013)
 Robust summaries of topical content with word frequency and exclusivity.
(pdf)
(a shorter version appeared at ICML 2012)
applications in computational social science
 A natural experiment of social network formation and dynamics.
Proceedings of the National Acadamy of Sciences, in press.
 Discussion of Hennig and Liao 'How to find an appropriate clustering for mixedtype variables with application to socioeconomic stratification'.
Journal of the Royal Statistical Society, Series C, 2013.
(pdf)
(article)
 Reconceptualizing the classification of PNAS articles.
Proceedings of the National Academy of Sciences, 2010.
(pdf)
(editorial feature)
 Whose ideas? Whose words? Authorship of the Ronald Reagan radio addresses.
Political Science & Politics, 2007.
(pdf)
(oped by Skinner & Rice)
 Who wrote Ronald Reagan's radio addresses?
Bayesian Analysis, 2006.
(pdf)
(tr with detailed predictions)
(notes on Negative Binomial)
theses
 Bayesian mixedmembership models of complex and evolving networks.
Doctoral dissertation, 2007.
(Savage award honorable mention, 2007)
 The theory of weak convergence of probability measures and its applications in statistics.
Undergraduate thesis, 1999.
(Gold medal for best graduates, 1999)
