E
Edoardo M. Airoldi
Researcher at Temple University
Publications - 230
Citations - 20370
Edoardo M. Airoldi is an academic researcher from Temple University. The author has contributed to research in topics: Estimator & Inference. The author has an hindex of 50, co-authored 224 publications receiving 18276 citations. Previous affiliations of Edoardo M. Airoldi include Google & Harvard University.
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Summarizing topical content with word frequency and exclusivity
TL;DR: This work introduces Hierarchical Poisson Convolution (HPC), a model which infers regularized estimates of the differential use of words across topics as well as their frequency within topics and develops a parallelized Hamiltonian Monte Carlo sampler that allows for fast and scalable computation.
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Identification and estimation of treatment and interference effects in observational studies on networks
TL;DR: An extended unconfoundedness assumption that accounts for interference is proposed, and new covariate-adjustment methods are developed that lead to valid estimates of treatment and interference effects in observational studies on networks.
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Predicting traffic volumes and estimating the effects of shocks in massive transportation systems
TL;DR: An in-depth statistical analysis of the Transport for London railway traffic system is conducted to quantify the effects of shocks in the system, and to predict traffic volumes, using network-wide data obtained from smart cards in the London transport system to predict future traffic volumes.
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Constant growth rate can be supported by decreasing energy flux and increasing aerobic glycolysis
Nikolai Slavov,Bogdan Budnik,David J. Schwab,Edoardo M. Airoldi,Edoardo M. Airoldi,Alexander van Oudenaarden,Alexander van Oudenaarden +6 more
TL;DR: The findings demonstrate that exponential growth can represent not a single metabolic/physiological state but a continuum of changing states and that aerobic glycolysis can reduce the energy demands associated with respiratory metabolism and stress survival.
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A survey of statistical network models
TL;DR: An overview of the historical development of statistical network modeling is overviewed and a number of examples that have been studied in the network literature are introduced, and a subsequent discussion focuses on anumber of prominent static and dynamic network models and their interconnections.