Edoardo Airoldi
Professor of Statistics & Data Science Temple University & PI, Harvard University
Research Expertise
About
Publications
Coming soon to a journal near you—The updated guidelines for the use and interpretation of assays for monitoring autophagy
Autophagy / Aug 22, 2014
Klionsky, D. J. (2014). Coming soon to a journal near you—The updated guidelines for the use and interpretation of assays for monitoring autophagy. Autophagy, 10(10), 1691–1691. https://doi.org/10.4161/auto.36187
IGraph/M: graph theory and network analysis for Mathematica
Journal of Open Source Software / Jan 08, 2023
Horvát, S., Podkalicki, J., Csárdi, G., Nepusz, T., Traag, V., Zanini, F., & Noom, D. (2023). IGraph/M: graph theory and network analysis for Mathematica. Journal of Open Source Software, 8(81), 4899. https://doi.org/10.21105/joss.04899
Handbook of Mixed Membership Models and Their Applications
Nov 06, 2014
Airoldi, E. M., Blei, D., Erosheva, E. A., & Fienberg, S. E. (Eds.). (2014). Handbook of Mixed Membership Models and Their Applications. https://doi.org/10.1201/b17520
Navigating the Local Modes of Big Data: The Case of Topic Models
Computational Social Science / Jan 31, 2016
Roberts, M. E., Stewart, B. M., & Tingley, D. (2016). Navigating the Local Modes of Big Data: The Case of Topic Models. Computational Social Science, 51–97. https://doi.org/10.1017/cbo9781316257340.004
Analysis and design of RNA sequencing experiments for identifying isoform regulation
Nature Methods / Nov 07, 2010
Katz, Y., Wang, E. T., Airoldi, E. M., & Burge, C. B. (2010). Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nature Methods, 7(12), 1009–1015. https://doi.org/10.1038/nmeth.1528
A Survey of Statistical Network Models
Foundations and Trends® in Machine Learning / Jan 01, 2009
Goldenberg, A. (2009). A Survey of Statistical Network Models. Foundations and Trends® in Machine Learning, 2(2), 129–233. https://doi.org/10.1561/2200000005
Coordination of Growth Rate, Cell Cycle, Stress Response, and Metabolic Activity in Yeast
Molecular Biology of the Cell / Jan 01, 2008
Brauer, M. J., Huttenhower, C., Airoldi, E. M., Rosenstein, R., Matese, J. C., Gresham, D., Boer, V. M., Troyanskaya, O. G., & Botstein, D. (2008). Coordination of Growth Rate, Cell Cycle, Stress Response, and Metabolic Activity in Yeast. Molecular Biology of the Cell, 19(1), 352–367. https://doi.org/10.1091/mbc.e07-08-0779
A Model of Text for Experimentation in the Social Sciences
Journal of the American Statistical Association / Jul 02, 2016
Roberts, M. E., Stewart, B. M., & Airoldi, E. M. (2016). A Model of Text for Experimentation in the Social Sciences. Journal of the American Statistical Association, 111(515), 988–1003. https://doi.org/10.1080/01621459.2016.1141684
Reversible, Specific, Active Aggregates of Endogenous Proteins Assemble upon Heat Stress
Cell / Sep 01, 2015
Wallace, E. W. J., Kear-Scott, J. L., Pilipenko, E. V., Schwartz, M. H., Laskowski, P. R., Rojek, A. E., Katanski, C. D., Riback, J. A., Dion, M. F., Franks, A. M., Airoldi, E. M., Pan, T., Budnik, B. A., & Drummond, D. A. (2015). Reversible, Specific, Active Aggregates of Endogenous Proteins Assemble upon Heat Stress. Cell, 162(6), 1286–1298. https://doi.org/10.1016/j.cell.2015.08.041
Stochastic blockmodels with a growing number of classes
Biometrika / Apr 17, 2012
Choi, D. S., Wolfe, P. J., & Airoldi, E. M. (2012). Stochastic blockmodels with a growing number of classes. Biometrika, 99(2), 273–284. https://doi.org/10.1093/biomet/asr053
Co-EM support vector learning
Twenty-first international conference on Machine learning - ICML '04 / Jan 01, 2004
Brefeld, U., & Scheffer, T. (2004). Co-EM support vector learning. Twenty-First International Conference on Machine Learning - ICML ’04. https://doi.org/10.1145/1015330.1015350
Differential Stoichiometry among Core Ribosomal Proteins
Cell Reports / Nov 01, 2015
Slavov, N., Semrau, S., Airoldi, E., Budnik, B., & van Oudenaarden, A. (2015). Differential Stoichiometry among Core Ribosomal Proteins. Cell Reports, 13(5), 865–873. https://doi.org/10.1016/j.celrep.2015.09.056
Quantitative visualization of alternative exon expression from RNA-seq data
Bioinformatics / Jan 22, 2015
Katz, Y., Wang, E. T., Silterra, J., Schwartz, S., Wong, B., Thorvaldsdóttir, H., Robinson, J. T., Mesirov, J. P., Airoldi, E. M., & Burge, C. B. (2015). Quantitative visualization of alternative exon expression from RNA-seq data. Bioinformatics, 31(14), 2400–2402. https://doi.org/10.1093/bioinformatics/btv034
Defining the Essential Function of Yeast Hsf1 Reveals a Compact Transcriptional Program for Maintaining Eukaryotic Proteostasis
Molecular Cell / Jul 01, 2016
Solís, E. J., Pandey, J. P., Zheng, X., Jin, D. X., Gupta, P. B., Airoldi, E. M., Pincus, D., & Denic, V. (2016). Defining the Essential Function of Yeast Hsf1 Reveals a Compact Transcriptional Program for Maintaining Eukaryotic Proteostasis. Molecular Cell, 63(1), 60–71. https://doi.org/10.1016/j.molcel.2016.05.014
Post-transcriptional regulation across human tissues
PLOS Computational Biology / May 08, 2017
Franks, A., Airoldi, E., & Slavov, N. (2017). Post-transcriptional regulation across human tissues. PLOS Computational Biology, 13(5), e1005535. https://doi.org/10.1371/journal.pcbi.1005535
Bolasso
Proceedings of the 25th international conference on Machine learning - ICML '08 / Jan 01, 2008
Bach, F. R. (2008). Bolasso. Proceedings of the 25th International Conference on Machine Learning - ICML ’08. https://doi.org/10.1145/1390156.1390161
Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks
Journal of the American Statistical Association / Jun 30, 2020
Forastiere, L., Airoldi, E. M., & Mealli, F. (2020). Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks. Journal of the American Statistical Association, 116(534), 901–918. https://doi.org/10.1080/01621459.2020.1768100
Asymptotic and finite-sample properties of estimators based on stochastic gradients
The Annals of Statistics / Aug 01, 2017
Toulis, P., & Airoldi, E. M. (2017). Asymptotic and finite-sample properties of estimators based on stochastic gradients. The Annals of Statistics, 45(4). https://doi.org/10.1214/16-aos1506
Improving and Evaluating Topic Models and Other Models of Text
Journal of the American Statistical Association / Oct 01, 2016
Airoldi, E. M., & Bischof, J. M. (2016). Improving and Evaluating Topic Models and Other Models of Text. Journal of the American Statistical Association, 111(516), 1381–1403. https://doi.org/10.1080/01621459.2015.1051182
Unified Inference for Variational Bayesian Linear Gaussian State-Space Models
Advances in Neural Information Processing Systems 19 / Jan 01, 2007
Unified Inference for Variational Bayesian Linear Gaussian State-Space Models. (2007). Advances in Neural Information Processing Systems 19. https://doi.org/10.7551/mitpress/7503.003.0015
Assessing the Impact of Granular Privacy Controls on Content Sharing and Disclosure on Facebook
Information Systems Research / Dec 01, 2016
Cavusoglu, H., Phan, T. Q., Cavusoglu, H., & Airoldi, E. M. (2016). Assessing the Impact of Granular Privacy Controls on Content Sharing and Disclosure on Facebook. Information Systems Research, 27(4), 848–879. https://doi.org/10.1287/isre.2016.0672
Musashi proteins are post-transcriptional regulators of the epithelial-luminal cell state
eLife / Nov 07, 2014
Katz, Y., Li, F., Lambert, N. J., Sokol, E. S., Tam, W.-L., Cheng, A. W., Airoldi, E. M., Lengner, C. J., Gupta, P. B., Yu, Z., Jaenisch, R., & Burge, C. B. (2014). Musashi proteins are post-transcriptional regulators of the epithelial-luminal cell state. ELife, 3. CLOCKSS. https://doi.org/10.7554/elife.03915
Detecting Network Effects
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining / Aug 13, 2017
Saveski, M., Pouget-Abadie, J., Saint-Jacques, G., Duan, W., Ghosh, S., Xu, Y., & Airoldi, E. M. (2017). Detecting Network Effects. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3097983.3098192
Solving large scale linear prediction problems using stochastic gradient descent algorithms
Twenty-first international conference on Machine learning - ICML '04 / Jan 01, 2004
Zhang, T. (2004). Solving large scale linear prediction problems using stochastic gradient descent algorithms. Twenty-First International Conference on Machine Learning - ICML ’04. https://doi.org/10.1145/1015330.1015332
Model-assisted design of experiments in the presence of network-correlated outcomes
Biometrika / Aug 06, 2018
Basse, G. W., & Airoldi, E. M. (2018). Model-assisted design of experiments in the presence of network-correlated outcomes. Biometrika, 105(4), 849–858. https://doi.org/10.1093/biomet/asy036
Dendrite morphological neurons trained by stochastic gradient descent
2016 IEEE Symposium Series on Computational Intelligence (SSCI) / Dec 01, 2016
Zamora, E., & Sossa, H. (2016). Dendrite morphological neurons trained by stochastic gradient descent. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). https://doi.org/10.1109/ssci.2016.7849933
Multiscale Local Polynomial Models for Estimation and Testing
Springer Proceedings in Mathematics & Statistics / Jan 01, 2014
Jansen, M. (2014). Multiscale Local Polynomial Models for Estimation and Testing. Topics in Nonparametric Statistics, 155–166. https://doi.org/10.1007/978-1-4939-0569-0_14
Credit-based network management by weighted fuzzy C-means
International Conference on Automatic Control and Artificial Intelligence (ACAI 2012) / Jan 01, 2012
Fei Wang, Jilong Wang, Qin Yan, & Zhuoming Xu. (2012). Credit-based network management by weighted fuzzy C-means. International Conference on Automatic Control and Artificial Intelligence (ACAI 2012). https://doi.org/10.1049/cp.2012.1082
Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study
Statistics in Medicine / Jan 01, 2017
Lunceford, J. K. (2017). Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine. Portico. https://doi.org/10.1002/sim.7231
A conserved cell growth cycle can account for the environmental stress responses of divergent eukaryotes
Molecular Biology of the Cell / May 15, 2012
Slavov, N., Airoldi, E. M., van Oudenaarden, A., & Botstein, D. (2012). A conserved cell growth cycle can account for the environmental stress responses of divergent eukaryotes. Molecular Biology of the Cell, 23(10), 1986–1997. https://doi.org/10.1091/mbc.e11-11-0961
Reconceptualizing the classification of PNAS articles
Proceedings of the National Academy of Sciences / Nov 15, 2010
Airoldi, E. M., Erosheva, E. A., Fienberg, S. E., Joutard, C., Love, T., & Shringarpure, S. (2010). Reconceptualizing the classification of PNAS articles. Proceedings of the National Academy of Sciences, 107(49), 20899–20904. https://doi.org/10.1073/pnas.1013452107
Exploiting social influence to magnify population-level behaviour change in maternal and child health: study protocol for a randomised controlled trial of network targeting algorithms in rural Honduras
BMJ Open / Mar 01, 2017
Shakya, H. B., Stafford, D., Hughes, D. A., Keegan, T., Negron, R., Broome, J., McKnight, M., Nicoll, L., Nelson, J., Iriarte, E., Ordonez, M., Airoldi, E., Fowler, J. H., & Christakis, N. A. (2017). Exploiting social influence to magnify population-level behaviour change in maternal and child health: study protocol for a randomised controlled trial of network targeting algorithms in rural Honduras. BMJ Open, 7(3), e012996. https://doi.org/10.1136/bmjopen-2016-012996
Estimating Selection on Synonymous Codon Usage from Noisy Experimental Data
Molecular Biology and Evolution / Mar 14, 2013
Wallace, E. W. J., Airoldi, E. M., & Drummond, D. A. (2013). Estimating Selection on Synonymous Codon Usage from Noisy Experimental Data. Molecular Biology and Evolution, 30(6), 1438–1453. https://doi.org/10.1093/molbev/mst051
Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security
Oct 29, 2004
Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security. (2004). https://doi.org/10.1145/1029208
Steady-state and dynamic gene expression programs inSaccharomyces cerevisiaein response to variation in environmental nitrogen
Molecular Biology of the Cell / Apr 15, 2016
Airoldi, E. M., Miller, D., Athanasiadou, R., Brandt, N., Abdul-Rahman, F., Neymotin, B., Hashimoto, T., Bahmani, T., & Gresham, D. (2016). Steady-state and dynamic gene expression programs inSaccharomyces cerevisiaein response to variation in environmental nitrogen. Molecular Biology of the Cell, 27(8), 1383–1396. https://doi.org/10.1091/mbc.e14-05-1013
On Learning Parsimonious Models for Extracting Consumer Opinions
Proceedings of the 38th Annual Hawaii International Conference on System Sciences
Xue Bai, Padman, R., & Airoldi, E. (n.d.). On Learning Parsimonious Models for Extracting Consumer Opinions. Proceedings of the 38th Annual Hawaii International Conference on System Sciences. https://doi.org/10.1109/hicss.2005.465
Integrating Compound Terms in Bayesian Text Classification
The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)
Jing Bai, Jian-Yun Nie, & Guihong Cao. (n.d.). Integrating Compound Terms in Bayesian Text Classification. The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI’05). https://doi.org/10.1109/wi.2005.79
The Structure of Negative Social Ties in Rural Village Networks
Sociological Science / Jan 01, 2019
Isakov, A., Fowler, J., Airoldi, E., & Christakis, N. (2019). The Structure of Negative Social Ties in Rural Village Networks. Sociological Science, 6, 197–218. https://doi.org/10.15195/v6.a8
Tree preserving embedding
Proceedings of the National Academy of Sciences / Sep 26, 2011
Shieh, A. D., Hashimoto, T. B., & Airoldi, E. M. (2011). Tree preserving embedding. Proceedings of the National Academy of Sciences, 108(41), 16916–16921. https://doi.org/10.1073/pnas.1018393108
Mapping Dynamic Histone Acetylation Patterns to Gene Expression in Nanog-Depleted Murine Embryonic Stem Cells
PLoS Computational Biology / Dec 16, 2010
Markowetz, F., Mulder, K. W., Airoldi, E. M., Lemischka, I. R., & Troyanskaya, O. G. (2010). Mapping Dynamic Histone Acetylation Patterns to Gene Expression in Nanog-Depleted Murine Embryonic Stem Cells. PLoS Computational Biology, 6(12), e1001034. https://doi.org/10.1371/journal.pcbi.1001034
A Common Electronic Health Record for Norwegian Municipalities
MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation / Jun 06, 2022
Ellingsen, G., Christensen, B., & Wynn, R. (2022). A Common Electronic Health Record for Norwegian Municipalities. Studies in Health Technology and Informatics. https://doi.org/10.3233/shti220288
The proximal Robbins–Monro method
Journal of the Royal Statistical Society: Series B (Statistical Methodology) / Dec 09, 2020
Toulis, P., Horel, T., & Airoldi, E. M. (2020). The proximal Robbins–Monro method. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 83(1), 188–212. Portico. https://doi.org/10.1111/rssb.12405
Testing for arbitrary interference on experimentation platforms
Biometrika / Sep 30, 2019
Pouget-Abadie, J., Saint-Jacques, G., Saveski, M., Duan, W., Ghosh, S., Xu, Y., & Airoldi, E. M. (2019). Testing for arbitrary interference on experimentation platforms. Biometrika, 106(4), 929–940. https://doi.org/10.1093/biomet/asz047
Cyclic motifs in the Sardex monetary network
Nature Human Behaviour / Oct 22, 2018
Iosifidis, G., Charette, Y., Airoldi, E. M., Littera, G., Tassiulas, L., & Christakis, N. A. (2018). Cyclic motifs in the Sardex monetary network. Nature Human Behaviour, 2(11), 822–829. https://doi.org/10.1038/s41562-018-0450-0
Causal Inference for Statistics, Social, and Biomedical Sciences
Apr 06, 2015
Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences. https://doi.org/10.1017/cbo9781139025751
A Coevolution Model of Network Structure and User Behavior: The Case of Content Generation in Online Social Networks
Information Systems Research / Mar 01, 2019
Bhattacharya, P., Phan, T. Q., Bai, X., & Airoldi, E. M. (2019). A Coevolution Model of Network Structure and User Behavior: The Case of Content Generation in Online Social Networks. Information Systems Research, 30(1), 117–132. https://doi.org/10.1287/isre.2018.0790
Nonstandard conditionally specified models for nonignorable missing data
Proceedings of the National Academy of Sciences / Jul 28, 2020
Franks, A. M., Airoldi, E. M., & Rubin, D. B. (2020). Nonstandard conditionally specified models for nonignorable missing data. Proceedings of the National Academy of Sciences, 117(32), 19045–19053. https://doi.org/10.1073/pnas.1815563117
Quantifying Homologous Proteins and Proteoforms
Molecular & Cellular Proteomics / Jan 01, 2019
Malioutov, D., Chen, T., Airoldi, E., Jaffe, J., Budnik, B., & Slavov, N. (2019). Quantifying Homologous Proteins and Proteoforms. Molecular & Cellular Proteomics, 18(1), 162–168. https://doi.org/10.1074/mcp.tir118.000947
Configurable Security Protocols for Multi-party Data Analysis with Malicious Participants
21st International Conference on Data Engineering (ICDE'05)
Malin, B., Airoldi, E., Edoho-Eket, S., & Yiheng Li. (n.d.). Configurable Security Protocols for Multi-party Data Analysis with Malicious Participants. 21st International Conference on Data Engineering (ICDE’05). https://doi.org/10.1109/icde.2005.37
A computational approach to map nucleosome positions and alternative chromatin states with base pair resolution
eLife / Sep 13, 2016
Zhou, X., Blocker, A. W., Airoldi, E. M., & O’Shea, E. K. (2016). A computational approach to map nucleosome positions and alternative chromatin states with base pair resolution. ELife, 5. CLOCKSS. https://doi.org/10.7554/elife.16970
Influence Estimation on Social Media Networks Using Causal Inference
2018 IEEE Statistical Signal Processing Workshop (SSP) / Jun 01, 2018
Smith, S. T., Kao, E. K., Shah, D. C., Simek, O., & Rubin, D. B. (2018). Influence Estimation on Social Media Networks Using Causal Inference. 2018 IEEE Statistical Signal Processing Workshop (SSP). https://doi.org/10.1109/ssp.2018.8450823
Geometric Representations of Random Hypergraphs
Journal of the American Statistical Association / Jan 02, 2017
Lunagómez, S., Mukherjee, S., Wolpert, R. L., & Airoldi, E. M. (2017). Geometric Representations of Random Hypergraphs. Journal of the American Statistical Association, 112(517), 363–383. https://doi.org/10.1080/01621459.2016.1141686
Estimating Causal Effects on Social Networks
2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) / Oct 01, 2018
Forastiere, L., Mealli, F., Wu, A., & Airoldi, E. M. (2018). Estimating Causal Effects on Social Networks. 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA). https://doi.org/10.1109/dsaa.2018.00016
Causal Inference with Bipartite Designs
SSRN Electronic Journal / Jan 01, 2020
Doudchenko, N., Zhang, M., Drynkin, E., Airoldi, E. M., Mirrokni, V., & Pouget-Abadie, J. (2020). Causal Inference with Bipartite Designs. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3757188
Optimizing Cluster-based Randomized Experiments under Monotonicity
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining / Jul 19, 2018
Pouget-Abadie, J., Mirrokni, V., Parkes, D. C., & Airoldi, E. M. (2018). Optimizing Cluster-based Randomized Experiments under Monotonicity. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3219819.3220067
SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series
IEEE Transactions on Signal Processing / Oct 01, 2017
Han, Q., Ding, J., Airoldi, E. M., & Tarokh, V. (2017). SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series. IEEE Transactions on Signal Processing, 65(19), 4994–5005. https://doi.org/10.1109/tsp.2017.2716898
Analyzing Time-Evolving Networks
Handbook of Mixed Membership Models and Their Applications / Nov 06, 2014
Analyzing Time-Evolving Networks. (2014). Handbook of Mixed Membership Models and Their Applications, 523–560. https://doi.org/10.1201/b17520-35
Hybrid Mixed-Membership Blockmodel for Inference on Realistic Network Interactions
IEEE Transactions on Network Science and Engineering / Jul 01, 2019
Kao, E. K., Smith, S. T., & Airoldi, E. M. (2019). Hybrid Mixed-Membership Blockmodel for Inference on Realistic Network Interactions. IEEE Transactions on Network Science and Engineering, 6(3), 336–350. https://doi.org/10.1109/tnse.2018.2823324
Tractable learning of large Bayes net structures from sparse data
Twenty-first international conference on Machine learning - ICML '04 / Jan 01, 2004
Goldenberg, A., & Moore, A. (2004). Tractable learning of large Bayes net structures from sparse data. Twenty-First International Conference on Machine Learning - ICML ’04. https://doi.org/10.1145/1015330.1015406
Network Topology Inference
Springer Series in Statistics / Jan 01, 2009
Kolaczyk, E. D. (2009). Network Topology Inference. Statistical Analysis of Network Data, 1–48. https://doi.org/10.1007/978-0-387-88146-1_7
Estimating the total treatment effect in randomized experiments with unknown network structure
Proceedings of the National Academy of Sciences / Oct 24, 2022
Yu, C. L., Airoldi, E. M., Borgs, C., & Chayes, J. T. (2022). Estimating the total treatment effect in randomized experiments with unknown network structure. Proceedings of the National Academy of Sciences, 119(44). https://doi.org/10.1073/pnas.2208975119
Intersection of the Web-Based Vaping Narrative With COVID-19: Topic Modeling Study
Journal of Medical Internet Research / Oct 30, 2020
Janmohamed, K., Soale, A.-N., Forastiere, L., Tang, W., Sha, Y., Demant, J., Airoldi, E., & Kumar, N. (2020). Intersection of the Web-Based Vaping Narrative With COVID-19: Topic Modeling Study. Journal of Medical Internet Research, 22(10), e21743. https://doi.org/10.2196/21743
Stacking models for nearly optimal link prediction in complex networks
Proceedings of the National Academy of Sciences / Sep 04, 2020
Ghasemian, A., Hosseinmardi, H., Galstyan, A., Airoldi, E. M., & Clauset, A. (2020). Stacking models for nearly optimal link prediction in complex networks. Proceedings of the National Academy of Sciences, 117(38), 23393–23400. https://doi.org/10.1073/pnas.1914950117
Intersection of the Web-Based Vaping Narrative With COVID-19: Topic Modeling Study (Preprint)
Jun 29, 2020
Janmohamed, K., Soale, A.-N., Forastiere, L., Tang, W., Sha, Y., Demant, J., Airoldi, E., & Kumar, N. (2020). Intersection of the Web-Based Vaping Narrative With COVID-19: Topic Modeling Study (Preprint). https://doi.org/10.2196/preprints.21743
Limitations of Design-based Causal Inference and A/B Testing under Arbitrary and Network Interference
Sociological Methodology / Jul 18, 2018
Basse, G. W., & Airoldi, E. M. (2018). Limitations of Design-based Causal Inference and A/B Testing under Arbitrary and Network Interference. Sociological Methodology, 48(1), 136–151. https://doi.org/10.1177/0081175018782569
Scalable estimation strategies based on stochastic approximations: classical results and new insights
Statistics and Computing / Jun 11, 2015
Toulis, P., & Airoldi, E. M. (2015). Scalable estimation strategies based on stochastic approximations: classical results and new insights. Statistics and Computing, 25(4), 781–795. https://doi.org/10.1007/s11222-015-9560-y
A natural experiment of social network formation and dynamics
Proceedings of the National Academy of Sciences / May 11, 2015
Phan, T. Q., & Airoldi, E. M. (2015). A natural experiment of social network formation and dynamics. Proceedings of the National Academy of Sciences, 112(21), 6595–6600. https://doi.org/10.1073/pnas.1404770112
Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast
PLOS Genetics / May 07, 2015
Csárdi, G., Franks, A., Choi, D. S., Airoldi, E. M., & Drummond, D. A. (2015). Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast. PLOS Genetics, 11(5), e1005206. https://doi.org/10.1371/journal.pgen.1005206
Post-transcriptional regulation across human tissues
May 31, 2015
Franks, A., Airoldi, E., & Slavov, N. (2015). Post-transcriptional regulation across human tissues. https://doi.org/10.1101/020206
Predicting traffic volumes and estimating the effects of shocks in massive transportation systems
Proceedings of the National Academy of Sciences / Apr 20, 2015
Silva, R., Kang, S. M., & Airoldi, E. M. (2015). Predicting traffic volumes and estimating the effects of shocks in massive transportation systems. Proceedings of the National Academy of Sciences, 112(18), 5643–5648. https://doi.org/10.1073/pnas.1412908112
Estimating a Structured Covariance Matrix From Multilab Measurements in High-Throughput Biology
Journal of the American Statistical Association / Jan 02, 2015
Franks, A. M., Csárdi, G., Drummond, D. A., & Airoldi, E. M. (2015). Estimating a Structured Covariance Matrix From Multilab Measurements in High-Throughput Biology. Journal of the American Statistical Association, 110(509), 27–44. https://doi.org/10.1080/01621459.2014.964404
Generalized Species Sampling Priors With Latent Beta Reinforcements
Journal of the American Statistical Association / Oct 02, 2014
Airoldi, E. M., Costa, T., Bassetti, F., Leisen, F., & Guindani, M. (2014). Generalized Species Sampling Priors With Latent Beta Reinforcements. Journal of the American Statistical Association, 109(508), 1466–1480. https://doi.org/10.1080/01621459.2014.950735
Constant Growth Rate Can Be Supported by Decreasing Energy Flux and Increasing Aerobic Glycolysis
Cell Reports / May 01, 2014
Slavov, N., Budnik, B. A., Schwab, D., Airoldi, E. M., & van Oudenaarden, A. (2014). Constant Growth Rate Can Be Supported by Decreasing Energy Flux and Increasing Aerobic Glycolysis. Cell Reports, 7(3), 705–714. https://doi.org/10.1016/j.celrep.2014.03.057
Differential stoichiometry among core ribosomal proteins
May 26, 2014
Slavov, N., Semrau, S., Airoldi, E., Budnik, B., & van Oudenaarden, A. (2014). Differential stoichiometry among core ribosomal proteins. https://doi.org/10.1101/005553
Stephen E. Fienberg's Contributions to Categorical Data Analysis and the Social Sciences
CHANCE / Nov 01, 2013
Airoldi, E. M. (2013). Stephen E. Fienberg’s Contributions to Categorical Data Analysis and the Social Sciences. CHANCE, 26(4), 12–14. https://doi.org/10.1080/09332480.2013.868749
Correction: Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates
PLoS ONE / Oct 02, 2013
Geiler-Samerotte, K. A., Hashimoto, T., Dion, M. F., Budnik, B. A., Airoldi, E. M., & Drummond, D. A. (2013). Correction: Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates. PLoS ONE, 8(10). https://doi.org/10.1371/annotation/9f5465d9-e9fa-4a80-84ca-9c9a3f6e82c7
Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates
PLoS ONE / Sep 25, 2013
Geiler-Samerotte, K. A., Hashimoto, T., Dion, M. F., Budnik, B. A., Airoldi, E. M., & Drummond, D. A. (2013). Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates. PLoS ONE, 8(9), e75320. https://doi.org/10.1371/journal.pone.0075320
Estimating Latent Processes on a Network From Indirect Measurements
Journal of the American Statistical Association / Mar 01, 2013
Airoldi, E. M., & Blocker, A. W. (2013). Estimating Latent Processes on a Network From Indirect Measurements. Journal of the American Statistical Association, 108(501), 149–164. https://doi.org/10.1080/01621459.2012.756328
Estimation of exchangeable graph models by stochastic blockmodel approximation
2013 IEEE Global Conference on Signal and Information Processing / Dec 01, 2013
Chan, S. H., Costa, T. B., & Airoldi, E. M. (2013). Estimation of exchangeable graph models by stochastic blockmodel approximation. 2013 IEEE Global Conference on Signal and Information Processing. https://doi.org/10.1109/globalsip.2013.6736873
Multi-way blockmodels for analyzing coordinated high-dimensional responses
The Annals of Applied Statistics / Dec 01, 2013
Airoldi, E. M., Wang, X., & Lin, X. (2013). Multi-way blockmodels for analyzing coordinated high-dimensional responses. The Annals of Applied Statistics, 7(4). https://doi.org/10.1214/13-aoas643
Confidence sets for network structure
Statistical Analysis and Data Mining / Sep 09, 2011
Airoldi, E. M., Choi, D. S., & Wolfe, P. J. (2011). Confidence sets for network structure. Statistical Analysis and Data Mining, 4(5), 461–469. https://doi.org/10.1002/sam.10136
Network sampling and classification: An investigation of network model representations
Decision Support Systems / Jun 01, 2011
Airoldi, E. M., Bai, X., & Carley, K. M. (2011). Network sampling and classification: An investigation of network model representations. Decision Support Systems, 51(3), 506–518. https://doi.org/10.1016/j.dss.2011.02.014
An entropy approach to disclosure risk assessment: Lessons from real applications and simulated domains
Decision Support Systems / Apr 01, 2011
Airoldi, E. M., Bai, X., & Malin, B. A. (2011). An entropy approach to disclosure risk assessment: Lessons from real applications and simulated domains. Decision Support Systems, 51(1), 10–20. https://doi.org/10.1016/j.dss.2010.11.014
Ranking relations using analogies in biological and information networks
The Annals of Applied Statistics / Jun 01, 2010
Silva, R., Heller, K., Ghahramani, Z., & Airoldi, E. M. (2010). Ranking relations using analogies in biological and information networks. The Annals of Applied Statistics, 4(2). https://doi.org/10.1214/09-aoas321
Systems-level dynamic analyses of fate change in murine embryonic stem cells
Nature / Nov 19, 2009
Lu, R., Markowetz, F., Unwin, R. D., Leek, J. T., Airoldi, E. M., MacArthur, B. D., Lachmann, A., Rozov, R., Ma’ayan, A., Boyer, L. A., Troyanskaya, O. G., Whetton, A. D., & Lemischka, I. R. (2009). Systems-level dynamic analyses of fate change in murine embryonic stem cells. Nature, 462(7271), 358–362. https://doi.org/10.1038/nature08575
Predicting Cellular Growth from Gene Expression Signatures
PLoS Computational Biology / Jan 02, 2009
Airoldi, E. M., Huttenhower, C., Gresham, D., Lu, C., Caudy, A. A., Dunham, M. J., Broach, J. R., Botstein, D., & Troyanskaya, O. G. (2009). Predicting Cellular Growth from Gene Expression Signatures. PLoS Computational Biology, 5(1), e1000257. https://doi.org/10.1371/journal.pcbi.1000257
Network Analysis of Wikipedia
Statistical Methods in e-Commerce Research
Warren, R. H., Airoldi, E. M., & Banks, D. L. (n.d.). Network Analysis of Wikipedia. Statistical Methods in E-Commerce Research, 81–102. https://doi.org/10.1002/9780470315262.ch5
Discovery of Latent Patterns with Hierarchical Bayesian Mixed-Membership Models and the Issue of Model Choice
Data Mining Patterns / Jan 01, 2008
Joutard, C. J., Airoldi, E. M., Edoardo M., S. E., & Love, T. M. (2008). Discovery of Latent Patterns with Hierarchical Bayesian Mixed-Membership Models and the Issue of Model Choice. Data Mining Patterns, 240–275. https://doi.org/10.4018/978-1-59904-162-9.ch011
Whose Ideas? Whose Words? Authorship of Ronald Reagan's Radio Addresses
PS: Political Science & Politics / Jul 01, 2007
Airoldi, E. M., Fienberg, S. E., & Skinner, K. K. (2007). Whose Ideas? Whose Words? Authorship of Ronald Reagan’s Radio Addresses. PS: Political Science & Politics, 40(3), 501–506. https://doi.org/10.1017/s1049096507070874
Combining Stochastic Block Models and Mixed Membership for Statistical Network Analysis
Statistical Network Analysis: Models, Issues, and New Directions
Airoldi, E. M., Blei, D. M., Fienberg, S. E., & Xing, E. P. (n.d.). Combining Stochastic Block Models and Mixed Membership for Statistical Network Analysis. Lecture Notes in Computer Science, 57–74. https://doi.org/10.1007/978-3-540-73133-7_5
Getting Started in Probabilistic Graphical Models
PLoS Computational Biology / Dec 07, 2007
Airoldi, E. M. (2007). Getting Started in Probabilistic Graphical Models. PLoS Computational Biology, 3(12), e252. https://doi.org/10.1371/journal.pcbi.0030252
Statistical Network Analysis: Models, Issues, and New Directions
Lecture Notes in Computer Science / Jan 01, 2007
Airoldi, E., Blei, D. M., Fienberg, S. E., Goldenberg, A., Xing, E. P., & Zheng, A. X. (Eds.). (2007). Statistical Network Analysis: Models, Issues, and New Directions. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-73133-7
Who wrote Ronald Reagan's radio addresses?
Bayesian Analysis / Jun 01, 2006
Airoldi, E. M., Anderson, A. G., Fienberg, S. E., & Skinner, K. K. (2006). Who wrote Ronald Reagan’s radio addresses? Bayesian Analysis, 1(2). https://doi.org/10.1214/06-ba110
Integrating Utility into Face De-identification
Privacy Enhancing Technologies / Jan 01, 2006
Gross, R., Airoldi, E., Malin, B., & Sweeney, L. (2006). Integrating Utility into Face De-identification. Lecture Notes in Computer Science, 227–242. https://doi.org/10.1007/11767831_15
Markov Blankets and Meta-heuristics Search: Sentiment Extraction from Unstructured Texts
Advances in Web Mining and Web Usage Analysis / Jan 01, 2006
Airoldi, E., Bai, X., & Padman, R. (2006). Markov Blankets and Meta-heuristics Search: Sentiment Extraction from Unstructured Texts. Lecture Notes in Computer Science, 167–187. https://doi.org/10.1007/11899402_11
The Effects of Location Access Behavior on Re-identification Risk in a Distributed Environment
Privacy Enhancing Technologies / Jan 01, 2006
Malin, B., & Airoldi, E. (2006). The Effects of Location Access Behavior on Re-identification Risk in a Distributed Environment. Lecture Notes in Computer Science, 413–429. https://doi.org/10.1007/11957454_24
A Network Analysis Model for Disambiguation of Names in Lists
Computational and Mathematical Organization Theory / Jul 01, 2005
Malin, B., Airoldi, E., & Carley, K. M. (2005). A Network Analysis Model for Disambiguation of Names in Lists. Computational and Mathematical Organization Theory, 11(2), 119–139. https://doi.org/10.1007/s10588-005-3940-3
A latent mixed membership model for relational data
Proceedings of the 3rd international workshop on Link discovery / Aug 21, 2005
Airoldi, E., Blei, D., Xing, E., & Fienberg, S. (2005). A latent mixed membership model for relational data. Proceedings of the 3rd International Workshop on Link Discovery. https://doi.org/10.1145/1134271.1134283
Sampling algorithms for pure network topologies
ACM SIGKDD Explorations Newsletter / Dec 01, 2005
Airoldi, E. M., & Carley, K. M. (2005). Sampling algorithms for pure network topologies. ACM SIGKDD Explorations Newsletter, 7(2), 13–22. https://doi.org/10.1145/1117454.1117457
Recovering latent time-series from their observed sums
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining / Aug 22, 2004
Airoldi, E., & Faloutsos, C. (2004). Recovering latent time-series from their observed sums. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/1014052.1014059
Statistical Challenges in Network Analysis
SSRN Electronic Journal / Jan 01, 2009
Airoldi, E. M., Bai, X., & Carley, K. M. (2009). Statistical Challenges in Network Analysis. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1545606
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