Edoardo Airoldi

Professor of Statistics & Data Science Temple University & PI, Harvard University

Research Expertise

Statistics
Causal Inference
Network Science
Cell Biology
Molecular Biology
Pulmonary and Respiratory Medicine
Pediatrics, Perinatology and Child Health
Biochemistry
Biotechnology
Artificial Intelligence
Human-Computer Interaction
Software
Statistics, Probability and Uncertainty
Statistics and Probability
Applied Mathematics
Cancer Research
Genetics (clinical)
Genetics
Ecology, Evolution, Behavior and Systematics
Computational Mathematics
Computational Theory and Mathematics
Computer Science Applications
Cellular and Molecular Neuroscience
Ecology
Modeling and Simulation
Library and Information Sciences
Information Systems and Management
Computer Networks and Communications
Information Systems
Management Information Systems
Developmental and Educational Psychology
Epidemiology
Theoretical Computer Science
Sociology and Political Science
Analysis
Behavioral Neuroscience
Experimental and Cognitive Psychology
Social Psychology
Analytical Chemistry
Health Informatics
Pharmacology (medical)
Control and Systems Engineering
Electrical and Electronic Engineering
Signal Processing

About

Edoardo Airoldi is a Professor in the Department of Machine Learning at Temple University. He is also the Director of the Center for Machine Learning and Health. He is a world-renowned expert in the fields of machine learning and artificial intelligence, with a focus on applications to health. Airoldi is a member of the prestigious Association for the Advancement of Artificial Intelligence (AAAI) and the International Machine Learning Society (IMLS). He has published over 200 papers in leading journals and conferences, and his work has been covered by various media outlets including The New York Times, The Wall Street Journal, The Economist, and Wired.

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

Education

Università Bocconi

B.Sc., Institute for Quantitative Methods

Milano

Carnegie Mellon University

Ph.D., School of Computer Science

Pittsburgh, Pennsylvania, United States of America

Experience

Harvard University

Links & Social Media

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