Grants

• PI, “Robust Experimental Designs for Measuring Spillover Effects in Large Networked Populations”, NSF SES-2419009
  with Best, M. (2024-2026)

Randomization Tests and Causal Inference

• Estimation of causal effects under non-individualistic treatments due to network entanglement
  with Volfovsky, A., Airoldi, EM. (2024), Biometrika (forthcoming).

• Randomization tests for peer effects in group formation experiments — slides
  with Basse, G., Ding, P., Feller, A. (2024), Econometrica, 92(2), pp. 567–590.

• Minimax designs for causal effects in temporal experiments with treatment habituation
  with Basse, G., Ding, Yi (2023). Biometrika, 110(1), pp. 155-168.

• A graph-theoretic approach to randomization tests of causal effects under interference — arxiv code slides | CBR Article
  with Puelz, D., Basse, G., Feller, A. (2022). Journal of the Royal Statistical Society, Series B, 84(1), pp.174-204.

• Randomization tests in observational studies with staggered adoption of treatment — code slides
  with Shaikh, A. (2021). Journal of the American Statistical Association, 116(536), pp. 1835-1848.

• Estimation of Covid-19 prevalence from serology tests: A partial identification approach — code slides | CBR Article (most read in 2020)
  (2020) Journal of Econometrics, 220(1), pp. 193-213.

• Randomization tests of causal effects under interference — supplement slides
  with Basse, G., Feller, A. (2019). Biometrika, 106(2), pp. 487-494.

• Long-term causal effects via behavioral game theory — slides
  with Parkes, DC. (2016). Neural Information and Processing Systems (NIPS).

• Estimation of causal peer influence effects
  with Kao, E. (2013). International Conference on Machine Learning (ICML), oral.

Statistical Machine Learning

• “Plus/Minus the Learning Rate”: Easy and Scalable Inference with SGD - slides
  with Chee, J., Kim, H. (2023). International Conference on AI and Statistics (AISTATS’23).

• Convergence and Stability of the Stochastic Proximal Point Algorithm with Momentum
  with Kim, JL, ad Kyrillidis, A. (2022). Proceedings of The 4th Annual Learning for Dynamics and Control Conference.

• Robust inference for high-dimensional linear models via residual randomization
  with Wang, S., Lee S.K., Kolar, M. (2021). International Conference on Machine Learning (ICML), oral.

• The proximal Robbins-Monro method — supplement code slides
  with Horel, T., Airoldi, EM. (2021). Journal of the Royal Statistical Society, Series B, 83(1), pp. 188-212.

• Dynamical systems theory for causal inference with application to synthetic controls — slides
  with Ding, Yi (2020). International Conference on AI and Statistics (AISTATS’20).

• Convergence diagnostics for stochastic gradient descent with constant step size — slides
  with Chee, J. (2018). International Conference on AI and Statistics (AISTATS’18), oral.

• Asymptotic and finite-sample properties of estimators based on stochastic gradients —code slides errata supplement errata
  with Airoldi, EM. (2017). Annals of Statistics, 45(4), pp. 1694-1727.

• Towards stability and optimality in stochastic gradient descent
  with Tran, D., Airoldi, EM. (2016). International Conference on AI and Statistics (AISTATS’16), oral

• Scalable estimation strategies based on stochastic approximations: classical results and new insights
  with Airoldi, EM. (2015). Statistics and Computing, 25(4), pp. 781-795.

• Statistical analysis of stochastic gradient methods for generalized linear models
  with Airoldi EM, and Rennie J. (2014). International Conference on Machine Learning (ICML’14).

Microeconomics and Networks

• Design and analysis of multi-hospital kidney-exchanges using random graphs — code slides
  with Parkes, DC. (2015). Games and Economic Behavior, 91, pp. 360-382.

• Incentive-compatible experimental design — slides
  with Zhou, J., Pfeffer, E., Parkes, DC. (2015).  Economics and Computation (EC’15).

• A Random Graph Model of Kidney Exchanges: Efficiency, Individual Rationality, Incentives
  with Parkes, DC. (2011). Economics and Computation (EC'11).

• Online social networking, Face Recognition, and Interactive Robotics
  with Mavridis, N., Kazmi, W., Ben-AbdelKader, C.(2009). CASoN'09.

Book chapters

• Stochastic gradient methods for estimation with large datasets
  with Airoldi, EM. (2016). Handbook of Big Data, CRC Press, eds. Buhlmann et. al.

• Friends with Faces: How Social Networks Can Enhance Face Recognition and Vice Versa
  with Mavridis, N., Kazmi, W. (2010). Computational Social Network Analysis, Springer London, eds. A. Ajith et. al.