Publications

NSF AI+MPS working group (2025). The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS). Community Paper from the NSF Future of AI+MPS Workshop Cambridge, Massachusetts, March 24-26, 2025.

James Carzon, Luca Masserano, Joshua D. Ingram, Alex Shen, Antonio Carlos Herling Ribeiro Junior, Tommaso Dorigo, Michele Doro, Joshua S. Speagle, Rafael Izbicki, Ann B. Lee (2025). Trustworthy scientific inference for inverse problems with generative models.

James Carzon, Aishik Ghosh, Rafael Izbicki, Ann B. Lee, Luca Masserano, Daniel Whiteson (2025). On Focusing Statistical Power for Searches and Measurements in Particle Physics.

Luca Masserano, Alex Shen, Michele Doro, Tommaso Dorigo, Rafael Izbicki, Ann B. Lee (2024). Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference. Proceedings of the Forty-First International Conference on Machine Learning (ICML 2024), PMLR 235, 2024.

Tommaso Dorigo, Max Aehle, Cornelia Arcaro, Muhammad Awais, Fabiola Bergamaschi, Julien Donini, Michele Doro, Nicolas R. Gauger, Rafael Izbicki, Jan Kieseler, Ann Lee, Luca Masserano, Federico Nardi, Raaghav Rajesh, Luis Recabarren Vergara, Alexander Shen (2023). Toward the end-to-end optimization of the SWGO array layout. Nuclear Physics B, Volume 1017, 116934, April 2025.

Biprateep Dey, David Zhao, Jeffrey A. Newman, Brett H. Andrews, Rafael Izbicki, Ann B. Lee (2022). Towards Instance-Wise Calibration: Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR). Accepted to Machine Learning Science and Technology, November 2025.

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Trey McNeely, Pavel Khokhlov, Niccolò Dalmasso, Kimberly M. Wood, Ann B. Lee (2022). Structural Forecasting for Short-Term Tropical Cyclone Intensity Guidance. Weather and Forecasting, 38(6):985-998, 2023.

Luca Masserano, Tommaso Dorigo, Rafael Izbicki, Mikael Kuusela, Ann B. Lee (2022). Simulator-Based Inference with WALDO: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems. Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023), PMLR 206:2960-2974, 2023. (Finalist at the ASA SPES and Q&P Student Paper Competition).

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Trey McNeely, Galen Vincent, Kimberly M. Wood, Rafael Izbicki, Ann B. Lee (2022). Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery. Annals of Applied Statistics 17(2):1260-1284, June 2023. (Selected for The Best of AOAS invited paper session at JSM 2023).

Niccolò Dalmasso, Luca Masserano, David Zhao, Rafael Izbicki, Ann B. Lee (2021). Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference. Electron. J. Statist. 18(2): 5045-5090, 2024.

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Niccolò Dalmasso, Luca Masserano, David Zhao, Rafael Izbicki, Ann B. Lee (2021). Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference. Electron. J. Statist. 18(2): 5045-5090, 2024.

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David Zhao, Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee (2021). Diagnostics for Conditional Density Models and Bayesian Inference Algorithms. Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021). PMLR 161:1830-1840, 2021.

Addison J. Hu, Mikael Kuusela, Ann B. Lee, Donata Giglio, Kimberly M. Wood (2020). Spatio-temporal methods for estimating subsurface ocean thermal response to tropical cyclones. Advances in Statistical Climatology, Meteorology and Oceanography, 10(2):69-93, 2024.

Trey McNeely, Niccolò Dalmasso, Kimberly M. Wood, Ann B. Lee (2020). Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning. Tackling Climate Change with Machine Learning workshop at NeurIPS 2020 (Spotlight talk).

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Lorenzo Tomaselli, Coty Jen, Ann B. Lee (2020). Wildfire Smoke and Air Quality: How Machine Learning Can Guide Forest Management. Tackling Climate Change with Machine Learning workshop at NeurIPS 2020 (Spotlight talk).

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Niccolò Dalmasso, Galen Vincent, Dorit Hammerling, Ann B. Lee (2020). HECT: High-Dimensional Ensemble Consistency Testing for Climate Models. Tackling Climate Change with Machine Learning workshop at NeurIPS 2020.

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Trey McNeely, Ann B. Lee, Kimberly M. Wood, Dorit Hammerling (2020). Unlocking GOES: A Statistical Framework for Quantifying the Evolution of Convective Structure in Tropical Cyclones. Journal of Applied Meteorology and Climatology (JAMC), 59(10):1671-1689, 2020.

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Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee (2020). Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting. Proceedings of the Thirty-Seventh International Conference on Machine Learning (ICML 2020), PMLR 119:2323-2334, 2020.

S. J. Schmidt et al. (24 members of the LSST Dark Energy Science Collaboration) (2020). Evaluation of probabilistic photometric redshift estimation approaches for LSST. Monthly Notices of the Royal Astronomical Society, Volume 499, Issue 2, December 2020, Pages 1587-1606.

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Niccolò Dalmasso, Taylor Pospisil, Ann B. Lee, Rafael Izbicki, Peter E. Freeman, Alex I. Malz (2019). Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference. Astronomy and Computing, Volume 30, January 2020.

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Niccolò Dalmasso, Ann B. Lee, Rafael Izbicki, Taylor Pospisil, Ilmun Kim, Chieh-An Lin (2019). Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS 2020), PMLR 108:3349-3361, 2020.

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Ilmun Kim, Ann B. Lee, Jing Lei (2019). Global and Local Two-Sample Tests via Regression. Electronic Journal of Statistics, 13(2): 5253-5305, 2019.

Taylor Pospisil, Ann B. Lee (2019). (f)RFCDE: Random Forests for Conditional Density Estimation and Functional Data.

Chien-Hao Lin, Joachim Harnois-Déraps, Tim Eifler, Taylor Pospisil, Rachel Mandelbaum, Ann B. Lee, Sukhdeep Singh (2019). Non-Gaussianity in the Weak Lensing Correlation Function Likelihood - Implications for Cosmological Parameter Biases. Monthly Notices of the Royal Astronomical Society, Volume 499, Issue 2, December 2020, Pages 2977-2993.

Rafael Izbicki, Ann B. Lee, Taylor Pospisil (2018). ABC-CDE: Toward Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations. Journal of Computational and Graphical Statistics, 28(3): 481-492, 2019.

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Peter E. Freeman, Ilmun Kim, Ann B. Lee (2017). Local Two-Sample Testing: A New Tool for Analysing High-Dimensional Astronomical Data. Monthly Notices of the Royal Astronomical Society (MNRAS), 471(3): 3273-3282, 2017.

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Rafael Izbicki, Ann B. Lee (2017). Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation. Electronic Journal of Statistics, 11(2): 2800-2831, 2017.

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Peter E. Freeman, Rafael Izbicki, Ann B. Lee (2017). A unified framework for constructing, tuning and assessing photometric redshift density estimates in a selection bias setting. Monthly Notices of the Royal Astronomical Society, 468(4): 4556-4565, 2017.

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Rafael Izbicki, Ann B. Lee, Peter E. Freeman (2016). Photo-z Estimation: An Example of Nonparametric Conditional Density Estimation under Selection Bias. The Annals of Applied Statistics, 11(2): 698-724, 2017.

Rafael Izbicki, Ann B. Lee (2015). Nonparametric Conditional Density Estimation in a High-Dimensional Regression Setting.. Journal of Computational and Graphical Statistics, 25(4): 1297-1316, 2016.

Ann B. Lee, Rafael Izbicki (2015). A Spectral Series Approach to High-Dimensional Nonparametric Regression. Electronic Journal of Statistics, 10(1): 423-463, 2016.

Trent Gaugler, Lambertus Klei, Stephan J Sanders, Corneliu A Bodea, Arthur P Goldberg, Ann B Lee, Milind Mahajan, Dina Manaa, Yudi Pawitan, Jennifer Reichert, Stephan Ripke, Sven Sandin, Pamela Sklar, Oscar Svantesson, Abraham Reichenberg, Christina M Hultman, Bernie Devlin, Kathryn Roeder, Joseph D Buxbaum (2014). Most genetic risk for autism resides with common variation. Nature Genetics, 46(8): 881–885, 2014.

Rafael Izbicki, Ann B. Lee, Chad M. Schafer (2014). High-Dimensional density ratio estimation with extensions to approximate likelihood computation. Proceedings of Machine Learning Research (AISTATS 2014), PMLR 33: 420-429, 2014.

Andrew Crossett, Ann B. Lee, Lambertus Klei, Bernie Devlin, Kathryn Roeder (2013). Refining genetically inferred relationships using treelet covariance smoothing. The Annals of Applied Statistics, 7(2): 669-690, 2013.

P. E. Freeman, R. Izbicki, A. B. Lee, J. A. Newman, C. J. Conselice, A. M. Koekemoer, J. M. Lotz, M. Mozena (2013). New Image Statistics for Detecting Disturbed Galaxy Morphologies at High Redshift. Monthly Notices of the Royal Astronomical Society, 434(1): 282-295, 2013.

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