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Education
PhD, Statistics, Harvard University, 2014
AM, Statistics, Harvard University, 2011
BS, Mathematics, Purdue University, 2009
BS, Mathematical Statistics, Purdue University, 2009
News
August 19 - 20, 2022: 2022 Purdue Krannert-Statistics Causal Machine Learning for Novel Settings Boot Camp.
July 13 - 15, 2021: 2021 Purdue Krannert-Statistics Machine Learning and Causal Inference Boot Camp.
December 17, 2020: Arman Sabbaghi becomes an Elected Member of the International Statistical Institute.
June 11, 2020: Purdue Innovators Use Funding to Advance Technologies.
June 11, 2020: Six Purdue innovators advance their technologies through Trask funding.
November 22, 2019: AMapi: An Application Programming Interface for the Control of Additive Manufacturing Systems.
October 2019: Arman Sabbaghi profiled in Purdue University College of Science Insights Magazine.
May 23, 2019: Innovations for the next 150 years were on display, available for licensing at annual Purdue Technology Showcase.
May 21, 2019: Automated machine learning for shape deviation modeling in additive manufacturing systems.
April 8, 2019: Purdue statisticians developing AI/ML methods for additive manufacturing.
February 7, 2019: AI technology addresses parts accuracy, a major manufacturing challenge in 3D printing for $7.3 billion industry.
November 12, 2018: Raquel De Souza Borges Ferreira recognized at the 2018 INFORMS QSR Best Student Paper Competition and the Data Mining Best Theoretical Paper Competition.
April 2015: Quality control for additive manufacturing.
Files for the 2022 Purdue Krannert-Statistics Causal Machine Learning for Novel Settings Boot Camp
Introduction to Fundamental Concepts in Causal Inference and ML Approaches of Causal Inference
Lalonde_observational_data.txt
Recent Publications
Ohnishi Y., Sabbaghi A. (2022). A Bayesian analysis of two-stage randomized experiments in the presence of interference, treatment nonadherence, and missing outcomes. Bayesian Analysis (accepted).
Geng Z., Sabbaghi A., Bidanda B. (2022). Automated variance modeling for three-dimensional point cloud data via Bayesian neural networks. IISE Transactions (in press). DOI: 10.1080/24725854.2022.2106389.
Geng Z., Sabbaghi A., Bidanda B. (2022). Reconstructing original design: Process planning for reverse engineering. IISE Transactions (in press). DOI: 10.1080/24725854.2022.2040761.
Geng Z., Sabbaghi A., Bidanda B. (2022). A framework of tolerance specification for freeform point clouds and capability analysis for reverse engineering processes. International Journal of Production Research (Special issue of ``Editorial Board contributions celebrating the 60th Anniversary of IJPR'') 60:24, 7475-7491.
Jensen C.L., Rodriguez K.E., MacLean E.L., Wahab A.H.A., Sabbaghi A., O'Haire M.E. (2022). Characterizing veteran and PTSD service dog teams: Exploring potential mechanisms of symptom change and canine predictors of efficacy. PLoS One 17(7): e0269186.
Keaton T.J., Sabbaghi A. (2022). Dismemberment and design for controlling the risk of regret for the multi-armed bandit. Journal of Statistical Theory and Practice (AISC-2021 Special Collection) 16:55, 1-29.
Nieforth L.O., Abdul Wahab A.H., Sabbaghi A., Wadsworth S.M., Foti D., O'Haire M.E. (2022). Quantifying the emotional experiences of partners of veterans with PTSD service dogs using ecological momentary assessment. Complementary Therapies in Clinical Practice 48: 101590.
Zhang Y., Sabbaghi A. (2021). The designed bootstrap for causal inference in Big Observational Data. Journal of Statistical Theory and Practice (Special issue of ``State of the Art in Research on Design and Analysis of Experiments'') 15(4): 1-26.
Odimayomi T., Proctor C.R., Wang Q.E., Sabbaghi A., Peterson K.S., Yu D., Lee J., Shah A.D., Ley C., Noh Y., Smith C., Webster J., Milinkevich K., Lodewyk M., Jenks J., Smith J., Whelton A.J. (2021). Water safety attitudes, risk perception, experiences, and education for households impacted by the 2018 Camp Fire, California. Natural Hazards 108: 947-975.
Sabbaghi A. (2021). An integrative framework for geometric and hidden projections in three-level fractional factorial designs. Journal of Statistical Planning and Inference 215: 257-267. (supplement, R Markdown supplement)
Carroll C.C., Patel S.H., Simmons J., Gorden B.D., Olsen J.F., Chemelewski K., Saw S.K., Hale T.M., Howden R., Sabbaghi A. (2020). The impact of genistein supplementation on tendon functional properties and gene expression in estrogen deficient rats. Journal of Medicinal Food. 23(12): 1266-1274.
Francis J., Sabbaghi A., Shankar R., Ghasri-Khouzani M., Bian L. (2020). Efficient distortion prediction of additively manufactured parts using Bayesian model transfer between material systems. ASME Journal of Manufacturing Science and Engineering. 142(5): 051001 (16 pages).
Ferreira R., Sabbaghi A., Huang Q. (2020). Automated geometric shape deviation modeling for additive manufacturing systems via Bayesian neural networks. IEEE Transactions on Automation Science and Engineering. 17(2): 584-598.
Sabbaghi A. (2020). An algebra for the conditional main effect parameterization. Statistica Sinica. 30(2): 903-924.