Arman Sabbaghi

Assistant Professor in the Area of Applied Statistics
Associate Director, Statistical Consulting Service
Department of Statistics
Purdue University

Contact Information
Purdue University Department of Statistics
150 North University Street
West Lafayette, IN 47907-2066
Office: MATH 204
E-mail: sabbaghi@purdue.edu
Phone: (765) 496-0234
Fax: (765) 494-0558

Home | Curriculum Vitæ | Research | FACAM Blog | Presentations | Teaching | Google Scholar | ResearchGate | LinkedIn


Invited Talks

[43] Deviation Modeling in Additive Manufacturing Systems - Institute of Industrial & Systems Engineers (IISE) Quality Control and Reliability Engineering (QCRE) Division Webinar, November 27, 2018.

[42] Bayesian Model Building From Small Samples of Disparate Data for Capturing In-Plane Deviation in Additive Manufacturing - INFORMS 2018 Annual Meeting Technometrics Invited Session, Phoenix, AZ, November 4, 2018.

[41] An Algebra for the Conditional Main Effects Parameterization - 2018 International Conference on Advances in Interdisciplinary Statistics and Combinatorics, Greensboro, NC, October 6, 2018.

[40] Geometric Shape Deviation Modeling Across Different Processes and Shapes in Additive Manufacturing Systems - 2018 Fall Technical Conference, West Palm Beach, FL, October 5, 2018.

[39] Developments in Design: From Neyman and Fisher to Google and Beyond - St. Mary's College of California School of Economics and Business Administration, Moraga, CA, September 15, 2018.

[38] Automated Geometric Shape Deviation Modeling for Cyber-Physical Additive Manufacturing Systems via Bayesian Neural Networks - University of Southern California Center for Cyber-Physical Systems and the Internet of Things (CCI) and Ming Hsieh Institute for Electrical Engineering Seminar, Los Angeles, CA, March 21, 2018.

[37] Input Correction Algorithms to Produce Better Quality Parts - Second Foundation of Accuracy Control for Additive Manufacturing Workshop (FACAM 2018), Los Angeles, CA, February 8, 2018.

[36] Deviation Modeling Across Different Process Conditions and Shapes in Additive Manufacturing Systems - Second Foundation of Accuracy Control for Additive Manufacturing Workshop (FACAM 2018), Los Angeles, CA, February 8, 2018.

[35] Model Transfer Across Additive Manufacturing Processes via Mean Effect Equivalence of Lurking Variables - University of Louisville Department of Bioinformatics and Biostatistics Seminar Series, Louisville, KY, February 2, 2018.

[34] Predictive Model Building Across Different Process Conditions and Shapes in 3D Printing - INFORMS 2017 Annual Meeting, Houston, TX, October 22, 2017.

[33] Predictive Model Building Across Different Process Conditions and Shapes in Additive Manufacturing - Sandia National Laboratories Statistical Sciences Colloquium, Albuquerque, NM, September 21, 2017.

[32] Predictive Model Building Across Different Process Conditions and Shapes in Additive Manufacturing - Accelerating NSF Research in Additive Manufacturing toward Industrial Applications Workshop, Pittsburgh, PA, August 18, 2017.

[31] Deformation Model Transfer via Equivalent Effects of Lurking Variables in Additive Manufacturing - 2017 Joint Statistical Meetings, Baltimore, MD, August 2, 2017.

[30] Predictive Model Building Across Different Process Conditions and Shapes in 3D Printing - 24th Annual ASA/IMS Spring Research Conference on Statistics in Industry and Technology, New Brunswick, NJ, May 18, 2017.

[29] Deformation Model Transfer via Equivalent Effects of Lurking Variables in Additive Manufacturing - Purdue University School of Industrial Engineering Seminar Series, West Lafayette, IN, February 8, 2017.

[28] Deformation Model Transfer via Equivalent Effects of Lurking Variables in Additive Manufacturing - INFORMS 2016 Annual Meeting, Nashville, TN, November 13, 2016.

[27] Model Transfer via Equivalent Effects of Lurking Variables - 2016 NIC-ASA and ICSA Midwest Joint Fall Meeting, Lincolnshire, IL, November 11, 2016.

[26] Smart Calibration Through Deep Learning for High-Confidence and Interoperable Cyber-Physical Additive Manufacturing Systems - 2016 National Science Foundation Cyber-Physical Systems Program Principal Investigators Meeting , Arlington, VA, October 31, 2016.

[25] Hidden Connections Between Different Projections under the Linear-Quadratic Parameterization - 2016 International Conference on Advances in Interdisciplinary Statistics and Combinatorics, Greensboro, NC, September 30, 2016.

[24] Predictive Model Building Across Different Process Conditions and Shapes in 3D Printing - Twelfth Annual IEEE International Conference on Automation Science and Engineering, Forth Worth, TX, August 23, 2016.

[23] Discussion of Powerful Experimental Designs for Non-Gaussian Responses Invited Session - 2016 Joint Statistical Meetings, Chicago, IL, August 3, 2016.

[22] Partial Aliasing Relations in Mixed Two- and Three-Level Designs - 2016 ICSA Applied Statistics Symposium, Atlanta, GA, June 14, 2016.

[21] Causal Model Transfer via Equivalent Effects of Lurking Variables - 23nd Annual ASA/IMS Spring Research Conference on Statistics in Industry and Technology, Chicago, IL, May 25, 2016.

[20] Causal Model Transfer via Equivalent Effects of Lurking Variables - Theoretical Foundations for Accuracy Control in Additive Manufacturing Workshop (FACAM 2016), Los Angeles, CA, January 18, 2016.

[19] Model Building from Small Samples of Disparate Data in 3D Printing - Theoretical Foundations for Accuracy Control in Additive Manufacturing Workshop (FACAM 2016), Los Angeles, CA, January 18, 2016.

[18] New Perspectives on Tests for Co-Primary and Secondary Endpoints - Epstein Institute Seminar, Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, November 17, 2015.

[17] Partial Aliasing Relations in Mixed Two- and Three-Level Designs - INFORMS 2015 Annual Meeting, Philadelphia, PA, November 4, 2015.

[16] Bayesian Additive Modeling for Quality Control of 3D Printed Products - INFORMS 2015 Annual Meeting, Philadelphia, PA, November 1, 2015.

[15] Bayesian Additive Modeling for Quality Control of 3D Printed Products - Eleventh Annual IEEE International Conference on Automation Science and Engineering (CASE 2015), Gothenburg, Sweden, August 26, 2015.

[14] New Perspectives on Randomization Tests for Co-Primary and Secondary Endpoints - 2015 Joint Statistical Meetings, Seattle, WA, August 10, 2015.

[13] Hidden Connections Between Different Projections under the Linear-Quadratic Parameterization - 60th ISI World Statistics Congress, Rio de Janeiro, Brazil, July 27, 2015.

[12] Hidden Connections Between Different Projections under the Linear-Quadratic Parameterization - 32nd Quality & Productivity Research Conference, Raleigh, NC, June 11, 2015.

[11] Bayesian Additive Modeling for Quality Control of 3D Printed Products - 32nd Quality & Productivity Research Conference, Raleigh, NC, June 10, 2015.

[10] Inference for Deformation and Interference in 3D Printing - 2nd Workshop on Predictive Modeling and Control of Additive Manufacturing, Epstein Institute at the Viterbi School of Engineering, University of Southern California, Los Angeles, CA, November 13, 2014.

[9] Projection Properties of Three-Level Fractional Factorial Designs under the Linear-Quadratic System - INFORMS 2014 Annual Meeting, San Francisco, CA, November 9, 2014.

[8] Interference in Deformation Compensation for 3D Printing - NASA Engineering and Safety Center's (NESC) Engineering Statistics Team, May 21, 2014.

[7] The Power of Potential Outcomes in Experimental Design: From the Neyman-Fisher Controversy to 3D Printing - Department of Statistics, Purdue University, West Lafayette, IN, February 26, 2014.

[6] Posterior Predictive Checks for Interference in a 3D Printing Experiment - 2014 ASA Conference on Statistical Practice, Tampa, FL, February 22, 2014.

[5] Expeditions in Modern Experimental Design: Partial Aliasing and Interference - Department of Statistics, Stanford University, Stanford, CA, February 11, 2014.

[4] Expeditions in Modern Experimental Design: Partial Aliasing and Interference - Department of Statistics, University of California Berkeley, Berkeley, CA, February 5, 2014.

[3] The Power of Potential Outcomes in Experimental Design: From the Neyman-Fisher Controversy to 3D Printing - Booth School of Business, University of Chicago, Chicago, IL, January 30, 2014.

[2] The Power of Potential Outcomes in Experimental Design: From the Neyman-Fisher Controversy to 3D Printing - Department of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, January 23, 2014.

[1] Inference for Deformation and Interference in 3D Printing - Stuart School of Business, Illinois Institute of Technology, Chicago, IL, October 22, 2013.


Contributed Talks

[7] Bayesian Additive Modeling for Quality Control of 3D Printed Products - 22nd Annual ASA/IMS Spring Research Conference on Statistics in Industry and Technology, Cincinnati, OH, May 21, 2015.

[6] Interference in Deformation Compensation for 3D Printing - 16th Meeting of New Researchers in Statistics and Probability, Cambridge, MA, August 1, 2014.

[5] Inference With Interference And Interference For Inference in a 3D Printing Experiment - INFORMS 2013 Annual Meeting, Minneapolis, MN, October 9, 2013.

[4] Indicator Functions and the Algebra of the Linear-Quadratic Parametrization - INFORMS 2013 Annual Meeting, Minneapolis, MN, October 7, 2013.

[3] Inference with Interference and Interference for Inference: Modeling Potential Outcomes and Interference in a 3D Printing Experiment - 2013 Joint Statistical Meetings, Montreal, Canada, August 5, 2013.

[2] Interesting Insights in Indicators: Indicator Functions and the Algebra of the Linear-Quadratic Parametrization - 20th Annual ASA/IMS Spring Research Conference on Statistics in Industry and Technology, Los Angeles, CA, June 22, 2013.

[1] Inference with Interference and Interference for Inference: Modeling Potential Outcomes and Interference in a 3D Printing Experiment - 30th Quality and Productivity Research Conference, Niskayuna, NY, June 5, 2013.


Purdue Statistics Talks

[5] Sports and Statistics, or, a Bayesian is Better at Betting on Basketball - 2017 Cary Quadrangle Talks, West Lafayette, IN, September 26, 2017.

[4] Challenges and Opportunities in Statistical Quality Control for 3D Printing - Statistics Living-Learning Community Spring 2015 Seminar (STAT 290: Rising Above the Gathering Storm), West Lafayette, IN, October 27, 2015.

[3] Challenges and Opportunities in Statistical Quality Control for 3D Printing - Exploring Statistical Sciences Research Seminar, West Lafayette, IN, September 23, 2015.

[2] Causal Inference under the Potential Outcomes Framework: History, Applications, Challenges - Statistics Living-Learning Community Spring 2015 Seminar (STAT 290: What is the Big Idea?), West Lafayette, IN, March 10, 2015.

[1] Causal Inference under the Potential Outcomes Framework: History, Applications, Challenges - Exploring Statistical Sciences Research Seminar, West Lafayette, IN, October 8, 2014.


Contributed Posters

[7] Screening and Interpreting Inputs in Machine Learning of Additive Manufacturing Systems - 2018 National Science Foundation Cyber-Physical Systems Program Principal Investigators Meeting, Arlington, VA, November 15, 2018.

[6] Automated Geometric Shape Deviation Modeling for Additive Manufacturing Processes via Bayesian Neural Networks - 2017 National Science Foundation Cyber-Physical Systems Program Principal Investigators Meeting, Arlington, VA, November 13, 2017.

[5] An Algebra for Conditional Main Effects - The Design and Analysis of Experiments (DAE 2017), Los Angeles, CA, October 12, 2017.

[4] Learning and Recalibration With Small Sets of Shapes for 3D Printing - 2016 National Science Foundation Cyber-Physical Systems Program Principal Investigators Meeting, Arlington, VA, October 31, 2016.

[3] Interference in Deformation Compensation for 3D Printing - 16th Meeting of New Researchers in Statistics and Probability, Cambridge, MA, August 1, 2014.

[2] Indicator Functions under the Linear-Quadratic Parametrization - 19th Annual ASA/IMS Spring Research Conference on Statistics in Industry and Technology, Cambridge, MA, June 13, 2012.

[1] Who was Right about ANOVA for Latin Squares: Neyman or Fisher? - 2012 Atlantic Causal Inference Conference, Baltimore, MD, May 24, 2012.