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Invited Talks and Short Courses

[89] Bayesian Data Analysis and Monte Carlo Methods - St. Mary's College of California School of Economics and Business Administration, Moraga, CA, November 17, 2022.

[88] A Closed-Loop Machine Learning and Compensation Framework for Geometric Accuracy Control of 3D Printed Products - FABTECH 2022, November 9, 2022.

[87] A Closed-Loop Machine Learning and Compensation Framework for Geometric Accuracy Control of 3D Printed Products - ASME 2022 International Mechanical Engineering Congress and Exposition (IMECE 2022), November 2, 2022.

[86] Closed-loop Machine Learning And Compensation For Geometric Accuracy Control Of Additively Manufactured Products - 2022 INFORMS Annual Meeting, October 16, 2022.

[85] A Bayesian Analysis of Two-Stage Randomized Experiments in the Presence of Interference, Treatment Nonadherence, and Missing Outcomes - 2022 INFORMS Workshop on Quality, Statistics, and Reliability. October 15, 2022.

[84] A Closed-Loop Machine Learning and Compensation Framework for Geometric Accuracy Control of 3D Printed Products - 2022 Fall Technical Conference, October 13, 2022.

[83] A Bayesian Analysis of Two-Stage Randomized Experiments in the Presence of Interference, Treatment Nonadherence, and Missing Outcomes - 2022 International Conference on Advances in Interdisciplinary Statistics and Combinatorics, October 7, 2022.

[82] Short Course: Estimating Treatment Effect in a Principal Stratum: Applications of Causal Inference to the Tripartite Estimand Approach (TEA) and Early Biomarker Response - 2022 ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop, September 20, 2022.

[81] 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 3, 2022.

[80] A Closed-Loop Machine Learning and Compensation Framework for Accuracy Control in 3D Printing - RAPID + TCT 2022 Conference, May 17, 2022.

[79] A Closed-Loop Machine Learning and Compensation Framework for Geometric Accuracy Control of 3D Printed Products - 2022 Online POMS Annual Conference, April 2022.

[78] A Closed-Loop Machine Learning and Compensation Framework for Geometric Accuracy Control of 3D Printed Products - Northwestern University Center for Optimization and Statistical Learning Seminar Series, April 7, 2022.

[77] A Closed-Loop Machine Learning and Compensation Framework for Geometric Accuracy Control of 3D Printed Products - Maynooth University Hamilton Institute Seminar Series, March 23, 2022.

[76] Causal Inference for Closed-Loop Quality Control in 3D Printing - University of California, Berkeley Causal Inference Group, March 16, 2022.

[75] Pouring New Wines From Old Bottles: Jeff Wu's Contributions to the Design and Analysis of Fractional Factorials - 2020 Monie A. Ferst Award Symposium, November 11, 2021.

[74] Distortion Modeling and Compensation Across Materials and Processes in Laser-Based Additive Manufacturing Systems via Bayesian Neural Networks - 2021 I-Dream4D Consortium Seminar Series, November 9, 2021.

[73] Distributed Design for Causal Inferences on Big Observational Data - CUNY Baruch College Zicklin School of Business Information Systems and Statistics Research Seminar Series, October 28, 2021.

[72] Distortion Modeling and Compensation Across Materials and Processes in Laser-Based Additive Manufacturing Systems via Bayesian Neural Networks - 2021 INFORMS Workshop on Quality, Statistics, Reliability, October 23, 2021.

[71] Distributed Design for Causal Inferences on Big Observational Data - University of Illinois at Urbana-Champaign Statistics Seminar, October 14, 2021.

[70] Bandits With Priors - 2021 Virtual International Conference on Advances in Interdisciplinary Statistics and Combinatorics, October 9, 2021.

[69] ICH E9(R1) Addendum in Practice: An Industry-Regulatory Estimand Role Play - 2021 Virtual ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop, September 24, 2021.

[68] New Perspectives on Randomization Tests for Co-Primary and Secondary Endpoints in Phase III Clinical Trials - 2021 Virtual ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop, September 23, 2021.

[67] The Epiphanies of Sir R.A. Fisher and Jerzy Neyman for Causal Inference - Purdue University Fall 2021 Distiguished Theme Seminar Series, August 27, 2021.

[66] Distortion Modeling and Compensation Across Materials and Processes in Laser-Based Additive Manufacturing Systems via Bayesian Neural Networks - 37th Annual Quality and Productivity Research Conference, July 27, 2021.

[65] AMapi: An Application Programming Interface for the Control of Additive Manufacturing Systems - Institute of Industrial & Systems Engineers (IISE) Virtual Annual Conference & Expo 2021, May 24, 2021.

[64] Collaborative Design for Improved Causal Machine Learning in Big Observational Data - 2021 International Indian Statistical Association (IISA) Conference, May 20, 2021.

[63] The Designed Bootstrap for Causal Inference in Big Observational Data - University of North Carolina at Greensboro Statistics Seminar, Greensboro, NC, February 12, 2021.

[62] AMapi: An Application Programming Interface for the Control of Additive Manufacturing Systems - 2020 INFORMS Virtual Annual Meeting, November 12, 2020.

[61] Collaborative Design for Improved Causal Machine Learning on Big Observational Data - Purdue University Krannert School of Management Econometrics Seminar Series, West Lafayette, IN, October 19, 2020.

[60] Predictive Comparisons for Screening and Interpreting Inputs in Machine Learning - University of Waterloo Department of Statistics and Actuarial Science, Waterloo, Ontario, Canada, October 8, 2020.

[59] Distortion Model Transfer Between Materials in Laser Based Additive Manufacturing Systems - Rutgers University Department of Industrial and Systems Engineering, Piscataway, NJ, October 6, 2020.

[58] AMapi: An Application Programming Interface for the Control of Additive Manufacturing Systems - Motivate the Market 2019 Forum, Dayton, OH, November 7, 2019.

[57] Designed Experiments on Additive Manufacturing Systems for Inference on Interference in Shape Deviations - 2019 INFORMS Annual Meeting, Seattle, WA, October 22, 2019.

[56] Distortion Model Transfer Between Materials in Laser Based Additive Manufacturing Systems via Bayesian Neural Networks - 2019 INFORMS Annual Meeting, Seattle, WA, October 22, 2019.

[55] Collaborative Design for Improved Causal Machine Learning on Big Observational Data - 2019 Design and Analysis of Experiments (DAE 2019) Conference, Konxville, TN, October 18, 2019.

[54] Predictive Comparisons for Screening and Interpreting Inputs in Machine Learning - 2019 Fall Technical Conference, Gaithersburg, MD, September 27, 2019.

[53] Machine Learning for Automated Predictive Modeling of Shape Deviations and Distortions in Additive Manufacturing Systems - 2019 Purdue University Additive Manufacturing Workshop, West Lafayette, IN, September 26, 2019.

[52] Modeling In-Plane Deviations of Shapes to Come Based on Prior Deviation Features in Additive Manufacturing - 2nd International Conference on Additive Manufacturing, Modeling Systems and 3D Prototyping, 10th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences, Washington, D.C., July 27, 2019.

[51] Model Transfer Across Additive Manufacturing Processes via Mean Effect Equivalence - Air Force Research Laboratory Seminar Series, Wright-Patterson Air Force Base, OH, July 9, 2019.

[50] Modeling In-Plane Deviations of Shapes to Come Based on Prior Deviation Features in Additive Manufacturing - Fifth International Conference on the Interface Between Statistics and Engineering (ICISE 2019), Seoul, South Korea, June 27, 2019.

[49] Geometric Shape Deviation Modeling Across Different Processes and Shapes in Additive Manufacturing Systems - 36th Annual Quality and Productivity Research Conference, Washington, D.C., June 13, 2019.

[48] Predictive Modeling Across Different Processes and Shapes in Additive Manufacturing - Institute of Industrial & Systems Engineers (IISE) Annual Conference & Expo 2019, Orlando, FL, May 19, 2019.

[47] Automated Machine Learning for Shape Deviation Modeling in Additive Manufacturing Systems - Third Annual Purdue Technology Showcase, West Lafayette, IN, May 16, 2019.

[46] Geometric Shape Deviation Modeling for Cyber-Physical Additive Manufacturing Systems - Purdue University Nanomanufacturing Symposium, West Lafayette, IN, April 30, 2019.

[45] Potential Outcome Model Transfer via Mean Effect Equivalence of Lurking Variables - University of Notre Dame Department of Applied and Computational Mathematics and Statistics Seminar, March 19, 2019.

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

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

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

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

[40] 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.

[39] 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.

[38] 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.

[37] 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.

[36] 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.

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

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

[33] 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.

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

[31] 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.

[30] 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.

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

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

[27] 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.

[26] 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.

[25] 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.

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

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

[22] 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.

[21] 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.

[20] 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.

[19] 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.

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

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

[16] 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.

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

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

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

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

[11] 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.

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

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

[8] 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.

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

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

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

[4] 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.

[3] 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.

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

[1] Indicator Functions and the Algebra of the Linear-Quadratic Parametrization - 2013 INFORMS Annual Meeting QSR Best Student Paper Competition Session, Minneapolis, MN, October 7, 2013.

Contributed Talks

Purdue Statistics Talks

[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