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Robust Parameter Design for Automatically Controlled Processes and Nanostructure Synthesis (Invited talk), Department of Statistics, University of Georgia, Athens, August 2008.
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Sequential Minimum Energy Designs for Synthesis of Nanostructures, INFORMS 2007, Seattle.
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Sequential Minimum Energy Designs for Synthesis of Nanostructures, Spring Research Conference on Statistics in Industry and Technology 2008, Atlanta.
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Statistical methods in Nanostructure Synthesis, Charlton College of Business, University of Massachusetts, Dartmouth, April 2008.
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Robust Design, Modeling and Optimization of Measurement Systems, ASA Quality and Productivity Research Conference 2008, Madison.
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A Physical-Statistical Model for Density Control of Nanowires, INFORMS 2008, Washington DC.
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Robust Design for Dynamic and Measurement Systems, Department of Mathematics and Statistics, Boston University, October 2008.
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Design and Analysis of Experiments to Improve Yield and Quality of Nanostructure Synthesis, Department of Industrial and Systems Engineering, University of South Florida, Tampa, May 2009.
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Design and analysis of experiments to improve yield and quality of nanostructure synthesis. Workshop on “Statistical Methods for Nanoresearch” organized by Georgia Institute of Technology, Atlanta, December 2009.
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Sequential Minimum Energy Designs, International Conference on Frontiers of Interface between Statistics and Sciences (in the honor of Prof. C R Rao on the occasion of his 90th Birthday), Hyderabad, India, January 2010.
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Causal Inference from 2k factorial designs: an initial exploration of the importance of additivity, Workshop on Design and Analysis of Experiments in Modern-day Science and Technology, Harvard University, April 2011.
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Sampling from computationally expensive probability distributions, Workshop on Design and Analysis of Experiments in Modern-day Science and Technology, Harvard University, April 2011.
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Causal Inference from 2k factorial designs (invited talk), DEMA 2011 organized by the Isaac Newton Institute of Mathematical Sciences, Cambridge, UK, August 2011.
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Causal Inference from 2k factorial designs, Harvard University Statistics Colloquium, August 2011.
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Causal Inference from 2k factorial designs, Boston University, October 2011.
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A Physical-Statistical Model for Density Control of Nanowires (IIE Transactions invited session), INFORMS 2011, Charlotte.
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Causal Inference from 2k factorial designs, University of Connecticut, Storrs, January 2012.
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Statistical methods in the design and analysis of experiments related to synthesis of nanostructures, Northern Illinois University, Dekalb, Chicago, April 2012.
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A D-optimal design for estimation of parameters of an exponential-linear growth of nanostructures, Northern Illinois University, Dekalb, Chicago, April 2012.
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“DoIt and Do it well”, Spring Research Conference on Statistics in Industry and Technology, Harvard University, June 2012.
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“Observational studies with a factorial structure,” International Chinese Statistical Association Symposium, Boston, June 2012.
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“A potential outcomes model for scale up”, INFORMS 2012, Phoenix, October 2012.
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Causal Inference from 2^k factorial designs, Rutgers University, New Brunswick, NJ, December 2012.
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Causal Inference from 2^k factorial designs, Indian Statistical Institute, Kolkata, January 2013.
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A D-optimal design for estimation of parameters of an exponential-linear growth of nanostructures, Indian Statistical Institute, Kolkata, January 2013.
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A D-optimal design for estimation of parameters of an exponential-linear growth of nanostructures, Quality and Productivity Research Conference, GE Global Research Center, June 2013.
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A D-optimal design for estimation of parameters of an exponential-linear growth of nanostructures, Spring Research Conference on Statistics in Science and Technology, Los Angeles, June 2013.
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A Bayesian Framework for Assessment of Prediction Uncertainty in Scale-up, INFORMS Annual Conference, Minneapolis, October 2013.
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The Potential of Potential Outcomes in Experimental Design, Department of Statistics, Virginia Tech, April 2014.
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Exploiting the potential advantages of potential outcomes in the analysis of new-generation scientific experiments, School of Mathematics and Statistics, Arizona State University, November 2014.
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Strategies for Experimenting and Building Predictive Models to Compensate for Geometric Shape Error in 3D Printed Products, Department of Mechanical Engineering, Politecnico Di Milano, Milan, Italy, January 2015.
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A potential outcomes-based perspective of the analysis of complex multi-factor experiments with randomization restrictions, New England Statistics Symposium, University of Connecticut, Storrs, April 2015.
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Some potentially useful Bayesian ideas for causal inference from randomized experiments, Department of Statistics, North Carolina State University, September 2015.
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Strategies for designing and analyzing complex experiments to achieve balance with respect to several covariates, Illinois Institute of Technology, Chicago, November 2015.
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Strategies for designing and analyzing complex experiments to achieve balance with respect to several covariates, RC Bose Plenary Session, Calcutta University Triennial Symposium, December 2015.
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Design and analysis of experiments for new-generation scientific studies: some challenges and potential solutions, Department of Mathematics and Statistics, Boston University, January 2016.
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Design and analysis of experiments for new-generation scientific studies: some challenges and potential solutions, Department of Statistics, Temple University, February 2016.
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Design and analysis of experiments for new-generation scientific studies: some challenges and potential solutions, Department of Statistics, North Carolina State University, March 2016.
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Design and analysis of experiments for new-generation scientific studies: some challenges and potential solutions, Department of Statistics, Rutgers University, April 2016.
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Design and analysis of experiments for new-generation scientific studies: some challenges and potential solutions, Department of Mathematics and Statistics, University of Maryland Baltimore County, April 2016.
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Sequential learning of deformation models in additive manufacturing through calibration of simulation models. Joint Statistical Meetings 2017, Baltimore.
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Sequential learning of deformation models in additive manufacturing. Department of Industrial and Systems Engineering, Rutgers University, September 2017.
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Strategies for designing and analyzing complex experiments with multiple interventions and sequentially exploring complex response surfaces. MIT Lincoln Labs, Lexington, MA, October 2017.
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Statistics: A Career in the Academia. On “Career Day” sponsored by the NJ and Princeton-Trenton Chapters of the ASA, October 2017.
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Sequential learning of deformation models in additive manufacturing. INFORMS Annual Conference, Houston, TX, October 2017.
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Handling complex experiments with multiple interventions: methods and examples. Institute of Quantitative Biomedicine, Rutgers University. November 2017.
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Randomization based perspectives of randomized block designs and a new test statistic for the Fisher randomization test, Workshop on Design of Experiments, April 30 - May 4 2018, CIRM, Marseilles, France.
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Discussant, Invited Session on Experimental Design Thinking for Big Data, Joint Statistical Meeting, Vancouver, Canada, August 2018.
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Randomization Based Inference from Unbalanced Split Plot Designs. AISC–2018 International Conference on Advances in Interdisciplinary Statistics and Combinatorics. Greensboro, NC, October 2018.
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An Adaptive Data Augmentation Strategy for Fitting Gaussian Process Models with Application to 3D Printing. INFORMS Annual Conference, Phoenix, AZ, November 2018.
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Fisher’s Randomization Test: A Confidence Distribution Perspective and Applications to Massive Experiments. WuFest, Atlanta, May 2019.
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Design, analysis and optimization of response surfaces in the presence of internal noise. IMS/ASA Spring Research Conference, Blacksburg, VA, May 2019.