1. Robust Parameter Design for Automatically Controlled Processes and Nanostructure Synthesis (Invited talk), Department of Statistics, University of Georgia, Athens, August 2008.

  2. Sequential Minimum Energy Designs for Synthesis of Nanostructures, INFORMS 2007, Seattle.

  3. Sequential Minimum Energy Designs for Synthesis of Nanostructures, Spring Research Conference on Statistics in Industry and Technology 2008, Atlanta.

  4. Statistical methods in Nanostructure Synthesis, Charlton College of Business, University of Massachusetts, Dartmouth, April 2008.

  5. Robust Design, Modeling and Optimization of Measurement Systems, ASA Quality and Productivity Research Conference 2008, Madison.

  6. A Physical-Statistical Model for Density Control of Nanowires, INFORMS 2008, Washington DC.

  7. Robust Design for Dynamic and Measurement Systems, Department of Mathematics and Statistics, Boston University, October 2008.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Sampling from computationally expensive probability distributions, Workshop on Design and Analysis of Experiments in Modern-day Science and Technology, Harvard University, April 2011.

  13. Causal Inference from 2k factorial designs (invited talk), DEMA 2011 organized by the Isaac Newton Institute of Mathematical Sciences, Cambridge, UK, August 2011.

  14. Causal Inference from 2k factorial designs, Harvard University Statistics Colloquium, August 2011.

  15. Causal Inference from 2k factorial designs, Boston University, October 2011.

  16. A Physical-Statistical Model for Density Control of Nanowires (IIE Transactions invited session), INFORMS 2011, Charlotte.

  17. Causal Inference from 2k factorial designs, University of Connecticut, Storrs, January 2012.

  18. Statistical methods in the design and analysis of experiments related to synthesis of nanostructures, Northern Illinois University, Dekalb, Chicago, April 2012.

  19. A D-optimal design for estimation of parameters of an exponential-linear growth of nanostructures, Northern Illinois University, Dekalb, Chicago, April 2012.

  20. “DoIt and Do it well”, Spring Research Conference on Statistics in Industry and Technology, Harvard University, June 2012.

  21. “Observational studies with a factorial structure,” International Chinese Statistical Association Symposium, Boston, June 2012.

  22. “A potential outcomes model for scale up”, INFORMS 2012, Phoenix, October 2012.

  23. Causal Inference from 2^k factorial designs, Rutgers University, New Brunswick, NJ, December 2012.

  24. Causal Inference from 2^k factorial designs, Indian Statistical Institute, Kolkata, January 2013.

  25. A D-optimal design for estimation of parameters of an exponential-linear growth of nanostructures, Indian Statistical Institute, Kolkata, January 2013.

  26. 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.

  27. 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.

  28. A Bayesian Framework for Assessment of Prediction Uncertainty in Scale-up, INFORMS Annual Conference, Minneapolis, October 2013.

  29. The Potential of Potential Outcomes in Experimental Design, Department of Statistics, Virginia Tech, April 2014.

  30. 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.

  31. 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.

  32. 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.

  33. Some potentially useful Bayesian ideas for causal inference from randomized experiments, Department of Statistics, North Carolina State University, September 2015.

  34. Strategies for designing and analyzing complex experiments to achieve balance with respect to several covariates, Illinois Institute of Technology, Chicago, November 2015.

  35. 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.

  36. Design and analysis of experiments for new-generation scientific studies: some challenges and potential solutions, Department of Mathematics and Statistics, Boston University, January 2016.

  37. Design and analysis of experiments for new-generation scientific studies: some challenges and potential solutions, Department of Statistics, Temple University, February 2016.

  38. Design and analysis of experiments for new-generation scientific studies: some challenges and potential solutions, Department of Statistics, North Carolina State University, March 2016.

  39. Design and analysis of experiments for new-generation scientific studies: some challenges and potential solutions, Department of Statistics, Rutgers University, April 2016.

  40. 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.

  41. Sequential learning of deformation models in additive manufacturing through calibration of simulation models. Joint Statistical Meetings 2017, Baltimore.

  42. Sequential learning of deformation models in additive manufacturing. Department of Industrial and Systems Engineering, Rutgers University, September 2017.

  43. Strategies for designing and analyzing complex experiments with multiple interventions and sequentially exploring complex response surfaces. MIT Lincoln Labs, Lexington, MA, October 2017.

  44. Statistics: A Career in the Academia. On “Career Day” sponsored by the NJ and Princeton-Trenton Chapters of the ASA, October 2017.

  45. Sequential learning of deformation models in additive manufacturing. INFORMS Annual Conference, Houston, TX, October 2017.

  46. Handling complex experiments with multiple interventions: methods and examples. Institute of Quantitative Biomedicine, Rutgers University. November 2017.

  47. 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.

  48. Discussant, Invited Session on Experimental Design Thinking for Big Data, Joint Statistical Meeting, Vancouver, Canada, August 2018.

  49. Randomization Based Inference from Unbalanced Split Plot Designs. AISC–2018 International Conference on Advances in Interdisciplinary Statistics and Combinatorics. Greensboro, NC, October 2018.

  50. An Adaptive Data Augmentation Strategy for Fitting Gaussian Process Models with Application to 3D Printing. INFORMS Annual Conference, Phoenix, AZ, November 2018.

  51. Fisher’s Randomization Test: A Confidence Distribution Perspective and Applications to Massive Experiments. WuFest, Atlanta, May 2019.

  52. Design, analysis and optimization of response surfaces in the presence of internal noise. IMS/ASA Spring Research Conference, Blacksburg, VA, May 2019.