• Uniformly Valid Post-Regularization Confidence Regions for Many Functional Parameters in {Z}-Estimation Framework, November 2016, USC Marshall Schools of Business, Statistics Seminar.
• Uniformly Valid Post-Regularization Confidence Regions for Many Functional Parameters in {Z}-Estimation Framework, November 2016, Wharton School, Statistics Colloquium.
• Budget-constrained Procurement, INFORMS 2016, Nashville.
• Causal Inference with High-Dimensional Controls and Parameters of Interest, Digital Economics Conference, Microsoft New York, September 2016.
• Measuring Context Effects in Z-estimation Framework, World Congress of Probability and Statistics, Toronto, July 2016.
• Budget-constrained Procurement, 16th SAET Conference on Current Trends in Economics, July 2016.
• Uniformly Valid Post-Regularization Confidence Regions for Many Functional Parameters in Z-Estimation Framework, PUC-rio, Department of Economics, July 2016.
• Uniformly Valid Post-Regularization Confidence Regions for Many Functional Parameters in Z-Estimation Framework, London Business School, May 2016.
• Uniformly Valid Post-Regularization Confidence Regions for Many Functional Parameters in Z-Estimation Framework, Econometrics Workshop at University of Warwick, May 2016.
• Uniformly Valid Post-Regularization Confidence Regions for Many Functional Parameters in Z-Estimation Framework, Columbia University, DRO-IEOR Seminar, April 2016.
• Valid Post-Selection Confidence Regions for Moment Condition Models with Functional Response Data, Triangle Econometric Meeting, December 2015.
• Structures of Optimal Contracts in Dynamic Mechanism Design with One Agent, INFORMS 2015.
• Approximate Group Context Tree, Princeton University, Operations Research and Financial Engineering Department, April 2015.
• Valid Post-Selection Confidence Regions for Moment Condition Models with Functional Response Data, University of Michigan, Ross School of Management, April 2015.
• Valid Post-Selection Confidence Regions for Moment Condition Models with Functional Response Data, North Carolina state University, Department of Statistics, February 2015.
• Program Evaluation in High-Dimensions, Wharton Department of Statistics Colloquium, December 2014.
• Uniform Post Selection Inference for Z-estimation problems, MIT Stochastics and Statistics Seminar, May 2014.
• Uniform Inference for Quantile Regression, Logistic Regression, and other Z-estimators, University of Pennsylvania, Department of Economics, April 2014.
• Uniform Inference for Quantile Regression, Logistic Regression, and other Z-estimators, Penn State University, Department of Economics, April 2014.
• Uniform Inference for Quantile Regression, Logistic Regression, and other Z-estimators, Boston University, Department of Economics, March 2014.
• Honest Confidence Regions for a Regression Parameter in Logistic Regression with a Large Number of Controls, Mathematical Statistics with Applications in Mind, CIRM, December, 2013.
• Uniform Inference After Model Selection, Columbia University, Department of Biostatistics, November 2013.
• Uniform Inference After Model Selection, MIT ORC, October 2013.
• Uniform Inference After Model Selection, IMPA, June 2013.
• Robust Post Model Selection Inference for High-Dimensional Sparse Quantile Regression Models, CEME, Cornell, May 2013.
• Uniform Inference After Model Selection, Stanford University, GSB, May 2013.
• Uniform Inference After Model Selection, Georgetown University, Department of Economics, April 2013.
• Inference on Treatment Effects After Selection Amongst High-Dimensional Controls, Midwest Economics Association Meeting, March 2013.
• Inference on Treatment Effects After Selection Amongst High-Dimensional Controls, University of Rochester, December 2012.
• Robust Post Selection Inference for High-Dimensional Sparse Quantile Regression, Frontiers in Quantile Regression, Mathematisches Forschungsinstitut Oberwolfach, November 2012.
• Robust Post Selection Inference for High-Dimensional Sparse Quantile Regression, invited (joint) seminar Statistics and Econometrics, University of Michigan, November 2012.
• High dimensional Sparse Econometric Models, An Introduction, ICE 2012, Booth School of Business, University of Chicago.
• Robust Inference After Model Selection, Summer Seminar Series, Fuqua School of Business, July 2012.
• Approximate Group Context Tree: applications to dynamic programming and dynamic choice models, invited talk at CIREQ, High-Dimensional Econometrics, May 2012.
• Approximate Group Context Tree: applications to dynamic programming and dynamic choice models, invited seminar at Duke University, Department of Statistics, February 2012.
• Approximate Group Context Tree: applications to dynamic programming and dynamic choice models, invited seminar at Kellogg School of Business, January 2012.
• Approximate Group Context Tree: applications to dynamic programming and dynamic choice models, invited seminar at USC, Business School, December 2011.
• On model selection and high-dimensional sparse models in Econometrics, invited seminar at North Carolina State University, Department of Economics, November 2011.
• Approximate Group Context Tree: applications to dynamic programming and dynamic choice models, invited talk INFORMS 2011.
• Pivotal Estimation of Nonparametric Functions via Conic Programming, invited talk INFORMS 2011.
• High-Dimensional Sparse Econometric Models, an Introduction Invited Talk, Initiative for Computational Economics, 2011, University of Chicago Booth School of Business.
• Model Selection and High-Dimensional Sparse Econometric Models, Invited Talk at Northwestern University, Department of Economics, 2011
• Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain, 2010 Triangle Econometrics Conference, NISS, December 2010.
• On Multivariate Quantiles under Partial Ordering, MIT/Harvard Econometric Workshop, November, 2010.
• On Multivariate Quantiles under Partial Ordering, Decision Sciences Seminar at Fuqua School of Business, November, 2010.
• High-Dimensional Sparse Econometric Models, Summer Series Seminar at Fuqua School of Business, June, 2010.
• Penalized Quantile Regression in Sparse High-dimensional Models, Invited Talk at IMPA, June, 2010.
• Multi-dimensional Mechanism Design: Finite Dimensional Approximations and Efficient Computation, invited talk at Booth Chicago School of Business, 2010.
• Multi-dimensional Mechanism Design: Finite Dimensional Approximations and Efficient Computation, STOR seminar at UNC, 2009.
• Multi-dimensional Mechanism Design: Finite Dimensional Approximations and Efficient Computation, ISMP at Chicago, 2009.
• On the Behrens-Fisher problem: a globally convergent algorithm and a finite-sample study of the Wald, LR, and LM tests, JSM, Washington, August, 2009.
• Penalized Quantile Regression in Sparse High-dimensional Models, invited talk at London Business School, June, 2009.
• Penalized Quantile Regression in Sparse High-dimensional Models, CEMMAP at UCL, June, 2009.
• Penalized Quantile Regression in Sparse High-dimensional Models, invited talk at PUC-rio, January, 2009.
• Penalized Quantile Regression in Sparse High-dimensional Models, 2008 Triangle Econometrics Conference, NISS, December, 2008.
• Penalized Quantile Regression in Sparse High-dimensional Models, Latin American Meeting of the Econometric Society, Rio de Janeiro, November, 2008.
• On the Behrens-Fisher problem: a globally convergent algorithm and a finite-sample study of the Wald, LR, and LM tests, SIAM Meeting on Optimization, Boston, May, 2008.
• Conditional Quantile Processes under Increasing Dimension, NAWM Econometric Society, New Orleans, January, 2008.
• An Integer Stochastic Programming Problem with Linear Fractional Objective Function. INFORMS 2007, Seattle, November.
• Conditional Quantile Processes under Increasing Dimension, INFORMS 2007, Seattle, November, 2007.
• Norm-induced densities and testing the boundedness of a convex set. International Conference on Continuous Optimization (ICCOPT), Hamilton, Canada, August, 2007.
  
• Efficiency of a Re-scaled Perceptron Algorithm for Conic Systems, International Conference on Continuous Optimization (ICCOPT), Hamilton, Canada, August, 2007.
  
• On the Computational Complexity of MCMC-Based Estimators under Large Samples, 2007 North American Summer Meeting of the Econometric Society, Durham NC, June 2007.
  
• Efficiency of a Re-scaled Perceptron Algorithm for Conic Systems, COLT, San Diego, June, 2007.
  
• Projective Pre-conditioners for Improving the Practical Performance of IPMs for Conic Programming, INFORMS 2006, Pittsburg PA, November 2006.
  
• On the Computational Complexity of MCMC-Based Estimators under Large Samples, George Nicholson Student Paper Competition, INFORMS 2006, Pittsburg PA, November 2006.
  
• Effiency of a Re-scaled Perceptron Algorithm for Conic Systems, IBM T. J. Watson Research Center, AP/IP Seminar Series, September, 2006.
  
• Testing the Boundedness of a Convex Set, 19th International Symposium on Mathematical Programming, Rio de Janeiro, Brazil, August 2006.
  
• Two Applications of High-Dimensional Random Sampling for Convex Problems. McMaster University, Canada, February 2006.
  
• Computational Complexity of MCMC-Based Estimators under the Central Limit Theorem Framework. New York University, Stern School of Business, Operations Management, NY, February 2006.
  
• Two Applications of High-Dimensional Random Sampling. Duke University, Fuqua School of Business, Decision Science, NC, February 2006.
  
• Optimizing Product Line Design: Efficient Methods and Comparisons. University of British Columbia, Sauder School of Business, Operations and Logistics, Canada, February 2006.
  
• Computational Complexity of MCMC-Based Estimators under the Central Limit Theorem Framework. Columbia University, Graduate School of Business, Decision, Risk and Operations, NY, February 2006.
  
• Optimizing Product Line Design: Efficient Methods and Comparisons. Columbia University, Graduate School of Business Decision, Risk and Operations, NY, February 2006.
  
• Projective Pre-conditioners for Solving Homogeneous Linear Conic Systems. University of Waterloo, Department of Combinatorics and Optimization, Canada, January 2006.
  
• Two (or Three) Applications of High-Dimensional Random Sampling for Convex Problems. IBM T. J. Watson Research Center, NY, December 2005.
  
• Computational Complexity of MCMC-based Estimators under the Central Limit Theorem, New York University, New York, February 2006.
  
• Two Applications of High Dimension Random Sampling, Duke University, Durham, North Carolina, February 2006.
  
• Optimizing Product Line Designs: Efficient Methods and Comparisons, University of British Columbia, Vancouver, Canada, February 2006.
  
• Computational Complexity of MCMC-based Estimators under the Central Limit Theorem, Columbia University, New York, February 2006.
  
• Optimizing Product Line Designs: Efficient Methods and Comparisons, Columbia University, New York, February 2006.
  
• Projective Pre-conditioners for Improving the Behavior of Homogeneous Conic Systems, Waterloo University, Canada, January 2006.
  
• Projective Pre-conditioners for Improving the Behavior of Homogeneous Conic Systems, INFORMS 2005, San Francisco, November 2005.
  
• Projective Pre-conditioners for Improving the Behavior of Homogeneous Conic Systems, SIAM Conference on Optimization, Stockholm, Sweden, May 2005.
  
• Projective Pre-conditioners for Improving the Behavior of Homogeneous Conic Systems, Invited Speaker, IMPA, Rio de Janeiro, Brazil, February 2005.
  
• Projective Pre-conditioners for Improving the Behavior of Homogeneous Conic Systems, Invited Speaker, IBM T. J. Watson Research Center, NY, January 2005.
  
• Symmetry Points of Convex Sets: Basic Properties and Complexity, V Brazilian Workshop on Continuous Optimization 2004, Brazil.
  
• Uncertainty Aversion Applied to the Power Systems, IV Brazilian Workshop on Continuous Optimization 2002, Brazil.
  
• Dynamic bundle methods for combinatorial optimization, IV Brazilian Workshop on Continuous Optimization 2002, Brazil.
  
• Dynamic Bundle Methods for Combinatorial Optimization, SIAM Conference on Optimization 2002, Toronto, Canada.
  
• Relax and Cut, III Brazilian Workshop on Continuous Optimization, 2001.
  
• Relax and Cut Algorithm for the Traveling Salesman Problem, 17th International Symposium on Mathematical Programming, Atlanta, USA, 2000.
  

My CV is here