• September 2018, Workshop on Structural Inference in High-Dimensional Models. High dimensional central limit theorem for one-dependent data. Poster
• July 2018, 12th International Vilnius Conference on Probability Theory and Mathematical Statistics and 2018 IMS Annual Meeting on Probability and Statistics, Vilnius, Lithuania, Rosenthal type inequality for regenerative Markov chains. Talk
• June 2018, IMS Asia Pacific Rim Meeting, Singapore, Robust estimation for piecewise deterministic Markov processes. Talk
• June 2018, Conference of the international Society for Nonparametric Statistics, Salerno, Italy, Learning minimum volume sets from regenerative Markov chains. Talk
• June 2018, Conference Statistical Learning and Data Science, New York, United States , Generalization bounds of ERM algorithm with regenerative Markov chain samples. Poster
• January 2018, Artificial Intelligence and Mathematics, Florida, United States. Generalization bounds for minimum volume set estimation problem for Markovian data. Talk
•August 2017, Joint Statistical Meetings, Baltimore, United States. Concentration inequalities with applications to statistical learning. Talk & Poster
• July 2017, Stochastic Processes and Applications Conference, Moscow, Russia. Exponential inequalities for regenerative Markov chains. Talk
• May 2017, Les probabilités de demain, Paris, France. Sharp Bernstein and Hoeffding type inequalities for regenerative Markov chains. Talk
• May 2017, Journée annuelle de la Chaire Machine Learning for Big Data, Paris, France, Bootstrapping periodically autoregressive models. Talk
• June 2016, International Society for Non-Parametric Statistics Conference , Avignon, France, Best Young Researchers Award, Bootstrap uniform central limit theorems for Harris recurrent Markov chains. Talk
• May 2016, Dependence, Stability and Extremes Workshop, Toronto, Canada. Bootstrapping Harris recurrent Markov chains. Poster
• February 2016, Computational Statistics and Molecular Simulation Workshop , Paris, France, Bootstrapping Harris recurrent Markov chains. Poster