Organizers Annie Qu and Regina Liu write:

We are delighted to report that the 2022 ICSDS (International Conference on Statistics and Data Science; December 13–16, 2022, in Florence, Italy) has received a tremendous response, including many outstanding invited speakers coming from different countries and continents, covering a wide range of subjects in statistics and data science, in theory, methodology and applications. Due to the unprecedented large number of participants and the space constraint of the venue, all speakers (invited or contributed) and poster presenters are now required to register by November 15, in order to be on the program. 

The meeting website is https://sites.google.com/view/icsds2022. 

The ICSDS award committee is pleased to announce that the following 10 PhD students are awarded 2022 ICSDS Student Travel Awards. The selection is made from 53 applications based on the quality of their manuscripts in statistics and data science. Please join us in congratulating these award recipients:

Samantha Dean, Yale University, USA: Effective treatment allocation strategies conditional on individuals’ characteristics under partial interference in randomized experiments

Bertille Follain, Ecole Normale Supérieure/INRIA Paris, France: High-dimensional changepoint estimation with heterogeneous missingness

Arkaprabha Ganguli, Michigan State University, USA: Feature selection integrated deep learning for ultrahigh dimensional and highly correlated feature space

Shimeng Huang, University of Copenhagen, Denmark: Supervised Learning and Model Analysis with Compositional Data

Takuya Koriyama, Rutgers University, USA: Asymptotic Analysis of Parameter Estimation for Ewens–Pitman Partition

Hanâ Lbath, University Grenoble Alpes, INRIA, France: Clustering-Based Inter-group Correlation Estimation

Marcos Matabuena, University of Santiago de Compostela, Spain: Kernel Biclustering algorithm in Hilbert Spaces

Lorenzo Pacchiardi, University of Oxford, UK: Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization

Javier Aguilar Romero, SimTech Stuttgart University, Germany: Intuitive Joint Priors for Bayesian Linear Multilevel Models: The R2-D2-M2 prior

Ye Tian, Columbia University, USA: Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models.