The New England Statistical Society (NESS) is a young statistical society founded by a group of prominent statisticians in the New England region of the United States in 2017. The objectives of NESS are to promote the growth and expansion of statistical science through scholarly activities and to be a leading professional society for supporting and sustaining statistics as a central pillar of data science (https://nestat.org/). Over a short period of five years, NESS has seen tremendous growth, and it now has over nine hundred members, its annual symposiums and educational activities, and its official journal, The New England Journal of Statistics in Data Science (NEJSDS). The aims of NEJSDS are to serve as an interface between statistics and other disciplines in data science, to encourage researchers to exchange innovative ideas, and to promote data science methods to the general scientific community (https://journal.nestat.org/). To achieve these objectives, NEJSDS now has eight subject-specific sections, which cover a broad range of data science fields. 

In this session, we will have one of the editor-in-chiefs, Dr. Min-ge Xie (Editor-in-Chiefs), and two section editors, Dr. Paul S. Albert (Co-Editor, Biomedical Research Section) and Dr. Grace Y. Yi (Editor, Statistical Methodology Section), to present novel developments and recent advances in the areas of analyzing complex data. The topics of these speakers will include methodology research, theoretical developments and applications, as well as main future directions and areas which are interested by the journal. This will be a great opportunity for meeting participants to exchange research developments and ideas and to learn about the topics suitable for publication in NEJSDS. We wish to attract high quality submissions to the journal. 

Tentative topics of the session are: 

1) Dr. Sijian Wang  "Repro sampling method for irregular inference problems and a novel solution to model selection and post selection inferences." 

2) Dr. Paul S. Albert (Senior Investigator, National Cancer Institute, USA) "Bayesian inference of chemical mixtures in risk assessment incorporating the hierarchical principle." 

3) Dr. Grace Y. Yi (Professor, Canada Research Chair in Data Science tier 1, University of Western Ontario, Canada) "Graphical proportional hazards measurements error models."

4) Dr. Eric Kolaczyk (Professor, Boston University), discussant. 

Organiser: Dr Colin O. Wu 

Chair: Prof. Changbao Wu 

Speaker: Dr. Sijian Wang 

Speaker: Dr Debamita Kundu

Speaker: Grace Yi 

Discussant:  Prof. Eric Kolaczyk