ASC AND OZCOTS 2023

The ASC2023 workshops are now open for registration:

Sunday, 10 December 2023

Essential Skills for Statistical Communication

Presented by Professor Sir David Spiegelhalter, University of Cambridge and Dr Linden Ashcroft, University of Melbourne:

Communication is an essential skill for statisticians who commonly need to explain complex concepts in a digestible manner to people from different disciplines. Unfortunately, few statisticians receive formal training in this area. The COVID-19 pandemic helped highlight just how important statistics is and raised the importance of clear communication. This workshop, delivered by leading figures in communication and statistical communication, will help attendees gain confidence in communicating their work and provide attendees with tips for good statistical communication.

The workshop will include a panel discussion — presenters Prof David Spiegelhalter and Dr Linden Ashcroft will be joined by applied mathematician and science communicator Dr Sophie Calabretto, and ABC journalist and ‘lapsed mathematician’, Casey Briggs, to share their experiences.

 Click here for more information and to register.

Sunday, 10 December 2023

Statistical Consultancy – The Essentials for Getting Started and Ongoing Success

Presented by Professor Julie Simpson, University of Melbourne, A/Professor Emily Karahalios, University of Melbourne, A/Professor Karen Lamb, University of Melbourne and A/Professor Sue Finch, University of Melbourne.

This workshop is designed to provide participants with an understanding and tips of some of the key considerations involved in setting up and running a statistical consultancy from within a university environment:  covering a range of topics including funding and operational models, reaching and securing clients, building and funding a team, how to run consultancy projects from start to finish, managing projects and communication skills.  

Click here for more information and to register.

Sunday, 10 December 2023

Deep Statistics for More Rigorous and Efficient Data Science

Presented by Professor  Xiao-Li Meng, Harvard University

This course introduces a trinity of deep statistics of, for and by multi-source, multi-phase, and multi-resolution statistical learning, and invites research participations on their implications and implementations in the context of AI and Earth Observations (EO) for sustainable development (e.g., global poverty and health). Theoretically, the course contemplates many trade-offs for ‘data science for science’: data quality vs. quantity, data privacy vs. utility, statistical vs computational efficiencies, inferential robustness vs relevance, etc.

 Click here for more information and to register.