ACSPRI Masterclasses in Choice Modelling

When
29 May 2023
Location
UQ, Brisbane

Stephane Hess is presenting two, 2-day masterclasses/workshops in Brisbane at UQ in late May/early June. Stephane holds the position of Professor of Choice Modelling and Director of the Choice Modelling Centre at The University of Leeds (UK). He is the editor of the Journal of Choice Modelling and has interests in behavioural models, travel behaviour, health choices, and decision making. He is widely recognised as a leader in the field of choice modelling.

The first masterclass is on the fundamentals of choice modelling (the Monday and Tuesday of the week beginning the 29th of May). The second masterclass is on applied choice modelling using Apollo (the Thursday and Friday of that same week). Stephane is one of the developers of the Apollo software – an R package for estimating choice models. Please see the links below for further details of the courses.

The Masterclasses are being offered by ACSPRI (the Australian Consortium for Social and Political Research, Inc.) and hosted by The University of Queensland Business School. ACSPRI is a member-based, non-profit consortium of Australian universities, government departments and agencies, and research-oriented non-profit organisations.

ACSPRI offers discounted fees to individuals from member organisations and from non-profit organisations (for example, research organisations, public and private schools), and also to SSA members. Please contact Len Coote if you have questions about the courses or wish to obtain an SSA membership discount (l.coote@business.uq.edu.au).

https://www.acspri.org.au/master-class-may-2023-fundamentals-choice-modelling

https://www.acspri.org.au/master-class-june-2023-applied-choice-modelling-using-apollo

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Canberra Branch Meeting -- Statistical methods for quantifying empathic accuracy

When
30 May 2023
5:45 PM - 7:00 PM (AEST)
Location
Hybrid (in-person and on Zoom)

SSA Canberra invites you to its May branch meeting of 2023, which will feature Linh Nghiem from the University of Sydney present in-person (+streamed online) on Functional Shape Analysis of Empathic Accuracy

Time: Start at 5:45pm and finish by 7:00pm Canberra time.

Venue: Room 4.05 in Marie Reay Teaching Centre, Australian National University (MRTC ANU), or via Zoom. Please see the bottom of this event page for Zoom links.

Dinner: After the talk we will be holding a dinner at 7.15pm at Madam Lu Malaysian Restaurant, 20/42 West Row, Canberra (Madam Lu Malaysian Restaurant | Facebook).

If you are interested in attending the dinner, please let us know by 6pm Monday 29 May by entering your details at SSA Canberra Branch dinner attendance sheet or contacting Warren Muller (warren.muller@csiro.au ; 0407 916 868). Please regard this as a firm commitment, not just an intention. For withdrawals after the deadline, please remove your name from the sheet and phone or text Warren (0407 916 868).

NOTE: We are offering discounts to SSA early career and student members who attend dinner! For this meeting, dinners will be a fixed charge of $10 for student members and $20 for early career members.

Talk details

Speaker: Dr. Linh Nghiem, University of Sydney

Topic: Functional Shape Analysis of Empathic Accuracy

Abstract:

Empathic accuracy (EA) is the ability of one person to accurately understand the thoughts and emotions of others. A widely used computer-based paradigm to measure EA for a subject is to compare the subject’s continuous ratings of a target’s feelings with the ratings given by the target themselves. Nevertheless, current measures of EA are based only on pointwise comparison between the two curves. As a result, these measures are not reliable when misalignments occur in the observed curves, which arise commonly due to the nature of EA studies. In this work, we propose new measures for EA by accounting for the global shape of both the subject’s curve and the target’s curve, which motivate the use of time warping functions in modelling the observed curves. These warping functions are able to accommodate a wide range of misalignments and can be incorporated naturally into many statistical models of functional data with efficient estimation algorithms. We will discuss the new EA measures in social and musical EA studies.

Biography:

Linh Nghiem is a Lecturer in Statistics from the School of Mathematics and Statistics at the University of Sydney. As a methodological and applied statistician, Linh is interested in developing novel statistical methodologies to address complex scientific questions using data. His current methodological interests are measurement error models, dimension reduction, and graphical models. On the applied side, Linh is collaborating with scientists to investigate human perception of music as well as how music can be used to improve social empathy among people in society.

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Topic: SSA Canberra branch meeting

Time: May 30, 2023 05:45 PM Canberra, Melbourne, Sydney

Join Zoom Meeting
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Meeting ID: 832 8282 1064
Password: 198902
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------------------------------

Website links: https://statsoc.org.au/Canberra-Branch-meetings

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SSA Vic & Tas May Event: What's new in the tidyverse? by Professor Mine Çetinkaya-Rundel

When
30 May 2023
6:00 PM (AEST)
Location
WEHI Auditorium, 1G, Royal Parade, Parkville VIC 3052
Registered
109 registrants

Register

SSA Vic & Tas and R-Ladies Melbourne are pleased to hold a joint event on Tue 30th May 6pm to welcome Professor Mine Çetinkaya-Rundel to talk to us about tidyverse. The event will be held in-person as well as online via Zoom. We look forward to seeing you there!

What's new in the tidyverse?

The tidyverse is an opinionated collection of R packages designed for data science. One important feature of the tidyverse is that packages in the tidyverse share an underlying design philosophy, grammar, and data structures. Another important feature is that the tidyverse is a living, breathing, and evolving ecosystem. In this talk I will discuss what is new in the tidyverse, highlighting tips and tricks for what's new in the tidyverse in 2023. Additionally, I will take a broader view of "new" and discuss major updates and new features in the tidyverse that got implemented between the 1st and 2nd editions of R for Data Science.

Biography

Mine Çetinkaya-Rundel is a Professor of the Practice and the Director of Undergraduate Studies at the Department of Statistical Science at Duke University. Mine's work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education as well as pedagogical approaches for enhancing the retention of women and underrepresented minorities in STEM. Mine works on the OpenIntro project, whose mission is to make educational products that are free, transparent, and lower barriers to education. As part of this project, she co-authored four open-source introductory statistics textbooks. She is also the creator and maintainer of datasciencebox.org, a co-author of R for Data Science (2nd Edition), and she teaches the popular Statistics with R MOOC on Coursera. In addition to her academic position, Mine also works as a Developer Educator with Posit, where she focuses primarily on building educational resources for tidyverse and Quarto. Read more about her on her webpage at <https://mine-cr.com>.

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How to Become a Fellow of the Australian Academy of Science

When
31 May 2023
1:00 PM - 2:00 PM (AEST)
Location
Online

Are you interested in learning about the nomination process for Australian Academy of Science (AAS) fellowship and strategies for improving gender balance within the Academy? Join the Australian Mathematical Society (AustMS), Women in Mathematics Special Interest Group (WIMSIG), and the AAS for an online information session on May 31^st 2023 from 1:00 pm - 2:00 pm AEST.

Featuring presentations from accomplished AAS Fellows and a Q&A panel discussion, this event is a valuable opportunity to gain insights into the AAS nomination process and connect with peers in the scientific community.

Program:

· Welcome

· Short talk from Prof Helene Marsh (Fellowship Secretary B with the AAS) on what it means to be a fellow of the Academy

· Short talk from Prof Louise Ryan (Council Member with the AAS and past chair of the Mathematical Sciences Sectional Committee of the AAS) about what a strong nomination looks like

· Q&A panel discussion with AAS Fellows Prof Catherine Greenhill, Prof Nalini Joshi, Prof Cheryl Praeger and Prof Kate Smith-Miles

· Conclusion

Don't miss this chance to learn about the nomination process and connect with some of the amazing AAS Fellows. Register now to secure your spot!

Registration link: https://www.eventbrite.com.au/e/wimsig-information-session-on-aas-fellow-nomination-process-tickets-623407066317

We look forward to seeing you there!

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Expression of Interest in Survey and Data Science Courses in 2023–24

Start
1 Jun 2023
(AEST)
End
31 Oct 2024
(AEDT)
Location
Online-weekly one hour classes-this is 6 courses offered over the next year

Register

Expression of interest in survey and data science courses in 2023–24

Due to the high demand for the Sampling Course in 2022 and strong interest in other courses from the International Program in Survey and Data Science (IPSDS) Masters program the Social Research Centre and Statistical Society of Australia have partnered again to expand IPSDS course offerings in Australia.

The IPSDS is a program of the University of Mannheim and the Joint Program in Survey Methodology at the University of Maryland. It is directed by Prof. Frauke Kreuter, who is professor at the Ludwig Maximilian University of Munich.

These course offerings are motivated back the lack of Australian equivalents.

If you are interested in the Item Nonresponse, Sampling, Big Data/Machine Learning for Surveys and/or Weighting courses please register your interest so that we can determine whether there is sufficient demand.

The courses to be offered in 2023–24 are:

Course	Dates	Instructor(s)	Prerequisites
Item Nonresponse and Imputation	Jun–Jul 2023 (4 weeks)	Prof Jörg Drechsler	Familiarity with generalised linear models and basic knowledge of R
Sampling I	Oct–Nov 2023 (8 weeks)	Dr Raphael Nishimura	A sound background in applied statistics, proficiency in mathematics, including basic algebra
Introduction to Big Data/‌Machine Learning I	Jan–Feb 2024 (4 weeks)	Prof Frauke Kreuter and Prof Trent D. Buskirk	None, but undergrad statistics background, some familiarity with regression models assumed and familiarity with R recommended
Sampling II	Mar–Apr 2024 (4 weeks)	Dr Raphael Nishimura	Sampling I or equivalent; R skills helpful
Step-by-Step in Survey Weighting	Mar–Apr 2024 (4 weeks)	Dr Anna-Carolina Haensch	Sampling I or equivalent
Machine Learning II	Sep–Oct 2024 (8 weeks)	Prof Christoph Kern and Prof Trent D. Buskirk	Machine Learning I or equivalent and basic knowledge of R

Basic R skills can be acquired from a SSA R workshop which will be offered before the Machine Learning course or online e.g. via DataCamp or equivalent.

All courses are conducted over 4 or 8 weeks period (depending on the course) with weekly 1 hour online classes in addition to assignments and exam assessment at the end of the course. Participants should expect pre-recorded videos, readings and exercises to be completed outside of the weekly meetings consistent with a master’s course.

Indicative cost per course is $1,500(ex-GST) per attendee, with $1,250(ex-GST) per attendee volume discount for organisations enrolling three or more.

We are asking for expressions of interest in these courses to ensure there is sufficient demand for the courses to run. Please register your interest or contact events.statsoc@gmail.com with any questions.

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SSA WA: Perth Biostats/Bioinfo Meetup 2023

When
13 Jun 2023
5:30 PM - 7:30 PM (AWST)
Location
McCusker Auditorium, Harry Perkins Institute of Medical Research (North), QEII Medical Centre, 6 Verdun Street, Nedlands WA 6009

Register

We're pleased to invite you to the Perth Biostatistics/Bioinformatics Meetup. This is a joint event supported by the WA Branch of the Statistical Society of Australia (SSA), the SSA Biostatistics and Bioinformatics Section, Clinical Trials Enablement Platform WA (CTEP-WA), Perth Epidemiological Group (PEG), and the WA Health Translation Network (WAHTN).

Date: Tuesday, 13 June 2023.
Time: Refreshments at 5:30PM for a 6:00PM start
Venue: McCusker Auditorium, Harry Perkins Institute of Medical Research (North).

The purpose of this event is to bring together those with expertise and/or interest in medical and healthcare statistics in Perth. It is an opportunity to socialise and network, and encourage upcoming professionals to pursue a career in this worthwhile field.

There will be three invited speakers this evening: Dr Rich Edwards (UWA), Dr Matt Cooper (Telethon Kids Institute), and Mr Wes Billingham (Telethon Kids Institute).

More presentation details to come (23/05/2023).

Venue & Parking

This event is held at Harry Perkins Institute of Medical Research (North; Google Maps). The presentations will be hosted in McCusker Auditorium followed by refreshments in the building foyer.

Suggested parking is in Car Park 3A, accessed via Hampden Rd and Caladenia Cr.

Registration

This event is free but please register your attendance to assist with catering and meeting dietary requirements.

Please circulate this invitation amongst your networks to anyone who might be interested, especially students and those early in their careers.

If you have any queries, please contact the convenor, Shih Ching Fu.

Thank you again to all of this event's supporters.

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Much More Than Just Fitting Models - Perspective from an Applied Statistical Scientist

When
22 Jun 2023
2:00 PM - 3:00 PM (AEST)
Location
online

Register

SSA ECSSN and NZSA joint webinar: Much more than just fitting models - perspective from an applied statistical scientist, presented by Olivia Angelin-Bonnet.

June 22, 2023 2pm AEST, 4PM NZST

In this talk, She will be giving an overview of her role as a statistical scientist working in the fields of Systems Biology and multi-omics integration. In particular, she will discuss how being an applied data scientist is about much more than just fitting models. Olivia will talk about how modern statistician and data science roles involve working with big and messy datasets, developing complex analysis pipelines, communicating results through reports and presentations, and implementing tools for disseminating our work. A number of principles and tools (e.g. data and code versioning, unit testing, workflow management systems) can support us through these activities to ensure our work is sustainable and reproducible. She will argue that mastering these principles is rapidly becoming a core competency for the modern statistical scientist.

Biography:

Olivia completed her PhD in Statistics at Massey University, where she worked on unravelling genotype-to-phenotype relationships from multi-omics data, with a focus on polyploid organisms. After a year as a lecturer in Statistics at Massey University, she is now a statistical Scientist at Plant & Food Research. Her research interests include Systems Biology, multi-omics data integration, the study of biological networks from a statistical and computational perspective, and the development of visualisation tools for omics data.

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SSA NSW June event: Prof Lucy Marshall - Quantifying the known unknowns

When
27 Jun 2023
12:30 PM - 3:00 PM (AEST)
Location
14SCO T2 Theatre, Macquarie University and via Zoom (https://uni-sydney.zoom.us/meeting/register/tZ0sf-CvrTopGdUX4dkTWL3gcGK49mu6gYec)

Register

We are happy to announce a seminar by Prof Lucy Marshall and Ms Yiyi Ma. We hope to see you all there.

Any questions, please feel free to contact: secretary.nswbranch@statsoc.org.au.

Date: Tuesday, 27th June 2023

Time:

12.30 - 13.00: Lunch

13.00 - 13.45: Junior Speaker: Ms Yiyi Ma

13.45 - 14.00: Break

14.00 - 15.00: Prof Lucy Marshall

Venue: 14SCO T2 Theatre, Macquarie University and via Zoom.

RSVP: Register at https://statsoc.org.au/event-5298441 or https://uni-sydney.zoom.us/meeting/register/tZ0sf-CvrTopGdUX4dkTWL3gcGK49mu6gYec

Title: Quantifying the known unknowns: Data science and machine learning in environmental analysis

Speaker: Prof. Lucy Marshall

Abstract:

Environmental models are essential tools for understanding complex natural systems and predicting the impacts of human activities on the environment. By simulating the behavior of physical, chemical, and biological processes in natural systems, environmental models can help make informed decisions about environmental management, policy, and planning. Environmental modeling has undergone significant advancements in recent years, transitioning from simplistic representations of ecological systems to sophisticated and integrated modeling platforms. With this advancement comes increasing recognition of the value of modern data science methods for model building, uncertainty quantification, and improved predictions about future environmental states and hazards. These methods generate huge opportunities to leverage the increase in computational power, big data sets, and novel sensing technologies for unprecedented model performance.

However, these opportunities heighten the debate about the use of methods like machine learning in disciplines like hydrology, ecology, or biogeochemistry, that traditionally has relied on the knowledge of natural and engineered systems for model development (also known as ‘physically-based’ models). While recent data science methods suggest great promise, will their advancement be limited by the divorce of machine learning models from the knowledge-base of physical systems?

This presentation will provide insight into these issues across a range of modeling scenarios with a focus on Bayesian inference and its application to environmental systems. We track the evolution of Bayesian methods from conceptual rainfall model inference, through to multi-objective analysis of integrated ecohydrologic and water quality models, through to hybrid machine-learning/physically-based models. Through a series of case studies, we demonstrate recent advances in data science in two areas: (1) novel approaches to uncertainty quantification by characterizing errors in different environmental data sets; (2) the use of hybrid machine learning methods for improved uncertainty quantification of high-dimensional environmental models. Overall, our studies demonstrate the power of data science for future and current modelling applications, but recognizes the need for improved training of future engineers and scientists to think probabilistically, and be expert users of data and models.

Biography:

Lucy Marshall is a Professor of Engineering and Executive Dean of the Faculty of Science and Engineering. She is a water resources engineer, with expertise in hydrologic modeling, environmental model optimization, and quantification of uncertainty in water resources analysis. She has a special interest in understanding how environmental observations can be used to quantify uncertainty in systems undergoing change. Her research has spanned the development of new models in the most heavily instrumented watershed in the United States to making flood predictions in ungauged catchments across Australia.

Lucy completed her BEng (Hons), MEngSc, and PhD at the University of New South Wales (UNSW) in Sydney before moving to Montana State University as an Assistant Professor of Watershed Analysis, where she worked at the interface of engineering and environmental science. She returned to Australia as an Australian Research Council Future Fellow at UNSW, and went on to become the director of the UNSW Water Research Centre. She held multiple leadership positions at UNSW, as the inaugural Associate Dean (Equity and Diversity), Associate Dean (Research), and the academic lead for Athena SWAN. She joined Macquarie as Executive Dean in 2022.

Junior Speaker: Yiyi Ma (UNSW)

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