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Events listing - SSA events

To have your event added to this list, please forward the event details, including url, to our Events Coordinator Jodi Phillips.

Upcoming events

    • 16 Nov 2023
    • (AEDT)
    • 31 Oct 2024
    • (AEDT)
    • 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 202324

     

    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.


    • 6 Feb 2024
    • (AEDT)
    • 27 Feb 2024
    • (AEDT)
    • Online
    Registration is closed

    The Social Research Centre and the Statistical Society of Australia (SSA) are very proud to offer statistical training from the International Program in Survey and Data Science (IPSDS), a joint program of the University of Mannheim and the Joint Program in Survey Methodology at the University of Maryland. Places are limited, please register early to take advantage of early bird discounts and secure a place.

    About the course:

    The amount of data generated as a by-product in society is growing fast including data from satellites, sensors, transactions, social media and smartphones, just to name a few. Such data are often referred to as "big data" and can be used to create value in different areas such as health and crime prevention, commerce and fraud detection. Big Data are often used for prediction and classification tasks. Both of which can be tackled with machine learning techniques. In this course, we explore how Big Data concepts, processes and methods can be used within the context of Survey Research. Throughout this course we will illustrate key concepts using specific survey research examples including tailored survey designs and nonresponse adjustments and evaluation.

    Presenters: Prof. Frauke Kreuter and Prof. Trent Buskirk

    Frauke Kreuter holds the Chair of Statistics and Data Science at LMU Munich, Germany and at the University of Maryland, USA, she is Co-director of the Social Data Science Center (SoDa) and faculty member in the Joint Program in Survey Methodology (JPSM). She is an elected fellow of the American Statistical Association, and received the Warren Mitofsky Innovators Award of the American Association for Public Opinion Research in 2020.

    In addition to her academic work, Professor Kreuter is the Founder of the International Program for Survey and Data Science (IPSDS), developed in response to the increasing demand from researchers and practitioners for the appropriate methods and right tools to face a changing data environment and has extensive experience with online instruction.

    Trent Buskirk is Novak Family Professor of Data Science and Chair of the Applied Statistics and Operations Research Department at Bowling Green State University as well as an Adjunct Research Professor at the University of Michigan and a Fellow of the American Statistical Association. His research includes the areas of Mobile and Smartphone Survey Designs, methods for calibrating and weighting nonprobability samples, and the use of big data and machine learning methods for health, social and survey science design and analysis. His research has been published in leading journals such as Cancer, Social Science Computer Review, Journal of Official Statistics, and the Journal of Survey Statistics and Methodology.

    Timeframe:

    Course duration: February 6 – February 27, 2024

    Weekly meetings: Tuesdays 10 AM AEDT – 11 AM AEDT.

    Course objectives:

    The course will cover
    •    An overview of key Big Data terminology and concepts
    •    An introduction to common data generating processes
    •    A discussion of some primary issues with linking Big Data with Survey Data
    •    Issues of coverage and measurement errors within the Big Data context
    •    Inference versus prediction
    •    General concepts from machine learning including signal detection and information extraction
    •    Potential pitfalls for inference from Big Data
    •    Key analytic techniques (e.g. classification trees, random forests, conditional forests) to process Big Data using R, with example code provided

    Weekly topics:

    1.    Overview of Big Data; Working with Big Data, Classical Statistical Approaches vs. Statistical Machine Learning
    2.    Model Evaluation/Validation, K-Means Clustering
    3.    Nearest Neighbours, CARTS
    4.    Random Forests


    Software:

    Example code in R will be provided. R is downloaded for free from http://cran.r-project.org/. Participants may also find https://www.rstudio.com/ a helpful interface to execute program code. For those new to R, there are many MarinStatsLectures available at https://www.youtube.com/playlist?list=PLqzoL9-eJTNBDdKgJgJzaQcY6OXmsXAHU

    Prerequisites

    We recommend good understanding of the material typically taught in undergraduate statistics courses and some familiarity with regression techniques and fundamentals of survey and data science. While not a prerequisite, familiarity with the R software package (base R or R using Rstudio) is strongly encouraged. If you are unsure whether you meet the prerequisites please email events@statsoc.org.au describing your background and experience with sampling.

    Reading:

    There is no required textbook. Useful recommended resources and reading will be provided to participants as part of course materials. 

    Grading:
    •    4 online quizzes (worth 5% each)
    •    Participation in discussion during the weekly online meetings and submission of questions to the weekly discussion forums demonstrating understanding of the required readings and video lectures (20% of grade)
    •    3 homework assignments (worth 20% each)


    Early Bird Deadline:
    Please book before 15 December 2023 to take advantage of the Early Bird Deadline.

    Disclaimer:
    Participants will receive access data for the online course, in particular to any learning platform that may be used. The rights of use connected to the access data are personally assigned to the participant. Passing on the access data is not allowed. Also, the temporary transfer to third parties is not permitted.
    The right to use the transmitted access data, in particular with regard to any materials or video recordings provided, can only be exercised up to a maximum of 2 months after the program end. After expiration of this 2-months period, the access data will be deleted by Mannheim Business School (MBS). Before the expiration of this period, the participant may view the respective recorded course as often as desired and without time restriction.
    If we have reasons to believe that the participant is abusing the right of use granted to him or that there is a violation of the terms of use, MBS reserves the right to change the participant’s access data as well as to partially or completely block the access or to prohibit the further use of the digital content.

    Group bookings

    For group bookings, please email events@statsoc.org.au with the names, email addresses, and telephone numbers  of the participants in the group.

    Cancellation Policy
    Occasionally courses have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen.

    Cancellations received prior to two weeks before the event will be refunded, minus a $20 administration fee. From then onward no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to events@statsoc.org.au.

    For any questions, please email events@statsoc.org.au

    • 15 Feb 2024
    • (AEDT)
    • 7 Mar 2024
    • (AEDT)
    • online- live 1 hour sessions on Thursdays at 6pm AEDT
    Registration is closed

    The Social Research Centre and the Statistical Society of Australia (SSA) are very proud to offer statistical training from the International Program in Survey and Data Science (IPSDS), a joint program of the University of Mannheim and the Joint Program in Survey Methodology at the University of Maryland. Places are limited, please register early to take advantage of early bird discounts and secure a place.

    About the course:

    Step by Step in Survey Weighting is a statistical methods class that combines hands-on applications and general review of the theory for survey weighting giving participants the necessary tools to calculate analysis weights for various survey designs in a real-world setting. The course will focus on methods to solve practical problems while providing overview of the theory for the underlying assumptions. Weekly homework problems are included to reinforce learning and provide opportunity to apply methods in practice. Participants are encouraged to discuss their own weighting challenges and solutions during weekly online meetings.

    Presenter: Anna-Carolina Haensch

    Anna-Carolina has worked with the International Program in Survey and Data Science (IPSDS) team since 2019 and enjoys teaching quantitative courses to Bachelor and Master students. Anna-Carolina’s research interests include Multiple Imputation, Big Data in the Social Sciences, and Statistics and Data Science training. She has worked at GESIS Institute, University of Mannheim and is also involved in the CTIS survey run by Facebook in cooperation with the University of Maryland.

    Timeframe:

    Course duration: February 15 – March 7, 2024

    Weekly meetings: Thursdays 6 PM AEDT –7 PM AEDT.

    Course objectives:

     By the end of the course, participants will understand
    •   The role of survey weights in population inference.
    •   Steps in weighting, including computation of base weights for single and multi-stage designs, nonresponse adjustments, and uses of auxiliary data.
    •   Nonresponse adjustment alternatives, including weighting cell adjustments, formation of cells using classification algorithms, and propensity score adjustments.
    •   Weighting via poststratification, raking, general regression estimation, and other types of calibration to align survey estimates with known population values.
    •   Assessing whether the weights are necessary

    Weekly Topics:
    1. Basic Steps in Weighting
    2. Basic Steps in Weighting (continued)
    3. Calibration and Other Uses of Auxiliary Data in Weighting
    4. Calibration (continued) and Replicate Weights

    Software:

    This course will emphasize R but some examples in SAS and Stata are also discussed. R is downloaded for free from http://cran.r-project.org/. Participants may also find https://www.rstudio.com/ a helpful interface to execute program code. For those new to R, there are many MarinStatsLectures available at https://www.youtube.com/playlist?list=PLqzoL9-eJTNBDdKgJgJzaQcY6OXmsXAHU

    Prerequisites

    As this is a statistical methods course, understanding of sampling theory and applied sampling is required. Some experience with variance estimation, statistical analysis using survey data, and the R statistical computing software will be helpful. If you are unsure whether you meet the prerequisites please email events@statsoc.org.au describing your background and experience with sampling.

    Reading:

    Valliant, R., Dever, J.A., and Kreuter, F. (2018). Practical Tools for Designing and Weighting Survey Samples, 2nd Edition. New York: Springer.  Please note that the recorded lectures are provided as a supplement to and not a substitution for the course readings. 

    Grading:

    • 4 Homework assignments (50% of grade)

    • A take-home final exam (30% of grade)

    • Class Participation (20% of grade) in discussion during the weekly online meetings and posting questions to the weekly discussion forum (deadline: 24 hours before class) demonstrating understanding of the required readings and video lectures

    Early Bird Deadline:
    Please book before 15 December 2023 to take advantage of the Early Bird Deadline.

    Disclaimer:
    Participants will receive access data for the online course, in particular to any learning platform that may be used. The rights of use connected to the access data are personally assigned to the participant. Passing on the access data is not allowed. Also, the temporary transfer to third parties is not permitted.
    The right to use the transmitted access data, in particular with regard to any materials or video recordings provided, can only be exercised up to a maximum of 2 months after the program end. After expiration of this 2-months period, the access data will be deleted by Mannheim Business School (MBS). Before the expiration of this period, the participant may view the respective recorded course as often as desired and without time restriction.
    If we have reasons to believe that the participant is abusing the right of use granted to him or that there is a violation of the terms of use, MBS reserves the right to change the participant’s access data as well as to partially or completely block the access or to prohibit the further use of the digital content.

    Group bookings

    For group bookings, please email events@statsoc.org.au with the names, email addresses, and telephone numbers  of the participants in the group.

    Cancellation Policy
    Occasionally courses have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen.

    Cancellations received prior to two weeks before the event will be refunded, minus a $20 administration fee. From then onward no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to events@statsoc.org.au.

    For any questions, please email events@statsoc.org.au

    • 12 Mar 2024
    • 5:15 PM - 7:00 PM (AWST)
    • Cheryl Praeger Lecture Room, The University of Western Australia
    Register

    The first meeting for 2024 is the Annual General Meeting (AGM) of the WA Branch of the Statistical Society of Australia.


    Date: Tuesday, 12 March 2024

    Time (AGM): 5:15PM to 6:00PM (AWST)

    Time (Guest Presentation): 6:00PM to 7:00PM (AWST)

    Location: Cheryl Praeger Lecture Room, The University of Western Australia.


    Non-members of the SSA WA Branch are welcome at the AGM but cannot vote on Branch matters. Following the AGM, Mr Alex Jenkins of the WA Data Science Innovation Hub (WADSIH) will deliver the guest address.


    Annual General Meeting

    The AGM agenda, proxy forms, and other relevant documents are emailed to members directly. Please contact the WA Branch Secretary if you have not received these by the preceding Monday.

    The election of the 2024 WA Branch Council will occur at the AGM.

    Presentation

    Data Science in WA

    Alex Jenkins, Director, WA Data Science Innovation Hub (WADSIH)

    In his presentation Alex will speak on WADSIH's work with data and AI, including opportunities with synthetic data. He will discuss the implications of AI, language models, and embedding models for industry an research. Furthermore there will be a discussion on potential areas of collaboration between WADSIH and the Statistical Society of Australia and its members.

    About the Speaker

    Alex Jenkins has over 15 years of experience working in the technology space, implementing and advocating for data science and analytics solutions. He used his expertise to build data science capabilities for mining technology businesses. Alex has also worked with leading organisations including ABB, BHP, St John of God Healthcare and Imdex to define big data, supply chain modelling and data science solutions. In June 2021 Alex was appointed director of the WA Data Science Innovation Hub, an initiative of the Western Australian Government New Industries Fund which helps industry, government and academia to build their data science capability and advance the application of data science throughout Western Australia.

    Refreshments and Dinner

    Members and visitors are invited to mingle over wine and cheese from 5:00PM onwards at the venue. Following the meeting all are invited to dine at a nearby restaurant. Visitors are welcome.

    Meeting Directions

    The Cheryl Praeger Lecture Room is located on the ground floor of the Mathematics building at The University of Western Australia. Its entrance is on the northern side of the building. See: UWA Maps, Google Maps.

    Parking is free on the UWA Crawley campus after 5:00PM. A convenient place to park is Car Park 18 accessible from Fairway Entry 1.

    For further information please contact the WA Branch Secretary (ssa.wa.secretary@gmail.com).
    • 19 Mar 2024
    • 5:30 PM - 6:00 PM (AEDT)
    • Staff Tea Room, Peter Hall Building, The University of Melbourne OR CSIRO, 3-4 Castray Esplanade, Battery Point, Hobart OR online via Zoom
    Register

    Join us for the SSA Vic & Tas annual general meeting, where we will recap on our last year and vote in the new council for 2024. The annual report will be made available closer to the date of the AGM.

    Following the AGM we will be hearing from Helen Baird, Program Manager a/g of the Standards, Classifications and Business Register Branch at the Australian Bureau of Statistics on how the ABS develops standards. To find out more and register for the talk, visit the event page: https://statsoc.org.au/event-5621667

    To register for the AGM you must be signed in to your SSA member account.


    • 19 Mar 2024
    • 6:00 PM - 7:00 PM
    • Evan Williams Theatre, Peter Hall Building, The University of Melbourne OR CSIRO, 3-4 Castray Esplanade, Battery Point, Hobart OR online via Zoom
    Register

    Following the SSA Vic & Tas AGM we will hear from Helen Baird, Program Manager a/c of the Standards, Classifications and Business Registers Branch of the Australian Bureau of Statistics, on how the ABS goes about developing Standards.

    Abstract

    Coming soon

    Speaker Biography

    Coming soon

    Post-event social

    Following the conclusion of the talk we will head to a nearby restaurant where attendees of the event are welcome to join us at their own expense.

    • 21 Mar 2024
    • (AEDT)
    • 22 Mar 2024
    • (AEDT)
    • Ground Floor Conference Rooms, Monash University, School of Public Health and Preventive Medicine, 553 St Kilda Road, Melbourne

    Assessing the risk of bias in studies evaluating the effects of interventions and of exposures (in- person workshop)

    Professor Julian Higgins, University of Bristol, will be leading this two-day (in-person) workshop, providing a unique opportunity to learn from the lead developer of the risk of bias tools. The course is designed for those undertaking systematic reviews, synthesizing evidence for guidelines, or generally interested in how to appraise studies. For registration information, please see here

    Day 1 of the course will introduce participants to tools for assessing risk of bias in studies with a focus on randomized trials using the RoB 2 tool. Day 2 of the course will cover assessing the risk of bias in non-randomized/observational studies, covering the ROBINS-I and ROBINS-E tools. Familiarity with domain-based risk-of-bias assessment tools and the issues addressed by RoB 2 tool will be assumed for Day 2. Therefore, participants who are not familiar with the RoB 2 tool will need to attend both days. Details of what will be covered each day are below.

    The workshop involves a mix of presentations, interactive examples and hands-on exercises.

    Electronic copies of the slides will be provided on the day of the workshop.

     

    Outline

    Day 1:

    • Introduction to risk-of-bias assessment
    • Overview of the RoB 2 tool
    • Bias arising from the randomization process
    • Intention-to-treat versus per-protocol effects and bias due deviations from intended interventions
    • Bias due to missing data
    • Bias arising from measurement of the outcome
    • Bias in selection of the reported result

    Day 2:

    • Introduction to the ROBINS tools
    • Preliminary considerations, including specification of the causal effect of interest
    • Bias due to confounding
    • Bias in classification of intervention or measurement of exposure
    • Bias in selection of participants into the study
    • Bias due to deviations from intended intervention or post-exposure interventions
    • Bias due to missing data, measurement of the outcome and selection of the reported result

     

    Course facilitators

    Julian Higgins, University of Bristol, UK: Julian is Professor of Evidence Synthesis with a long-standing interest in methodology of systematic reviews and meta-analysis. He is author of over 350 publications, collectively cited more than 350,000 times. His contributions include: a Bayesian approach to network meta-analysis; the I-squared statistic to quantify inconsistency across studies in a meta-analysis; simple prediction intervals for random-effects meta-analysis; a general framework for individual participant data meta-analysis; and risk-of-bias assessment tools for clinical trials and other study designs. He is a past President of the Society for Research Synthesis Methodology and has co-edited the Cochrane Handbook for Systematic Reviews of Interventions since 2003. He is also co-author of the Wiley textbook Introduction to Meta-analysis and co-editor of the 3rd edition of Wiley textbook Systematic Reviews in Health Research: Meta-analysis in Context. Read more about Julian’s research interests here.

    Matthew Page, Monash University: Matthew is a Senior Research Fellow and Deputy Head of the Methods in Evidence Synthesis Unit. His research aims to improve the quality of systematic reviews of health and medical research. He has led many studies investigating the transparency, reproducibility and risk of bias in systematic reviews and the studies they include and has developed several methods to address these issues. For example, he co-led the development of the PRISMA 2020 statement, a highly cited reporting guideline for systematic reviews, was a member of the core group who developed the RoB 2 tool for assessing risk of bias in randomized trials, and led the development of the ROB-ME tool for assessing risk of bias due to missing evidence in meta-analyses. He is a member of Cochrane’s Methods Executive, the group that is responsible for directing the methods used within Cochrane Reviews. Read more about Matthew’s research interests here.

    Joanne McKenzie, Monash University: Jo is a Professor and Head of the Methods in Evidence Synthesis Unit. She leads a programme of research on methods for evidence synthesis, with some key areas of interest being methods to present and synthesize results when meta-analysis is not possible, statistical methods for analysing and meta-analysing results from interrupted time series studies, and the development of reporting guidelines for different evidence synthesis products. She co-led the PRISMA 2020 statement and contributed to the development of the ROB-ME tool for assessing risk of bias due to missing evidence in meta-analyses. She is an active contributor to Cochrane, including being a Co-convenor of the Statistical Methods Group and an author of several chapters of the Cochrane Handbook for Systematic Reviews of Interventions. Read more about Jo’s research interests here.

     

    Organisation

    The Methods in Evidence Synthesis Unit (MESU) sits within the School of Public Health and Preventive Medicine at Monash University. The Unit’s mission is to develop, evaluate and make accessible optimal statistical and research methodology for evidence synthesis. The MESU team has led and contributed to major developments and understanding in evidence synthesis including developing reporting guidelines (PRISMA 2020, PRIOR, SWiM, and extensions to PRISMA 2020), risk of bias tools (ROB-ME, RoB 2), methods for synthesis when meta-analysis is not possible, methods for meta-analysing results from non-randomised studies, methods for overviews of systematic reviews, examining reproducibility in systematic reviews and bias in the review process. MESU staff regularly provide training to researchers, nationally and internationally, and collaborate on systematic reviews. MESU is funded through nationally competitive NHMRC and ARC grants. 

    Cochrane Australia sits within the School of Public Health and Preventive Medicine at Monash University and is a centre of expertise in evidence synthesis and the use of evidence to support healthcare decision-making. Cochrane Australia conducts evidence syntheses for government and public sector agencies, provides guidance and training on how to conduct evidence syntheses, and works with guideline developers and others to translate research into healthcare practice and policy.

     


    • 2 May 2024
    • (CEST)
    • 4 May 2024
    • (CEST)
    • Valencia, Spain

    Call for Papers

    They invite submissions to the 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), and welcome paper submissions on artificial intelligence, machine learning, statistics, and related areas.

    AISTATS is an interdisciplinary gathering of researchers at the intersection of computer science, artificial intelligence, machine learning, statistics, and related areas. Since its inception in 1985, the primary goal of AISTATS has been to broaden research in these fields by promoting the exchange of ideas among them. The conference is committed to diversity in all its forms, and encourages submissions from authors of underrepresented groups and geographies in ML/AI.

    Key dates

    • Abstract deadline: 6 October 2023 (Anywhere on Earth)
    • Paper submission deadline: 13 October 2023 (Anywhere on Earth)
    • Appendix submission deadline: 20 October 2023
    • Reviews released: 27 November 2023
    • Author rebuttals due: 5 December 2023 (Anywhere on Earth)
    • Paper decision notifications: 19 January 2024
    • Conference dates: May 2 - May 4, 2024

    Paper Submission (Proceedings Track)

    The proceedings track is the standard AISTATS paper submission track. Papers will be selected via a rigorous double-blind peer-review process. All accepted papers will be presented at the Conference as contributed talks or as posters and will be published in the Proceedings.

    Solicited topics include, but are not limited to:

    • Machine learning methods and algorithms (classification, regression, unsupervised and semi-supervised learning, clustering, logic programming, …)
    • Probabilistic methods (Bayesian methods, approximate inference, density estimation, tractable probabilistic models, probabilistic programming, …)
    • Theory of machine learning and statistics (optimization, computational learning theory, decision theory, online leaning and bandits, game theory, frequentist statistics, information theory, …)
    • Deep learning (theory, architectures, generative models, optimization for neural networks, …)
    • Reinforcement learning (theory of RL, offline/online RL, deep RL, multi-agent RL, …)
    • Ethical and trustworthy machine learning (causality, fairness, interpretability, privacy, robustness, safety, …)
    • Applications of machine learning and statistics (including natural language, signal processing, computer vision, physical sciences, social sciences, sustainability and climate, healthcare, …)

    Formatting and Supplementary Material

    Submissions are limited to 8 pages excluding references using the LaTeX style file we provide below (the page limit will be 9 for camera-ready submissions). The number of pages containing only citations and the reproducibility checklist is not limited. You can also submit a single file of additional supplementary material which may be either a pdf file (such as proof details) or a zip file for other formats/more files (such as code or videos). Note that reviewers are under no obligation to examine the supplementary material. If you have only one supplementary pdf file, please upload it as is; otherwise gather everything to the single zip file.

    Submissions are accepted at https://cmt3.research.microsoft.com/AISTATS2024/.

    Formatting information (including LaTeX style files) is available in the AISTATS2024PaperPack. We do not support submission in preparation systems other than LaTeX. Please do not modify the layout given by the style file. If you have questions about the style file or its usage, please contact the publications chair or the program chairs via aistats2024conference@gmail.com.


    For more information about submission or registration click here.

    • 8 May 2024
    • (NZST)
    • 9 May 2024
    • (NZST)
    • National Library in Wellington, New Zealand

    Join R Exchange 2024

    Now in its 4th year, R Exchange provides a regional opportunity for New Zealand and Australian open-source analytics and data science enthusiasts to connect and learn from each other. The programme showcases how organisations across a variety of disciplines are using R and related tools to create insights for decision-making and align them with strategic goals. Learn with us how to better visualise outputs, automate reporting, and scale up work flows and data products. If you haven’t worked with R and other open-source tools yet, get inspired by how others use them and what can be done.

    R Exchange 2024 will take place on 8-9 May at the National Library in Wellington, New Zealand.

    The event will include a conference day full of presentations, panel discussions and networking; as well as a pre-conference workshop (see details below).


    Click here for more details, the full schedule and to register

    Don’t miss out on the R community event!
    The Epi-interactive team

     


     

    R Shiny in Production

    Pre-event workshop (8 May 2024)

     

    Moving beyond conceptualising R Shiny apps to deploying them in real-world scenarios can be challenging. This workshop addresses the gap, focusing on essential software engineering and DevOps practices to elevate your R Shiny projects. Dive deep into code modularisation, performance optimisation, testing, security, and data source integration. Ideal for those progressing from basic R Shiny skills, this workshop caters to developers looking to enhance existing projects or embark on new ones.

    Facilitated by: Nick Snellgrove (Tech Lead – R Development, Epi-interactive), Ieuan Jenkins (Tech Lead – Infrastructure, Epi-interactive) & Dr Stefan Schliebs (Senior Data Scientist, Air New Zealand)


    Learn more and register here

    Please note: This workshop does assume familiarity with R and prior experience with R Shiny, either acquired on your own or through attending our Introduction or Advanced R Shiny Masterclass, which will be offered online in February and March. Places are limited and will be allocated on a first come – first served basis.

     




    Connecting data, science & people

     

    • At Epi-interactive they use open-source technologies, visualise data, craft dashboards, research websites and analytical software, or help you fine-tune your data science infrastructure. Learn more or connect at www.epi-interactive.com.
    • As a Full-Service Posit Partner they can help you succeed with Posit professional products such as Posit Server, Connect or Package Manager at all levels. You can buy all licenses at list price directly through them and access added support as you need it: epi-interactive.com/posit.
    • They are B Corp certified, joining a global community of like-minded businesses that deeply care about people, community and the environment, not just profit.


    Follow them on LinkedIn for news and updates or contact them on info@epi-interactive.com if you would like to discuss your project idea.

     

    • 13 May 2024
    • (BST)
    • 15 May 2024
    • (BST)
    • Online

    FOURTH INTERNATIONAL CONFERENCE ON STEPPED WEDGE TRIAL DESIGN


    13th-15th May 2024, FREE ONLINE CONFERENCE

    The University of York is pleased to announce the Fourth International Conference on Stepped Wedge Trial Design ONLINE on 13th-15th May 2024.

    The conference is a platform to share ideas, experience, best practice and challenges for the design, implementation and analysis of the stepped wedge design model.

    Keynote Speakers: Professor Richard Hooper (Queen Mary University of London) and Associate Professor Jessica Kasza (Monash University)

    Registration is FREE.

    Abstract submissions are welcome now (Deadline: Noon 16th February 2024).
    We especially encourage contributions from underrepresented groups and diverse fields of application.

    For further information and link to the abstract submission form, see:
    https://www.york.ac.uk/healthsciences/research/trials/stepped-wedge2024/

    Free and open to all (Registrations will open in February 2024)

    • 14 Jul 2024
    • (BST)
    • 19 Jul 2024
    • (BST)
    • Durham, England, UK.

    UPDATE!!!! The deadline for 4-pages short paper submission for the 38th International Workshop on Statistical Modelling (IWSM2024) in Durham-UK (15-19 July 2024) has been extended to Thursday 27th of February 2024. Please visit the conference website https://maths.dur.ac.uk/iwsm2024/.  and submission details IWSM2024 Durham | Call for Papers. for more information.



    The 38th International Workshop on Statistical Modelling (IWSM) will take place in Durham City, England, UK.

    The IWSM is one of the major activities of the Statistical Modelling Society, founded with the purpose of promoting and encouraging statistical modelling in its widest sense. The workshop aims to involve both academic and professional statisticians and data analysts with a particular focus on real data problems which involve an element of novel statistical modelling, or novel model application.

    The atmosphere of the workshop is friendly and supportive, with no parallel sessions, with the aim of stimulating the exchange of ideas and experiences related to statistical modelling.

    Papers focusing on applications with important substantive implications as well as methodological issues are welcome, including new developments in Data Science. Submissions by students and young researchers are particularly encouraged.

    The programme will consist of a short course on the Sunday (July 14th), an academic programme spanning from Monday (July 15th) morning to Friday (July 19th) lunchtime, and a social programme including a welcome reception (Monday), an excursion (Wednesday afternoon) and a conference dinner (Thursday).

    Registration: Event Booking Details (durham.ac.uk)

    Paper submission deadline: February 20, 2024