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X-WR-CALDESC:Events for Australian Data Science Network
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DTSTART:20210101T000000
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DTSTART;VALUE=DATE:20221121
DTEND;VALUE=DATE:20221125
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SUMMARY:AMSI BioInfoSummer 2022
DESCRIPTION:Develop your bioinformatics skills\, national networks and employability at AMSI BioInfoSummer 2022. Hosted by Melbourne Integrative Genomics\, participants will explore bioinformatics under the themes of high-resolution biology\, genome architecture\, whole-cell modelling\, populations over the four-day program.
URL:https://australiandatascience.net/event/amsi-bioinfosummer-2022/
LOCATION:The University of Melbourne\, Parkville\, Victoria\, 3010\, Australia
CATEGORIES:Event
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20221121
DTEND;VALUE=DATE:20221123
DTSTAMP:20221120T070514Z
CREATED:20220822T060648Z
LAST-MODIFIED:20221120T070514Z
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SUMMARY:ADSN Inaugural Conference
DESCRIPTION:Day\n				Hour\n				Minute\n				Second\n	Welcome to the inaugural conference for the Australian Data Science Network! The QUT Centre for Data Science is host organisation for the conference being held at QUT in Brisbane over two days on Monday and Tuesday\, 21-22 November. \nThe ADSN conference is an in-person event. One of the main aims is to get members from partner organisations together to build collaborations across the network that have not happened because of the COVID-19 pandemic. \nWe have an exciting lineup of invited speakers and other events! \nKeynote Speaker \n\nDay 1: Tomasz Bednarz\, Director of Strategic Researcher Engagement at NVIDIA\nDay 2: Richard Fox\, Data & Analytics Manager for the AFL (Australian Football League)\n\n				\n				\n				\n	Invited Speakers\n	Day 1: Monday\, 21 November \n					Becki Cook\, Centre Manager\, QUT Centre for Data Science\n											Expand\nTitle: Indigenous Data Science: co-designing a framework for best practice enhancement of data literacy.  \nAbstract: This session will explore considerations surrounding Indigenous Research\, in particular developing Indigenous research projects\, undertaking research with Aboriginal and Torres Strait Islander Peoples\, Indigenous data sovereignty and data governance. This will be demonstrated though discussing how researchers in the QUT Centre for Data Science are engaging with the Aboriginal and Torres Strait Islander Community Heath Service Brisbane to co-design a framework for best practice enhancement of data literacy. \n					A/Prof Yoni Nazarathy\, The University of Queensland\n											Expand\n					Title: Julia as a data science research tool. \nAbstract: Data Science practice and research thrives on open source computer programming languages and their supporting ecosystems. When taking the statistical viewpoint\, the R language is probably the most common choice\, and when focusing on machine learning\, Python certainly rules. However\, most computationally demanding packages for these languages use other machinery under the hood\, often written in C or Fortran. This prevailing “multi-language” approach is fine for high level end users\, yet it poses a serious entry barrier for research and innovation. It requires those developing and deploying new computationally intensive data science ideas to have expertise not only in data science domains\, but also in low level software engineering. \nThe emerging Julia language and its ecosystem aims to overcome this barrier by leveraging on several 21st century software technologies and ideas. Julia “feels like” a combination of Matlab\, Python\, and R to the end user\, yet runs incredibly fast. Since its birth\, about a decade ago\, Julia has attracted practitioners and researchers from applied maths\, operations research\, machine learning\, statistics\, and general data science domains\, and by now it hosts a rich eco-system useful both for applied high level data science analysis\, and for quick transfer of new research ideas from pen and paper to software. In this talk we discuss the Julia data science eco-system and highlight the pros and cons of using Julia as data science research tool. \n					Dr Emi Tanaka\, Monash University Econometrics & Business Statistics\n											Expand\n					Title: Towards a unified language in experimental designs \nAbstract: Experimental data are hallmarks of scientific evidence to prove or disprove theories or hypotheses. Multiple people with different expertise are typically involved in planning and executing experiments but rarely is the communication easy or seamless\, especially across people from different domains\, yet we predicate on the assumption that misapprehensions will be somehow sorted out. This assumption leaves the success of an experiment at the mercy of the interpersonal communication skills of people involved. Rather than leaving the success of an experiment to serendipity\, I propose a novel framework to robustify the workflow of the construction of experimental designs that encourages users to deliberate on understanding the experimental structure. This framework\, called “the grammar of experimental designs”\, considers an object oriented system to encapsulate the experimental structure in a cognitive programming approach. I demonstrate this approach using the R packages\, edibble and deggust. \n					Dr Astrid Zeman\, Melbourne Centre for Data Science\n											Expand\n					Title: Analysing images using Deep Convolutional Neural Networks to supplement human decision making \nAbstract: Recognising and classifying objects within images is generally a straightforward task for Deep Convolutional Neural Networks (DCNNs)\, with their performance exceeding humans in benchmark competition datasets since 2015. Their impressive performance on object images allow for relatively simple integration into automated systems. An open question is how well do these networks deal with more challenging datasets\, such as those containing medical images\, which would require a level of visual expertise in human observers? I describe a case study in collaboration with the UZLeuven hospital in Belgium\, where we analysed a dataset of over 30\,000 microscopy images of fertilised human embryos. To date\, DCNNS have assisted in classifying embryos as early as day 5 after insemination. We investigated whether DCNNs could successfully predict the destiny of each embryo (discard or transfer) at an even earlier stage\, namely at day 3. We first assessed whether the destiny of each embryo could be derived from technician scores\, to examine whether the ratings that technicians gave to images correlated with the decision made. We then explored whether a DCNN could make accurate predictions using images alone. We found that a simple 8-layer network was able to achieve 75.24% accuracy of destiny prediction\, outperforming deeper\, state-of-the-art models. Importantly\, when analysing cases of transferred embryos\, we found that our lean\, DCNN predictions were correlated (0.65) with clinical outcomes. I describe some of the known shortcomings of DCNNs compared to human observers\, which is especially relevant when integrating this technology within a clinical context for making medical decisions. \n	Industry Panel (Day 1)\n\nDr Matt Aburn\, WearOptimo\nEmma Black\, Black Box Co\nProf Mark Harvey\, QUT VP of Business Development\nSuzy Lynch-Watson\, Metso Outotec\nDr Iain McCowan\, Dubber AI\n\nModerator: Prof Michael Rosemann\, Director of QUT’s Centre for Future Enterprise \n	CONFERENCE LOCATION: \nQUT Gardens Point Campus\, S-Block\, Room 1215 \n\nQUT Campus Map\n\nClick here for accommodation options near QUT Gardens Point. \n	Day 2: Tuesday\, 22 November \n					Prof Joanna Batstone\, Monash Data Futures Institute\n											Expand\n					Title: “AI and data science for social good” \nAbstract: The Monash Data Futures Institute brings together leading cross-disciplinary expertise\, international partnerships and a large affiliate network to address future technologies\, social partnerships and advanced applications. The Institute’s ifocus areas include using data-driven AI to enhance governance and policy\, sustainable development\, climate change\, health sciences and thriving communities.  This talk will include examples of the AI and data science work underway at Monash in the context of Australia’s opportunity for AI leadership around social change. \n					Prof Lewis Mitchell\, The University of Adelaide\n											Expand\n					Title: Data science and mathematical modelling \nAbstract: Data science has become one of the “buzzwords” of the past 10 years\, in both academic and industry contexts. However\, it involves many of the same core skills once associated with mathematical modelling: real-world applications\, computation\, data analysis\, and importantly\, assumptions-based modelling. Does data science present an existential threat to mathematical modellers? In this talk I’ll attempt to define data science\, and discuss its interconnections with mathematical modelling\, illustrating with examples from my own research. Far from being a threat\, I will argue that data science and mathematics (both pure and applied) have many synergies\, and that the two disciplines can work together and interact for mutual benefit. \n					Prof Richi Nayak\, QUT Centre for Data Science\n											Expand\n					Title: Neural Natural Language Processing Methods with added Context \nAbstract: In this talk\, I will present novel methods of deep learning models with added contexts to deal with the text data for natural language processing tasks. I will present an Informed Machine Learning model for sentiment mining with prior information. I will also show how topic modelling of text data can be improvised by utilising visual information with a deep learning model. \n					Dr Ides Wong\, CSIRO\n											Expand\n					Title: Healthcare\, tech-celerated: using data to power an open and equitable health system. \nAbstract: “Excellence is never an accident. It represents the wise choice of many alternatives”. An excellent healthcare system hinges on using timely and good-quality data to support decision making\, at both the clinician-patient level and across systems level. \nOur pandemic response highlighted the use of data to support timely and high-quality decisions across all levels of individual-behaviour\, clinical operation\, system administration and health and intergovernmental policy. Enabled by data and technology\, health and medical services\, research and industry partners worked together and created a more open and equitable health system. \nThe pandemic has “tech-celerated” the healthcare sector in developing and implementing innovative breakthroughs there were previously considered fantastical. Iterative improvement and innovation are essential to underpin the safe and effective evolution of our healthcare system to meet a rapidly changing healthcare environment. At the CSIRO Australia e-Heath Research Centre\, we undertake research and develop technologies across the full spectrum from genome sequencing to systems-level analytics\, that: 1) transform health systems with data and artificial intelligence\, 2) transform healthcare delivery with virtual care; 3) improve health system efficiency and readiness with digital health; and 4) speed the transition to precision health. \nWith our existing conventions molten by the needs of a global pandemic\, we have a unique opportunity to create a new conversation around the future of human health and incorporate and build upon these ideas. \n					A/Prof Andrew Zammit Mangion\, University of Wollongong\n											Expand\n					Title: Deep learning for facilitating parameter estimation in statistical models \nAbstract: Parameter estimation is often the computational bottleneck in analyses involving intractable statistical models. In the first part of the talk I will show how deep learning models trained to be Bayes estimators can alleviate this computational burden. The trained “neural Bayes estimators” yield optimal parameter estimates from data at a fraction of the computational cost typically associated with parameter estimation. In the second part of the talk I show how permutation-invariant neural networks are ideal for being trained as Bayes estimators when the data are exchangeable. In experiments involving multiple replicates and spatial models of extremes\, I show that these permutation-invariant neural Bayes estimators considerably outperform other neural-network-based estimators that do not account for replication appropriately in their network design\, and that they are highly competitive and much faster than traditional likelihood-based estimators. The work is joint work with Matthew Sainsbury-Dale (University of Wollongong) and Raphael Huser (KAUST). \n				\n				\n				\n				\n				\n				\n	\n\n\n\nDay 1 – Monday 21st November\n\n\n\nTime\nDuration\nItem\n\n\n8:45am\n30 mins\nCoffee/sign In\n\n\n9:15am\n15 mins\nUncle Cheg – Welcome to Country\n\n\n9:30am\n45 mins\nKeynote – Tomasz Bednarz\, NVIDIA\n\n\n10:15am\n45 mins\nWelcome by Kerrie and Meet and Greet\n\n\n11:00am\n30 mins\nBreak\n\n\n11:30am\n30 mins\nInvited talk – Yoni Nazarathy\, The University of Queensland\n\n\n12:00pm\n30 mins\nInvited talk – Astrid Zeman\, Melbourne Centre for Data Science\n\n\n12:30pm\n1 hour\nLunch Break\n\n\n1:30pm\n45 mins\nCommunications Workshop – Tim Macuga\, ADSN\n\n\n2:15pm\n30 mins\nInvited talk – Emi Tanaka\, Monash University Econometrics & Business Statistics\n\n\n2:45pm\n30 mins\nInvited talk – Becki Cook\, QUT Centre for Data Science\n\n\n3:15pm\n30 mins\nBreak\n\n\n3:45pm\n1 hour\nIndustry Panel – “Reimagining Data Science”\n\n\n4:45pm\n15 mins\nClosing\n\n\n5:00pm\n1 hour\nPosters & Networking until 6pm\n\n\n\n	\n\n\n\nDay 2 – Tuesday\, 22nd November\n\n\n\nTime\nDuration\nItem\n\n\n8:30am\n30 mins\nCoffee/sign In\n\n\n9:00am\n15 mins\nKerrie Mengersen Open & Welcome to Country Video\n\n\n9:15am\n45 mins\nKeynote – Richard Fox\, AFL Data & Analytics\n\n\n10:00am\n30 mins\nMeet and Greet Activity\n\n\n10:30am\n30 minutes\nInvited talk – Joanna Batstone\, Monash Data Futures Institute\n\n\n11:00am\n30 mins\nBreak\n\n\n11:30am\n30 mins\nResponse to Science Academy Report on Advancing Data-intensive research\n\n\n12:00pm\n30 mins\nInvited talk – Ides Wong\, CSIRO\n\n\n12:30pm\n1 hour\nLunch Break\n\n\n1:30pm\n75 mins\nADSN Profile – planning\n\n\n2:45pm\n30 mins\nInvited talk – Lewis Mitchell\, University of Adelaide\n\n\n3:15pm\n30 mins\nBreak\n\n\n3:45pm\n30 mins\nInvited talk – Andrew Zammit Mangion\, University of Wollongong\n\n\n4:15pm\n30 mins\nInvited talk – Richi Nayak\, QUT Centre for Data Science\n\n\n4:45pm\n15 mins\nClosing\n\n\n5:00pm\n1 hour\nPosters & Networking until 6pm
URL:https://australiandatascience.net/event/2022-conference/
LOCATION:QUT\, Queensland University of Technology\, Brisbane\, Queensland\, 4000\, Australia
CATEGORIES:Event
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ORGANIZER;CN="Australian Data Science Network":MAILTO:info@australiandatascience.net
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