- Unlocking the secrets in your DNA using Machine learning and Cloud-computing
5:30 pm Welcome networking with refreshments 6:00 pm Presentation followed by Q&A Genomic produces more data than Astronomy, twitter, and YouTube combined, having caused research in this discipline to leapfrog to the forefront of cloud technology. Dr. Denis Bauer provides an insider’s view into the development of a Spark-based machine learning framework that is able to find disease genes in the 3 billion letters of the genome. She will also cover serverless, which is pitted to become a $8 Billion market for its ability to accelerate software development, akin to how pre-fabrication has sped up the construction sector over bricklaying. Her serverless “search engine for the genome” enables researchers to use genome engineering for next-generation medicines. Dr Denis Bauer is head of cloud computing, Bioinformatics, at Australia’s government research agency. She is an internationally recognised expert in machine learning and cloud-based genomics, having presented at AWS Summit, Canberra, 2018 and Open data science conference, India, 2018. Her achievements include developing open-source machine-learning cloud services that accelerate disease research, which is used by 10,000 researchers annually. https://www.linkedin.com/in/denisbauer/ This is a joint event between Data Science Melbourne and YOW! Attendance is strictly limited, so only RSVP if you plan on attending.
- Statistics with industry: demonstrating impact
Note: this event is being organised by the Statistical Society meetup, so keep an eye on their page to confirm final details: https://www.meetup.com/Statistical-Society-of-Australia-Victorian-Branch/events/259316065/ Statistics with industry: demonstrating impact Companies are increasingly aware of the power of data and have read glowing success stories of transformation and added value through data science. They want to learn more and many are turning to Universities for support. Universities in the UK are keen to engage with business and industry because of the valuable research opportunities, and also because they need to show societal impact as part of their submissions to periodic Research Excellence Framework assessments. The Industrial Statistics Research Unit was set up to help companies take advantage of statistical thinking to improve the way they function. Interaction with Universities is valuable to companies and in return provides the University with case studies for our teaching and evidence of our research impact. In this talk I will describe how we engage with companies through knowledge transfer partnerships and will outline a successful impact case study from REF 2014 that arose from our collaborative statistical research. The talk will be illustrated by case studies from our current partnerships with small to medium enterprises (SMEs). Shirley Coleman, Technical Director of Industrial Statistics Research Unit, Newcastle University, UK Shirley Coleman, PhD is Principal Statistician and Technical Director of the Industrial Statistics Research Unit, Newcastle University and a visiting scholar at the Faculty of Economics, Ljubljana University, Slovenia. She works on data analytics in SMEs and the energy sector and contributed a highly ranked impact case study to Newcastle University’s 2014 Research Excellence Framework. She is a past President of the European Network for Business and Industrial Statistics (ENBIS), an elected member of the International Statistics Institute and a Chartered Statistician of the Royal Statistical Society, instrumental in mentoring early career statisticians and developing relations with business and industry. She is an active writer, reviewer and conference organiser interested in helping to address the challenges of embedding statistical thinking in the wider community.
- Data Visualisation: Learning through critique
For our next event we are lucky to have Cole Nussbaumer Knaflic talk to us about communicating effectively with data. This is a special event where we'll be teaming up up with the Melbourne Data Visualisation Meetup. *Data Visualisation: learning through critique* Less-than-ideal graphs are everywhere. It’s remarkably easy to point out what is “wrong” with other people’s data visualisations. But much can be gained from learning how to constructively discuss and critique graphs and the pages that contain them—both for giving feedback as well as honing your own data visualisation skills. Join us in this engaging, highly interactive session with author of bestselling book, storytelling with data, Cole Nussbaumer Knaflic, where you’ll practice applying core principles for communicating effectively with data, learning through critique! About Cole: Cole Nussbaumer Knaflic tells stories with data. She is the author of bestseller storytelling with data: a data visualisation guide for business professionals, writes the popular blog storytellingwithdata.com and is the voice behind the storytelling with data podcast. Cole’s unique talent was honed over the past decade through analytical roles in banking, private equity, and on Google’s People Analytics team. Her well-regarded workshops and presentations are highly sought after by data-minded individuals, companies, and philanthropic organisations all over the world.
- Europe’s new Digital Market and Data Rules - why should Australia care?
RMIT University - Building 80
5:35 - pizza 5:55 (ish) - talks start Europe is completing its new Digital Single Market designed to ensure fair and seamless access to online activities for individuals and businesses. The new EU regulation is broad - from consumer rights, to data protection and competition. Does this mean there is a new third internet emerging – along the US internet where ‘anything goes’ and the Chinese internet with its strict control? This talk and panel discussions evaluates the global impact of the Digital Single Market. It aims to clarify what the new Digital Single Market means for Australia. Is it just a new set of rules to follow or perhaps even a business and innovation opportunity? Speakers: Erich Prem, CEO at eutema GmbH & EPIC coordinator https://www.linkedin.com/in/erichprem/ https://epicproject.eu/index.php Maarten de Rijke , Professor of Artificial Intelligence and Information Retrieval at the University of Amsterdam https://www.linkedin.com/in/maartenderijke/ Nicholas Nicoloudis, Senior Business Innovation Strategist at SAP https://www.linkedin.com/in/nicholasnicoloudis/
- Building and Deploying Machine Learning Solutions
Welcome to our final event of 2018. Note: Inspire9 is a co-working space until 6pm so please be mindful of this if you arrive early. Help setting up the chairs and packing up at the end would be appreciated. If you see the pizzas being delivered give them a hand! 6:00pm - please arrive and help set up the chairs 6:10pm - grab some pizza & drinks from the fridge 6:30pm - talk 1 7:30pm - break 7:45pm - talk 2 8:45pm - finish off the drinks and help put things away Talk 1. Application nudges @ SEEK Rebecca Dridan, Oliver Mannion, Kendra Vant Wouldn’t it be great if you could get an idea of whether you were to be a good fit for that job you’re browsing on SEEK? We’re excited to share with you both the machine learning effort and the engineering smarts behind a new ML product currently in beta release across SEEK AU & NZ that will tell you just that! Learn about the ensemble of algorithms we built, the joys(!) of making LightGBM and Tensorflow play nice at scale and the lessons we’ve learned in how to version a complex pipeline, split the computational effort for maximum speed at inference, backfill and hot switch a new model and so much more. Talk 2. Big data stories from the front line - when ML surrenders to optimisation. Evan Shellshear In this talk we'll be presenting front line case studies of how to tackle the biggest challenge in data science - deploying machine learning solutions. As new data flows in, machine learning performance typically degrades but there are tricks to get around this and we'll show how we've used optimisation techniques to beat this and stop performance degradation. Learn how one of the worst predictors around paired with a good optimisation algorithm beat the best predictive analytics tools available. Dr Evan Shellshear is the Head of Analytics at Biarri and has a career spanning the globe. He began his PhD in Germany at Bielefeld and then moved up to Sweden to work at the Fraunhofer Institute on HPC and optimisation. He moved back to Australia in 2015 and worked in many startups in the digital space focusing on data engineering and data analytics before his recent move to Biarri.
- How Statisticians and Data Scientists could learn from each other
KPMG Collins Square Tower Two Level 36
Save the date for a special event. We'll be hearing from Xavier Conort, chief data scientist at Data Robot plus other speakers to be announced shortly. This event is a KPMG 'Diversity in Data' event and you should sign up at this link.... https://www.eventbrite.com.au/e/diversity-in-data-tickets-50893648272 Xavier Conort http://www.labourbeat.org/2018/09/20/what-does-a-data-scientist-do/ Data Scientists have been highly successful at automating modeling through Machine learning, and continue to build capabilities to extract powerful insights at an impressive pace. On the other hand, Statisticians have been attempting to manually build complex and robust models with features from Generalized Linear Models (GLMs), such as p-values, exponential distributions, link functions, offsets and mixed models. These GLMs functions are little-known by Data Scientists while Statisticians may dismiss Machine Learning tools that they find too complex, calling them “black boxes”. Are Statisticians missing something here that could present important opportunities to help them find patterns and build solutions for the increasingly larger and more complex ranges of data? This presentation will show that Statisticians and Data Scientists can complement, and learn important practices from each other. The Xgboost package, one of the most popular open source projects, is a good example of such collaboration.
- In the Office with Medibank
Please note in order to give the opportunity for a greater variety of members to attend, this event is at 3pm-5-5:15pm. Please only RSVP if you can make this time slot as places are strictly limited. We will also be checking people in for building security purposes, so we will know who the no shows are. There will be 3 x 25 minute talks starting at Talks 3:00 with refreshments and networking from 4:30 - 5:15. Natalie Kelly https://www.linkedin.com/in/natalie-kelly-gaicd-093b8042/ Analytics adding business value In a world of big data, predictive analytics, AI and more, how do you ensure that analytics simply adds value to a business. Analysts need to work with the business to define a problem, generate insights, create actionable recommendations and clearly communicate their findings to stakeholders. This approach will help unlock the potential in data and analytics and ensure that they remain front and centre in all decision making in a business. Sandeep Reddy https://www.linkedin.com/in/sandeepreddy/ Artificial Intelligence enabled healthcare delivery In recent years there has been massive progress in Artificial Intelligence (AI) with the development of Deep Neural Networks, Natural Language Processing, Computer Vision and Robotics. These techniques are now actively being applied in healthcare with many of the health service activities currently being delivered by clinicians and administrators predicted to be taken over by AI in the coming years. However, there has also been exceptional hype about the abilities of AI with a mistaken notion that AI will replace human clinicians altogether. These perspectives are inaccurate and if a balanced perspective of the limitations and promise of AI is taken, one can gauge which parts of the health system AI can be integrated to make a meaningful impact. The four main areas where AI would have the most influence would be: patient administration, clinical decision support, patient monitoring and healthcare interventions. This health system where AI plays a central role could be termed an AI enabled, or AI augmented health system. In this talk, I will discuss how this system can be developed based on a realistic assessment of current AI technologies and predicted developments. James Bailey https://www.linkedin.com/in/james-bailey-603b49/ Delivering feedback for performance improvement: A case study on self-guided surgical training using machine learning Feedback technology is a critical requirement in education, to improve the performance of students and trainees. The rise of environments such as digital simulation provides a rich environment for collecting data, monitoring and providing feedback. In this talk, we will overview some of our recent work in this area for training surgeons in a cochlear implant procedure, which is based on the use of machine learning algorithms.
- Di Cook: Human vs computer: when visualising data, who wins?
This is an event that rum by the Statistical Society of Australia that may be of interest. For up to date info see the link below. https://www.meetup.com/Statistical-Society-of-Australia-Victorian-Branch/events/253960343/ We look forward to seeing you at the 2018 Belz Lecture, this year presented by Professor Di Cook. Please note that this year the Belz lecture is occurring one week earlier than usual. Please arrive at 5:45pm for a 6pm start. The lecture will be followed by a gala dinner at University House, starting at 7:45pm. Please book your spot at the dinner here: https://www.trybooking.com/YGOX Human vs computer: when visualising data, who wins? Professor Di Cook, EBS, Monash University Can computers relieve data analysts of the arduous task of graphically diagnosing models? Computer vision has come a long way in recent years. It primarily addresses reading and analysing images, and the models have advanced to the state where they can be used to automatically inspect quality of items emerging along production lines, identifying objects in photos, and even navigating an autonomous vehicle. Despite the fact that visualisation plays a weighty role in data analysis, for both exploration and model diagnosis, the use of, and interpretation of graphics by data scientists/statisticians is subjective. Analysts rely almost entirely on their own judgement, years of experience, and an implicit calculation of uncertainty, when interpreting graphics. Considering data plots as a type of statistic, allows data analysts to move away from subjectivity, towards an inferential approach to reading data plots. Defining data plots as statistics is made explicit by the tidyverse and the grammar of graphics, and in conjunction with a null generating mechanism, data plots can be measured against null plots. In this visual inference context, we are also better posed to build computer vision models to automatically read data plots. The null generating mechanism provides the framework to create a large volume of null plots upon which to train a computer vision model. This talk will discuss these ideas, along with our results from comparing the results from a database of human evaluated residual plots, with the performance of a computer vision model for the task. Who do you think wins? This is joint work with Shuofan Zhang, and builds on joint work with Heike Hofmann, Mahbub Majumder, Andreas Buja, Hadley Wickham, Deborah Swayne, Eun-kyung Lee, Niladri Roy Chowdhury, Lendie Follett, Susan Vanderplas, Adam Loy, Yifan Zhao, Nathaniel Tomasetti. Di Cook is Professor of Business Analytics, in the Department of Econometrics and Business Statistics, at Monash University. She is a Fellow of the American Statistical Association, elected Ordinary Member of the R Foundation, and past editor of the Journal of Computational and Graphical Statistics. She received her Statistics PhD from Rutgers University, NJ, on research in interactive graphics for high-dimensional data, and an undergraduate Bachelor of Science from the University of New England. Effectively plotting data motivates her research in many different directions, from high-dimensional spaces to bridging the gap between confirmatory and exploratory statistics, and experimenting with new technology, like virtual reality and eye-trackers.