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*** REGISTRATION ONLY VIA SSA WEBSITE *** To register for this event, sign up through: https://statsoc.org.au/event-3443957 Thinking about the next stage in your career? Want to know how others overcame work challenges? Looking for ideas on where statistics can take you? Would you like to share your own story with others? Come to our members-only mentoring breakfast, a place where statisticians at all career stages and sectors can meet in a friendly and welcoming environment. Our invited mentors for the day come with diverse backgrounds and experience. You will have the opportunity to speak with them as well as your fellow members. The morning will include small group discussions and general networking. Breakfast will be provided. Schedule: 7:45am – Doors open, breakfast is served 8:00am – Welcome and group discussions 9:00am – General networking 9:30am – End Sponsors: Eliiza, Bunnings Mentors: Kohleth Chia Kohleth Chia graduated with an MSc in 2011. Since then, his career has already spanned government, academia and industry, something that very few statisticians can boast even across their whole career! He currently works as a Data Scientist at Bunnings. Sandy Clarke-Errey Dr Sandy Clarke-Errey has been a consultant with the Statistical Consulting Centre at the University of Melbourne since 2004, completing a PhD in Statistics in 2010. She provides statistical advice to researchers at the University and works on industry and government projects, using a wide range of statistical tools and techniques. Andy Kitchen Andy Kitchen is Head of AI Research at CCLabs.ai, and the organiser of the Machine Learning and AI Meetup group in Melbourne. Stephen Leslie A/Prof Stephen Leslie works in the field of mathematical genetics. He obtained his doctorate from University of Oxford and is now based at the University of Melbourne. His work on immune system genetics and understand genetic differences across the British Isles have been highly influential. This year he was awarded the Moran Medal by the Australian Academy of Sciences. Margarita Moreno-Betancur Dr Margarita Moreno-Betancur is a Senior Research Fellow at the University of Melbourne and the Murdoch Children’s Research Institute (MCRI). Her research focuses on methods in causal inference, missing data and survival analysis. She is an emerging leader in biostatistics, with management roles in the Victorian Centre for Biostatistics (ViCBiostat) and the MCRI’s LifeCourse Initiative, comprised of over 40 longitudinal cohort studies. Dennis Trewin Dennis Trewin AO FASSA has led a long and distinguished career in official statistics and is an honorary life member of the SSA. He was the Australian Statistician in[masked] and has also taken other senior roles in Australia, New Zealand and internationally. He has served as President of the International Statistical Institute as well as the SSA. James Wilson James is the CEO of Eliiza, a data science and engineering consultancy specialising in the design and build of Machine Learning systems. He is a host of the AI Australia Podcast, interviewing some of Australia's leading experts in the field of AI. He also co-organises Responsible AI Australia, a group of passionate AI/ML enthusiasts who are committed to ensuring all Australians benefit from the technology. The group raises awareness of AI/ML, its inherent risks, and strategies to mitigate. Registration: This event has limited capacity. Registration costs $10. You need to be an SSA member to register. To register for this event, go to: https://statsoc.org.au/event-3443957
Speaker: Dr Anthony Lee, University of Bristol I will introduce Sequential Monte Carlo (SMC) methodology from a Statistics perspective. This particle-based algorithm was initially proposed to approximate predictive and filtering distributions for general state-space hidden Markov models, and in this context it is also known as a Particle Filter. SMC is now used to approximate a variety of intractable integrals arising in Statistics, e.g. intractable likelihoods in latent variable models and expectations with respect to high-dimensional, complex posteriors. I will cover the basic algorithm and its properties, as well as some innovations that have improved its performance and extended its impact. No previous knowledge of SMC is required. Biography: I am a Computational Statistician in the School of Mathematics at the University of Bristol, and Director of the Data Science at Scale Programme at the Alan Turing Institute, with Intel as Strategic Partner. My research is primarily in the area of stochastic algorithms for approximating intractable quantities that arise in data analysis. Examples of such algorithms are Markov chain and Sequential Monte Carlo. I work on both theory and methodology: research in this area is interdisciplinary, bringing together advances in applied probability, algorithms, and statistics. I am often interested in algorithms that scale well in parallel and distributed computing environments, and in computational and statistical trade-offs when conducting inference.