Antonietta Mira is professor of statistics, founder and director of the Data Science Lab at USI where she served as the Vice-Dean in the Faculty of Economics (2013-2015). She is also part-time professor of statistics at Università dell'Insubria, is a fellow of the Istituto Lombardo Accademia di Scienze e Lettere, a fellow of the International Society for Bayesian Analysis (ISBA), a visiting fellow of the Isaac Newton Institute for Mathematical Sciences at Cambridge University (2014 and 2016) and has been a visiting professor at Université Paris-Dauphine, University of Western Australia, Queensland University of Technology, Brisbane, and University of Bristol, UK. She has won awards for excellence in both research and teaching. She is the principal investigator on several projects at the Swiss National Science Foundation and a member of multiple scientific committees representing her areas of expertise: Bayesian statistical models and efficient Monte Carlo simulation algorithms and theory. Her current research focuses on data science and methodological and computational statistics, both of which have a clear interdisciplinary scope across life and social science, finance, economic and health. She is often invited to talk at international scientific conferences where she also organizes sessions on topics related to her research interests. She serves on the editorial board of high impact scientific journals such as Statistica Sinica (2005-8), Journal of Computational and Graphical Statistics (2006-8), Bayesian Analysis (2008-16) and as guest editor of special issues (2014-15-16). She has been involved in public engagement (such as EXPO Milano 2015), has delivered public lectures (Festival of the Swiss Academy of Sciences 200 year anniversary 2015; opening lecture of the USI academic year 2011-12; Istituto Lombardo Accademia di Scienze e Lettere, Milano, 2012 and 2016), and is the scientific lead for the exhibit Numbed by Numbers! She is often interviewed in the media on topics related to Data Science and Big Data. Within the Interdisciplinary Institute of Data Science (that she co-founded and co-directed), first, and the Data Science Lab next, she organizes a series of public lectures (Data and Society: Opportunities and Fears, 2015-16) and scientific seminars (Directions in Data Science, 2015-16). Antonietta holds a PhD in Computational Statistics (1998) and a Master’s in Statistics (1996) from the University of Minnesota in Minneapolis, US. She also has a Doctorate in Methodological Statistics from the University of Trento (1995), Italy, and earned her Bachelor’s in Economics, summa cum laude, from the University of Pavia, Italy. Her work has been published in over 50 scientific articles and books.
Emtiyaz Khan (also known as Emti) is a (tenured) team leader at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where he leads the Approximate Bayesian Inference Team. Previously, he was a postdoc and then a scientist at Ecole Polytechnique Fédérale de Lausanne (EPFL), where he also taught two large machine learning courses and received a teaching award. He finished his PhD in machine learning from University of British Columbia in 2012. The main goal of Emti’s research is to understand the principles of learning from data and use them to develop algorithms that can learn like living beings. For more than 10 years, his work has focused on developing Bayesian methods that could lead to such fundamental principles. The approximate Bayesian inference team now continues to use these principles, as well as derive new ones, to solve real-world problems.
I am a statistician with a focus on probabilistic (Bayesian) methods currently working as a Full Professor for Computational Statistics at the Department of Statistics, TU Dortmund University. Having originally studied psychology and mathematics, my core research is nowadays located somewhere between statistics and machine learning, with applications in almost all quantitative sciences.
Ole Maneesoonthorn is an Associate Professor at the Department of Econometrics and Statistics, Monash University (as of January 2023). She previously served as Associate Dean, Research at the Melbourne Business School, the University of Melbourne. Her research are in the fields of time series econometrics, Bayesian econometrics and financial econometrics. Ole has published in top field journals in econometrics, such as the Journal of Econometrics and Journal of Applied Econometrics. She has been recognized on many occasions, including winning the prize for best PhD paper at both the inaugural Peter C.B. Phillip PhD Camp in 2012 (held at the National University of Singapore) and the 2010 Financial Integrated Research Network (FIRN) Doctoral Tutorial; and an honourable mention at the 2013 New Zealand Econometrics Study Group.
Ole received a PhD in Econometrics from Monash University. The work on her thesis earned her the prestigious International Savage Award, bestowed by the International Society of Bayesian Analysis (ISBA) for the most outstanding doctoral dissertations in Bayesian econometrics or statistics, as well as the Mollie Holman Doctoral Medal 2013 from Monash University.
Ole's teaching specialty is in statistics for business, including Data Analysis for the MBA, as well as advanced analytics subjects for the Master of Business Analytics. She has won several teaching awards in the analytics domain.
I am a Lecturer in Data Science and Statistics at the School of Mathematics and Statistics, University of Melbourne in Melbourne, Australia.
Previously, I was an ACEMS Postdoctoral Research Associate at the University of Technology Sydney, and worked under the supervision of Prof. Louise Ryan. I completed my PhD at the University of New South Wales and was supervised by Prof. Robert Kohn, Dr Matias Quiroz and Dr Minh-Ngoc Tran.
My research focuses on Bayesian computations and Bayesian modelling. Currently I am interested in efficient methods for Bayesian inference in settings involving multiple outcomes data.
I prefer to be addressed by KD or Khue Dung.
I am Associate Professor with the School of Mathematics and Applied Statistics, and my research interests lie in spatio-temporal modelling and the tools that enable it. During my PhD at the University of Sheffield, (2008-2011), I focused on variational Bayesian methods for approximate inference of spatio-temporal log-Gaussian Cox process models. The methods I developed were successfully applied in conflict modelling. Following my PhD and a brief postdoc at the University of Edinburgh, I joined the University of Bristol (2012-2014). In my work there I used well-established approximations to spatio-temporal multivariate processes to assess the Antarctic contribution to sea-level rise. The project involved fusing multiple data products (from diverse satellites and research groups) through the use of a large-scale spatio-temporal model. Work involved the use of the message-passing interface on a high-performance computer, parallel Gibbs sampling methods, and sparse linear algebra methods. In my early years at NIASRA (2014-2017), my work focused on developing nonstationary, non-Gaussian, multivariate spatial models and software for spatial modelling. In 2018, I took up a Discovery Early Career Research Award (DECRA) from the Australian Research Council (ARC), to investigate deep learning methods in spatio-temporal statistics, which is my current research focus. As of 2023 I am Chief Investigator on three other projects: a Discovery Project on estimating the sources and sinks of greenhouse gases; an ARC Special Research Initiative on Securing Antartica's Environmental Future (SAEF); and an ARC Industrial Transformation Research Hub on Transforming Energy Infrastructure through Digital Engineering (TIDE), for which I am also Data Science Coordinator.
More speakers will be announced soon!