Keynote and Invited Speakers

 Keynote Speakers

Chris Holmes, University of Oxford, Oxford, United Kingdom

Chris Holmes
University of Oxford

Chris Holmes is a Professor of Biostatistics and UK Medical Research Council (MRC) Programme Leader in Statistical Genomics. He holds a joint appointment between the Department of Statistics and the Nuffield Department of Medicine, University of Oxford. He is an Affiliate Member of the Big Data Institute, Li Ka Shing Centre for Health Informatics and Discovery, Oxford, and a faculty fellow of The Alan Turing Institute, London. He serves on the MRC’s Expert Panel in Stratified Medicine. His research interests surround the theory, methods, and applications of statistics to medical research. Particular interests are in Bayesian decision analysis, statistical machine learning, and model misspecification within stratified medicine. 

Louise Ryan, University of Technology, Sydney, Australia

Louise Ryan
University of Technology, Sydney

After completing her undergraduate degree in statistics and mathematics at Macquarie University, Louise Ryan left Australia in 1979 to pursue her PhD in statistics at Harvard University in the United States.  In 1983, Louise took up a postdoctoral fellowship in Biostatistics, jointly between Dana-Farber Cancer Institute and the Harvard School of Public Health.  She was promoted to Assistant Professor in 1985, eventually becoming the Henry Pickering Walcott Professor and Chair of the Department of Biostatistics at Harvard.  Louise returned to Australia in early 2009 to take up the role as Chief of CSIRO’s Division of Mathematics, Informatics and Statistics.  In 2012, she joined UTS as a distinguished professor of statistics in the School of Mathematical Sciences.  Louise is well known for her contributions to the development of statistical methods for cancer and environmental health research.  She is loves the challenge and satisfaction of multi-disciplinary collaboration and is passionate about training the next generation of statistical scientists.    

Natalie Shlomo, University of Manchester, Manchester, United Kingdom

Natalie Shlomo
University of Manchester

Natalie Shlomo is Professor of Social Statistics at the School of Social Sciences, University of Manchester. Prior to that she was on faculty at the University of Southampton and a methodologist at the Israel Central Bureau of Statistics.  She is a survey statistician with interests in survey design and estimation, record linkage, statistical disclosure control, statistical data editing and imputation and small area estimation.  Natalie is an elected member of the International Statistical Institute and currently serving as Vice President. She is also a  fellow of the Royal Statistical Society and the International Association of Survey Statisticians.  She is the methodology editor of the Journal of the International Association of Official Statistics and an associate editor of several journals including the International Statistical Review and the Journal of the Royal Statistical Society, Series A.   She is a member of several national and international methodology advisory boards. 


Susan Murphy, Harvard University, Boston, USA

Susan Murphy
Harvard University

Susan A. Murphy is Professor of Statistics, Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences and Radcliffe Alumnae Professor at the Radcliffe Institute at Harvard University. Her lab focuses on improving sequential, individualized, decision making in health, in particular on clinical trial design and data analysis to inform the development of personalized just-in-time adaptive interventions in mobile health. Her work is funded by the National Institutes of Health, USA.    

Susan is a Fellow of the Institute of Mathematical Statistics, a Fellow of the College on Problems in Drug Dependence, a former editor of the Annals of Statistics, a member of the US National Academy of Sciences, a member of the US National Academy of Medicine and a 2013 MacArthur Fellow.

Thomas Lumley, University of Auckland, Auckland, New Zealand

Thomas Lumley
University of Auckland

Thomas Lumley is Professor of Biostatistics at the University of Auckland, and Affiliate Professor of Biostatistics at the University of Washington.  His research covers a wide range of topics in biostatistics, including genomics, the design and analysis of complex epidemiological studies, meta-analysis, and statistical computing and graphics.  He writes about statistics in the media at

Invited Speakers

Selected invited speakers, from a total of more than 30 who have agreed to participate in Invited Sessions

Alexei Drummond, University of Auckland, Auckland, New Zealand

Alexei Drummond
University of Auckland

Alexei Drummond is a Professor of Computational Biology in the Department of Computer Science at the University of Auckland and Director of the Centre for Computational Evolution – a centre that develops software tools and mathematical models for understanding evolution and molecular ecology. Alexei works on probabilistic models for phylogenetics, population genetics and molecular evolution. His team has developed software that has become the leading tool for investigating how viruses evolve, and for addressing questions about species evolution. Their software is used daily all over the world to study everything from infectious disease outbreaks to conservation biology and cultural evolution. ​

Augustine Kong, Oxford University Big Data Institute, Oxford, United Kingdom

Augustine Kong
Oxford University Big Data Institute

Dr Kong received his Bachelor degree from Caltech and PhD degree from Harvard University. He became a tenured professor in Statistics at The University of Chicago in 1994. He started working in Iceland in 1996 when deCODE Genetics was founded, leading the Statistics group. Last July, he joined the Big Data Institute at Oxford University as Professor of Statistical Genetics. He is on the list of highly cited researchers (top 1%) tabulated by Thomson and Reuters (now Clarivate Analytics), and in the top 10 among all scientists in 2010. His most recent publication is on the genetic component of nurture (Science 359, 2018). The results have serious implications for many areas of quantitative genetics including the Nature versus Nurture debate.

Chris Wild, University of Auckland, New Zealand

Chris Wild
University of Auckland, New Zealand

Chris Wild’s main interests have been in methods for response-selective data and missing data problems, and in statistics education with particular emphasis on statistical thinking and reasoning processes, data visualisation and concept visualisation. After a PhD from the University of Waterloo in Canada he joined Auckland’s then Department of Mathematics in 1979. An elected Fellow of the American Statistical Association and the Royal Society of New Zealand, and a former President of the International Association for Statistics Education, he was Head of Auckland’s Statistics Department from 2003-2007 and co-led its first-year teaching team to a national Tertiary Teaching Excellence Award.

Deborah Nolan, University of California, Berkeley, USA

Deborah Nolan
University of California

Deborah Nolan is Professor and Chair of Statistics at the University of California, Berkeley, where she also holds the Zaffaroni Family Chair in Undergraduate Education. Her work in statistics education focusses on teaching statistical and computational thinking in real world contexts, and she is co-author of the books Stat Labs: Mathematical theory through application (with T. Speed), Teaching Statistics: A bag of tricks (with A. Gelman), and Data Science in R: A case studies approach to computational reasoning and problem solving (with D. Temple Lang). 

David Frazier, Monash University, Australia

David Frazier
Monash University, Australia

After graduating from the University of North Carolina at Chapel Hill in 2014, David joined the Department of Econometrics and Business Statistics at Monash in July of that year. David’s current research focuses on the development of theoretically sound and robust statistical inference methods for computationally intractable models. Much of David’s recent research has focused on approximate Bayesian approaches, such as approximate Bayesian computation, indirect inference and variational Bayes. David’s current research into approximate Bayesian methods is supported by Australian Research Council discovery Grant DP170100729, titled “The Validation of Approximate Bayesian Computation: Theory and Practice”. 

Eric Laber, North Carolina State University, Raleigh, USA

Eric Laber
North Carolina State University

Eric Laber is Associate Professor and Faculty Scholar in the Department of Statistics and Director of Research Translation and Engagement in the College of Sciences at North Carolina State University.   His major research areas are causal inference, non-regular asymptotics, optimization, and reinforcement learning. His primary application areas include precision medicine, artificial intelligence, adaptive conservation, and the management of infectious diseases.  

Francis Hui, Australian National University

Francis Hui
Australian National University

Francis is a lecturer in statistics at the Mathematical Sciences Institute, ANU, in Canberra, Australia. After completing his PhD at UNSW Sydney in 2014, researching various model-based approaches for community ecology, he took up a postdoctoral fellowship at the ANU and has set up camp there since then. He enjoys watching anime, taking part in trivia events, and occassionally doing some statistics, all while drinking copious amounts of tea. His current statistical interests include mixed models, model-based dimension reduction, variable selection, longitudinal data, and semiparametric regression, all strongly motivated by ecological and public health applications.

Harald Binder, Institute for Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany

Harald Binder
Institute for Medical Biometry and Statistics, University of Freiburg

Harald Binder is a Professor of Medical Biometry and Statistics and heads the Institute of Medical Biometry and Statistics, Medial Center — University of Freiburg. He studied Psychology and Mathematical Behavioral Sciences at Regensburg and UC Irvine. After a PhD from the Department of Statistics at Ludwigs-Maximilians-University Munich, he became a postdoc in Freiburg, and later head of the Division Biostatistics and Bioinformatics, University Medical Center Mainz, before moving to his present position. He focuses on integrative statistical modeling of molecular measurements with clinical characteristics using machine learning, and in particular deep learning.

Hsin-Cheng Huang, Academia Sinica, Taipei City, Taiwan

Hsin-Cheng Huang
Academia Sinica, Taipei City, Taiwan

Hsin-Cheng Huang is a Research Fellow in Institute of Statistical Science, Academia Sinica, Taiwan. He graduated from National Taiwan University in 1989 with a BS degree in mathematics and received his MS and PhD degrees from Iowa State University in 1994 and 1997. He has been in Academia Sinica since 1997. His main research interests include spatial statistics, spatio-temporal modeling of environmental processes, and model selection.

Jeff Miller, Harvard University, Boston, USA

Jeff Miller
Harvard University

Jeff Miller is an Assistant Professor of Biostatistics at the Harvard T.H. Chan School of Public Health.  He received his PhD in Applied Mathematics from Brown University in 2014, where he was awarded the Brown University Outstanding Dissertation Award in the Physical Sciences.  Jeff’s research focuses on flexible Bayesian models, robustness to model misspecification, and efficient algorithms for inference in complex models.  He is currently working on methods for cancer phylogenetic inference and using RNA-seq data to study the molecular mechanisms of aging.

Karla Hemming, University of Birmingham, United Kingdom

Karla Hemming 
University of Birmingham, United Kingdom

Karla Hemming is senior lecturer in biostatistics at the Institute of Applied Health Research, University of Birmingham, UK. Her research interests are in cluster randomised trials, particularly the stepped-wedge design. Karla’s research interests include how to design cluster and stepped-wedge trials so as to maximise their statistical efficiency; how to model time and treatment effect heterogeneity in longitudinal cluster trials; and the ethical issues surrounding these pragmatic trial designs, such as ethical oversight and consent. Karla has recently led the CONSORT extension for the stepped-wedge cluster randomised trial.

Michal Abrahamowicz, McGill University, Montréal, Canada

Michal Abrahamowicz
McGill University

Dr. Michal Abrahamowicz is a James McGill Professor of Biostatistics at McGill University, in Montreal, Canada. His statistical research aims at the development and validation of new, flexible statistical methods for time-to-event (survival) analysis, including non-linear, time-dependent, and cumulative effects of prognostic/risk factors. He has also developed new methods to control for different sources of bias in observational studies. His collaborative research includes pharmaco-epidemiology, arthritis, cardiovascular, and cancer epidemiology. He is a co-chair of the international STRATOS initiative for strengthening the analysis of observational studies. In 2010-14 he was a member of the Executive Committee of ISCB. 

Natalia Bochkina, University of Edinburgh, Edinburgh, Scotland

Natalia Bochkina
University of Edinburgh, Scotland

Natalia Bochkina is a Lecturer in Statistics at the School of Mathematics of the University of Edinburgh and a faculty fellow of the Alan Turing Institute, London, UK. Previously she has been a Postdoctoral Fellow at the Biostatistics group at the Imperial College London and a biostatistician at Oxford GlycoSciences (UK) Ltd. Her research interests lie mainly in robust Bayesian statistics and statistical analysis of high throughput genomic data. She is a member of International Society for Bayesian Analysis, currently serving as a member of the Board, of the Royal Statistical Society (UK) and of the Institute of Mathematical Statistics.

Per Kragh Andersen, Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark

Per Kragh Andersen
Section of Biostatistics, University of Copenhagen

Per Kragh Andersen was born in 1952 and obtained a PhD degree in mathematical statistics from University of Copenhagen in 1982 and a degree of DMSc in 1997. He has been employed at the Section of Biostatistics (former Statistical Research Unit), University of Copenhagen since 1978. His main research interests are in survival analysis and analysis of epidemiological cohort studies. He has co-authored more than 100 scientific articles about statistical methodology and more than 200 applied articles – mainly in the medical/epidemiological literature. He was one of the four authors of the 1993 Springer book ‘Statistical Models Based on Counting Processes’.


Richard Hooper, Queen Mary University of London, United Kingdom

Richard Hooper
Queen Mary University of London, United Kingdom

Richard studied mathematics and then mathematical statistics at the University of Cambridge UK, before beginning a career as a medical statistician which has spanned more than 25 years, first at Cambridge, and later at King’s College London, Imperial College London, and Queen Mary University of London (QMUL). It was his move to QMUL in 2010, where he is a senior statistician at the Pragmatic Clinical Trials Unit, which gave him an introduction to the world of clinical trials and kick-started an interest in innovative trial design which has spawned fruitful collaborations in stepped wedge trials and other areas.

Stephan Huckemann, University of Göttingen, Göttingen, Germany

Stephan Huckemann
University of Göttingen

Stephan Huckemann is a Professor of Non-Euclidean Statistics at the Institute for Mathematical Stochastics and the Felix-Bernstein-Institute for Mathematical Statistics in the Biosciences at the University of Göttingen in Germany.
His theoretical research interests concentrate on interaction between topology and geometry of data spaces, on the one side, and statistical descriptors and their asymptotic limiting behavior, on the other side. On the applied side his research surrounds fingerprint analysis, biomolecular structure analysis, adult stem cell differentiation, biomedical imaging and biomechanics.

Stijn Vansteelandt, University of Ghent, Belgium

Stijn Vansteelandt
University of Ghent, Belgium

Stijn Vansteelandt is Professor of Statistics at Ghent University and Professor of Statistical Methodology at the London School of Hygiene and Tropical Medicine. He has authored over 140 peer-reviewed publications in international journals on a variety of topics in biostatistics, epidemiology and medicine, primarily related to causal inference (mediation and moderation/interaction, instrumental variables, time-varying confounding), as well as the analysis of longitudinal and clustered data, missing data, family-based genetic association studies, analysis of outcome-dependent samples and phylogenetic inference. He is currently co-Editor of Biometrics, the flagship journal of the International Biometric Society.

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