Seminar Schedule in Google Calendar
4192 Earth Sciences Building (2207 Main Mall)
Thu 28th May 2015
Kwok L Tsui is head and chair professor in the Department of Systems Engineering and Engineering Management
at City University of Hong Kong. Prior to the current position, Dr. Tsui has been professor/associate professor in the School
of Industrial and Systems Engineering at Georgia Institute of Technology in 1990-2011; and member of technical staff in the
Quality Assurance Center at AT&T Bell Labs in 1986-1990. He received his Ph.D. in Statistics from the University of Wisconsin
at Madison. Professor Tsui was a recipient of the National Science Foundation Young Investigator Award. He is Fellow of the American
Statistical Association, American Society for Quality, International Society of Engineering Asset Management, and Hong Kong Institution
of Engineers; and U.S. representative to the ISO Technical Committee on Statistical Methods. Professor Tsui was Chair of the INFORMS
Section on Quality, Statistics, and Reliability and the Founding Chair of the INFORMS Section on Data Mining.
Professor Tsui’s current research interests include data mining, surveillance in healthcare and public health,
prognostics and systems health management, calibration and validation of computer models, process control
and monitoring, and robust design and Taguchi methods.
Evolution of Big Data Analytics
Due to the advancement of computation power and data storage/collection technologies, the field of data modelling and applications have been evolving rapidly over the last two decades, with different buzz words as knowledge discovery in databases (KDD), data mining (DM), business analytics, big data analytic, ... . There are tremendous opportunities in interdisciplinary research and education in data science, system informatics, and big data analytics; as well as in complex systems optimization and management in various industries of finance, healthcare, transportation, and energy, etc. In this talk we will present our views and experience in the evolution of big data analytics, challenges and opportunities, as well as applications in various industries.
Statistics / BRG
Wed 13th May 2015
Dr Shaddick is a Reader in Statistics in the Department of Mathematical Sciences at the University of Bath.
He has a PhD from Imperial College London in statistics and epidemiology and a Masters from
University College London in applied stochastic systems. His research interests include the theory and
application of Bayesian statistics to the areas of spatial epidemiology, environmental health risk and
the modelling of spatio-temporal fields of environmental hazards. He was a co-author of the Oxford Handbook of
Epidemiology for Clinicians which was Highly Commended in the Basis of Medicine Category, BMA Book Awards 2013.
Together with Jim Zidek, he has a recently written a book `Spatio-temporal methods in environmental epidemiology’
which will be published in July 2015.
Smog, smoke and standards: modelling the effects of air pollution in space and time
From the famous London smogs in the 1950s to air quality in megacities today, the potential effects of air pollution are a major concern both in terms of the environment and in relation to human health. In order to support environmental policy and to estimaterisks of environmental hazards on human health study there is a requirement for accurate estimates of exposures that might be experienced by the populations at risk. For epidemiological studies these exposures must be linked to health outcomes but health and exposure data may not match at all locations in space and time. In such cases a direct comparison of exposures and health outcomes is often not possible without an underlying model to align the two in the spatial and temporal domains. In addition, there may be periods of missing data and preferential sampling, where monitoring locations in environmental networks may be located in areas where levels are expected to be high. Biased estimates of exposures may lead to biased estimates of risk. The Bayesian approach provides a natural framework for modelling complexities within exposure data and provides a means of incorporating the results within health models. However, the large amounts of data that can arise from environmental networks mean that inference using MCMC may not be computational feasible. Here we use Integrated Nested Laplace Approximations (INLA) to implement spatio-temporal exposure models and show how the results can be used to reduce the potential biases that may occur when estimating levels of air pollution and the associated risks to human health.