SW23 Keynote Speakers

Meet our keynote speakers for Simulation Workshop 2023 (SW23):

Professor Susan Howick

'Mixing methods: reflections for simulation' 

Complex problems often benefit from being modelled by multiple operational research (OR) methods and, for a number of years, there has been increasing interest in the literature on how such methods can be effectively mixed. This includes simulation methods where (i) multiple simulation methods have been effectively combined and (ii) individual simulation methods have been combined with other types of OR methods. 

Although a key focus of the presenter’s research and work with industry has included using system dynamics, much of her work has also focused on combining system dynamics with other methods, including other simulation methods. 

This presentation will reflect on over 25 years of mixing system dynamics with other OR methods and will consider lessons for simulation. This will include topics such as the skills required and client value. 

Biography: 

Susan is a Professor of Management Science and Vice-Dean (Academic) for Strathclyde Business School. Her main research interest lies in taking a systems perspective when using models to support decision-makers, including the use of system dynamics. She has developed approaches to systemic risk evaluation and modelled highly disrupted projects to understand the causes of project failure. She is also interested in exploring approaches to integrating models that promote client value from the modelling process. 

Susan has been Vice-President of the Operational Research Society and President of the Policy Council for the UK Chapter of the System Dynamics Society. Susan is on the Editorial Board of the European Journal of Operational Research and is an Associate Editor of the System Dynamics Review. Research funding has included grants from H2020, EPSRC and NERC. Her consultancy experience includes projects for Bombardier, Strathclyde Police, Reed Elsevier, Pricewaterhouse Coopers, White & Case, Scottish and Southern Energy. 

Susan Howick,
Professor of Management Science and Vice-Dean (Academic)
Strathclyde Business School

Professor Raghu Pasupathy

On constructing confidence regions from generic simulation output 

Modern simulation methods have dramatically increased our ability to analyse and predict the performance of time-dependent systems. For example, we are able to track the expected path of a hurricane's approach, estimate the 90th percentile travel time in response to congestion pricing measures in a city, and calculate the time-dependent heat flow across new materials as the solution to a stochastic PDE. In all of these environments, in addition to estimating a performance measure of interest, the simulationist routinely seeks some measure of uncertainty, e.g., a confidence region. What is more, the requested confidence region is frequently non-classical in the sense that the "system" being analyzed does not exist yet, and the performance measure being sought is not necessarily a mean. 

I will start this talk by asking the question, what is meant by a confidence region on (an infinite-dimensional object such as) the expected path of a hurricane?  And, is there a way to think about the previous question in general, so that the answer applies equally in the context of tracking hurricanes as it does when calculating a simulation-based heat flow solution? Then, standing on the steady progress made within the simulation community over the previous three decades, I will formalize a simple method, known to many simulationists through folklore, to construct confidence regions from generic simulation output. The method I present results in provably valid confidence regions and competes favorably with the famous bootstrap and subsampling techniques from classical statistics. It also provides the true analogues of the chi-square and the Student's t distributions --- fundamental distributions resulting from William Gosset's seminal 1908 paper --- for the simulation context. 

 Biography:   

Raghu Pasupathy is a Professor of Statistics at Purdue University. Prior to joining Purdue in 2014, Raghu spent nine years in the Industrial and Systems Engineering Department at Virginia Tech, first as an Assistant Professor and then as an Associate Professor. Raghu's research interests lie in the theoretical and computational aspects of statistical and simulation-based inference, with a focus on stochastic optimisation problems. Raghu has been associated with the simulation and optimization communities in various capacities over the previous two decades, including serving as President of the INFORMS Simulation Society from 2018 -- 2020, and as an editor for ACM TOMACS, Operations Research, INFORMS Journal on Computing, IISE Transactions, and Mathematical Programming. More information, including downloadable papers and computational software can be obtained through his website at https://web.ics.purdue.edu/~pasupath/ 

Raghu Pasupathy,
Professor of Statistics
Purdue University