Bei Chen is a research staff member (statistician) at IBM Research-Ireland. Since joining IBM (2012), she has been working on large scale real-time predictions of transportation and energy systems. Her primary research interests include time series analysis, forecasting, resampling/subsampling for dependent data and statistical learning methods in industrial applications. Prior to IBM, she was an Assistant Professor in the Department of Mathematics and Statistics at McMaster University, Canada (2011-2012) and a visiting member at the Fields Institute for Research in Mathematical Sciences, Toronto, Canada (2012). Bei received her BMath in Statistics and Actuarial Science (2007), MMath (2008) and PhD (2011) in Statistics from the University of Waterloo, Canada. She is the recipient of the 2011 Pierre Robillard Award of the Statistical Society of Canada. Her research was funded by various grants, including NSERC, ECR and MITACS.
Chris Develder is associate professor with the research group IDLab of the Dept. of Information Technology (INTEC) at Ghent University - imec, Ghent, Belgium. He currently leads two research teams within IDLab, one on information retrieval and extraction, the other on data analytics and machine learning for smart grids. His research interests also still include optical networks (dimensioning, modeling, optimization). Chris received the MSc degree in computer science engineering and a PhD in electrical engineering from Ghent University (Ghent, Belgium), in July 1999 and December 2003 respectively (as a fellow of the Research Foundation, FWO). From Jan. 2004 to Aug. 2005, he worked for OPNET Technologies, on optical network design and planning. In September 2005, he re-joined INTEC as a postdoctoral researcher, and as a postdoctoral fellow of the FWO since October 2006 (until 2012). In Oct. 2007 he obtained a part-time, and since Feb. 2010 a fulltime professorship at Ghent University. He has stayed as a research visitor at UC Davis (Jul.-Oct. 2007), CA, USA and at Columbia University, NY, USA (Jan. 2013 - Jun. 2015). He was and is involved in various national and European research projects (e.g., FP7 Geysers, FP7 Increase, FP7 C-DAX). He regularly serves as reviewer/TPC member for international journals and conferences (IEEE Trans. Smart Grid, IEEE/OSA JOCN, IEEE/ACM Trans. Networking; IEEE SmartGridComm, ACM CIKM, etc.). He is Senior Member of IEEE and Member of ACM.
graduated in 1994 from Ecole Polytechnique and Ponts et Chaussées. She is now a
Research Engineer at EDF R&D. She has worked on statistical modeling time series
and applications in electricity market. She co-supervised a thesis on bayesian methods
with Nantes University. Sophie worked as a team manager. Her team was in charge of load
forecasting methods for various EDF topics. She manages now a project dedicated to i
ndividual demand data and statistical methods applied to EDF commercial issues.
Ram Rajagopal is an Assistant Professor of Civil and Environmental Engineering at Stanford University, where he directs the Stanford Sustainable Systems Lab (S3L), focused on large scale monitoring, data analytics and stochastic control for infrastructure networks, in particular power networks. His current research interests in power systems are in integration of renewables, smart distribution systems and demand-side data analytics. Prior to his current position he was a DSP Research Engineer at National Instruments and a Visiting Research Scientist at IBM Research. He holds a Ph.D. in Electrical Engineering and Computer Sciences and an M.A. in Statistics, both from the University of California Berkeley, Masters in Electrical and Computer Engineering from University of Texas, Austin and Bachelors in Electrical Engineering from the Federal University of Rio de Janeiro. He is a recipient of the NSF CAREER Award, Powell Foundation Fellowship, Berkeley Regents Fellowship and the Makhoul Conjecture Challenge award. He holds more than 30 patents and several best paper awards from his work and has advised or founded various companies in the fields of sensor networks, power systems and data analytics.
Prof. Gavin Shaddick is a professor of Data Science and Statistics in the Department of Mathematics at the University of Exeter. He received his master's in applied stochastic systems from University College London and his PhD in statistics and epidemiology from Imperial College London. His research interests include the theory and application of Bayesian hierarchical models and spatio-temporal modelling in a number of fields including epidemiology, environmental modelling, disease progression in rheumatology and the power industry. He is actively engaged in research with the power industry, using big data to model demand profiles, forecasting demands and identifing customer profiles, and the use of data reduction techniques. Of particular interest are computational techniques that allow the implementation of complex statistical models to real-life applications where the scope over both space and time may be very large. He is a co-author of Spatio-Temporal Methods in Environmental Epidemiology and the Oxford Handbook of Epidemiology for Clinicians, which was Highly Commended in the Basis of Medicine Category, BMA Book Awards 2013.
Simon N. Wood works as a Professor of Statistics at the University of Bath and currently holds an established research fellowship from the Engineering and Physical Sciences Research Council. He is author of the widely used R package mgcv for smooth statistical modelling and the book Generalized Additive Models: An Introduction with R, as well as a number of well-cited papers on associated statistical methods. Originally trained in physics, before a spell in theoretical ecology, he has twenty years' experience of teaching statistics at undergraduate and postgraduate level, including teaching the 'statistical computing' module of the UK Academy for PhD training in statistics, for several years.
works as a research associate at the University of Bristol, where his position is
funded by EPSRC and EDF. His current research is concerned with extending Generalized
Additive Models (GAMs), with particular focus on electricity load forecasting applications.
He is the authors of the
qgam R package, which provides fitting methods for quantile
regression GAMs, and he is currently developing, in collaboration with Raphaël Nedellec
at EDF, the
mgcViz R package, which offers new visualization tools for GAMs. He is
also the author of the
esaddle R packages on CRAN. Matteo has
an MEng in Industrial Engineering from the University of Udine (Italy), an MSc in Financial
Engineering from the University of London (Birkbeck College) and a PhD in Statistics from
the University of Bath, where he was supervised by Prof. Simon N. Wood.