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DESCRIPTION:Overview\n\nThis series of conferences on topics in applied mat
 hematics began in 2024. Each year\, the CRM welcomes one or two distinguis
 hed speakers. Each edition is devoted to a different theme. The theme for 
 2025 was numerical analysis. The theme for 2026 will focus on the statisti
 cal science of space and time.\n\nJean-François Coeurjolly (Université Gre
 noble Alpes)\n\nBiography: Jean-François Coeurjolly has been a professor a
 t the Jean Kuntzmann Laboratory (LJK) of University Grenoble Alpes\, Franc
 e\, since 2020\, after spending several years in the Department of Mathema
 tics at UQAM (from 2016 to 2020). His research interests focus on the simu
 lation and inference of stochastic processes\, in particular irregular pro
 cesses (fractional and multifractional)\, random fields\, and spatial and 
 spatio-temporal point processes. He is also very interested in multidiscip
 linary collaborations and has applied his work to climate science\, cognit
 ive science\, environmental studies\, economics\, computer graphics\, and 
 experimental design\, among others. He notably leads the Statistics Depart
 ment of LJK as well as the CNRS research network MAIAGES dedicated to imag
 e processing and stochastic geometry.\n\nTuesday\, May 26\, 2026\, 2:00 p.
 m to 3:00 p.m. (UQAM\, room PK-5115)\n\n(Presentation in French\, slides i
 n English)\n\nScientific conference (STATQAM)\n\nTitle: Spatial median for
  a point process and its link with the median of a perturbed Poisson distr
 ibution\n\nThis work was carried out\, among others\, in collaboration wit
 h Christophe Biscio (Aalborg University\, Denmark) and Joëlle Rousseau Tré
 panier (M.Sc. student at UQAM\, Data Scientist)..)\n\nAbstract: Estimating
  the intensity of a stationary point process is usually the first (and sim
 plest) problem considered when analyzing point pattern data. It allows one
  to estimate\, for instance\, the number of trees in a forest or the numbe
 r of lightning strikes over a given territory per unit volume. In this tal
 k\, we begin by showing that this estimator is\, by construction\, highly 
 non-robust in the presence of outliers\, that is\, spatial regions where e
 ither no points or excessive concentrations of points are observed. We the
 n show that a robust version can be developed by defining an analogue of a
  spatial median for a point process. The remainder of the talk investigate
 s the theoretical properties of this new estimator and focuses in particul
 ar on a simple but non-trivial question: the study of the median of a pert
 urbed Poisson distribution.\n\nThursday\, May 28\, 2026\, 2:00 p.m to 3:00
  p.m. (CRM\, room 6214)\n\n(Presentation in French\, slides in English)\n
 \nPublic conference\n\nTitle: The point at the interface between time and 
 space.\n\nAbstract: Point processes or point patterns are datasets used to
  model interacting objects-so-called points. A point may correspond to the
  firing time of a neuron\, the location of a tree in a forest\, a particle
  in an ideal gas\, or the time and location of a lightning strike over a g
 iven territory\, among others. Although such examples have appeared in man
 y classical works\, their use has increased significantly since the 2000s 
 with the rise of georeferenced data acquisition techniques. Through a vari
 ety of examples (including forestry\, economics\, cognitive science\, expe
 rimental design\, genomics\, and climate science) studied in recent years\
 , we will illustrate several methodological challenges and recent contribu
 tions needed to account for the complexity of these applications: strong i
 nhomogeneity\, strong dependence\, and high dimensionality (in terms of da
 ta size and multivariate structure)\, etc.\n\nPeter F. Craigmile (Hunter C
 ollege)\n\nBiography: Peter F. Craigmile is a Professor of Statistics in t
 he Department of Mathematics and Statistics at Hunter College\, City Unive
 rsity of New York. His research interests include time series and longitud
 inal analysis\, spatial statistics\, and spatio-temporal modeling. He work
 s on building scientifically relevant hierarchical statistical models\, ap
 plied to areas such as climate science\, public health\, psychology\, envi
 ronmental health\, and medicine. He has extensive experience in collaborat
 ive\, team-based interdisciplinary research\, working with other statistic
 ians and practitioners on numerous research projects at the local\, nation
 al\, and international levels. Professor Craigmile is a fellow of the Amer
 ican Statistical Association\, the Institute of Mathematical Statistics\, 
 and the Royal Statistical Society.\n\nTuesday\, May 26\, 2026\, 3:30 p.m t
 o 4:30 p.m. (UQAM\, room PK-5115)\n\n(Presentation in English)\n\nScientif
 ic conference\n\nTitle: Modeling Nonstationary Time Series using Locally S
 tationary Basis Processes\n\nPeter F. Craigmile\, Department of Mathematic
 s and Statistics\, Hunter College\, City University of New York. (This is 
 joint research with Shreyan Ganguly.)\n\nAbstract: Methods of estimation a
 nd forecasting for stationary models are well known in classical time seri
 es analysis. However\, stationarity is an idealization which\, in practice
 \, can at best hold as an approximation\, but for many time series may be 
 an unrealistic assumption. We define a class of locally stationary process
 es which can lead to more accurate uncertainty quantification rather than 
 making an invalid assumption of stationarity. This class of processes assu
 mes model parameters to be time-varying and parameterizes them in terms of
  a transformation of basis functions that ensures the processes are locall
 y stationary. We develop methods and theory for parameter estimation in th
 is class of models and propose a test that allows us to examine certain de
 partures from stationarity. We assess our methods using simulation studies
  and apply these techniques to the analysis of an electroencephalogram tim
 e series. We conclude with a discussion of a spatio-temporal extension.\n
 \nThursday\, May 28\, 2026\, 3:30 p.m to 4:30 p.m. (CRM\, room 6214)\n\n(P
 resentation in English)\n\nPublic conference\n\nTitle: Stories in Statisti
 cal Climatology\n\nPeter F. Craigmile\, Department of Mathematics and Stat
 istics\, Hunter College\, City University of New York. (This is joint rese
 arch with Peter Guttorp and Thordis Thorarinsdottir.)\n\nAbstract: Climate
  impacts are real and continue to affect our world. Thus\, the study of cl
 imate is of great interest to the scientific community\, policy makers\, a
 nd the general public. In statistical climatology\, we develop and use sta
 tistical methodologies to investigate how climate processes interact and e
 volve over space and time.\n	Through a selection of statistical stories\, w
 e explore how statisticians contribute to understanding the behavior of th
 e climate system\, using measures such as temperature and precipitation. W
 e also introduce salient features of climate processes and climate data th
 at should be incorporated into statistical models. Finally\, we discuss th
 e role of careful dataset selection and highlight the role that climate mo
 del simulations can play in studying our world.\n
DTSTART;VALUE=DATE:20260426
DTEND;VALUE=DATE:20260428
SUMMARY:Distinguished lectures in applied mathematics and statistics
URL:https://www.mcgill.ca/mathstat/channels/event/distinguished-lectures-ap
 plied-mathematics-and-statistics-372809
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