Campuses:

Talks

Date Title / Speaker Eventsort ascending
October 23, 2018 On the effectiveness of joint inversion
Jodi Mead (Boise State University)
Recent Advances in Machine Learning and Computational Methods for Geoscience
October 26, 2018 Generalization and regularization in deep learning for nonlinear inverse problems
Maarten De Hoop (Rice University)
Recent Advances in Machine Learning and Computational Methods for Geoscience
October 23, 2018 Machine Learning in Imaging and Inverse Problems: Where is it Going?
Charles Bouman (Purdue University)
Recent Advances in Machine Learning and Computational Methods for Geoscience
October 24, 2018 Indomitable segmentation and pooling: A new perspective on clairvoyant fusion
Alan Schaum (Naval Research Laboratory)
Recent Advances in Machine Learning and Computational Methods for Geoscience
October 24, 2018 Learning generative models of monostatic and bistatic synthetic aperture radar imagery
Emre Ertin (The Ohio State University)
Recent Advances in Machine Learning and Computational Methods for Geoscience
October 24, 2018 Space-borne SAR interferometry: Three Decades of Innovation and Problem Solving
Manoochehr Shirzaei (Arizona State University)
Recent Advances in Machine Learning and Computational Methods for Geoscience
October 24, 2018 GEO-AI - the birth of a new discipline
Eldad Haber (University of British Columbia)
Recent Advances in Machine Learning and Computational Methods for Geoscience
October 25, 2018 Advances in Supervised and Semi-Supervised Machine Learning for Image Analysis of Multi-Modal Geospatial Imagery Data
Saurabh Prasad (University of Houston)
Recent Advances in Machine Learning and Computational Methods for Geoscience
October 23, 2018 Impact of geological stress on flow and transport in fractured media
Peter Kang (University of Minnesota, Twin Cities)
Recent Advances in Machine Learning and Computational Methods for Geoscience
October 24, 2018 Machine Learning for Background Estimation in Multispectral Imagery
James Theiler (Los Alamos National Laboratory)
Recent Advances in Machine Learning and Computational Methods for Geoscience
October 22, 2018 Overview of today’s joint inversion methodology and its future into the machine-learning era for geophysical imaging
Monica Maceira (Oak Ridge National Laboratory)
Recent Advances in Machine Learning and Computational Methods for Geoscience
October 25, 2018 Regularization and Compression via Tensor Dictionaries
Misha Kilmer (Tufts University)
Recent Advances in Machine Learning and Computational Methods for Geoscience
October 22, 2018 Fast and Accurate Maximum-Likelihood Estimation of Parameterized Spectral Densities that Jointly Characterize Bivariate Two-Dimensional Random Fields
Frederik Simons (Princeton University)
Recent Advances in Machine Learning and Computational Methods for Geoscience
October 23, 2018 Dictionary learning and graph signal processing on a 5200 element seismic array
Peter Gerstoft (University of California, San Diego)
Recent Advances in Machine Learning and Computational Methods for Geoscience
October 22, 2018 Hierarchical deep generative networks for Bayesian inverse problems
Pengchuan Zhang (Microsoft Research)
Recent Advances in Machine Learning and Computational Methods for Geoscience
October 25, 2018 Novel Machine Learning Methods for Extraction of Features Characterizing Complex Datasets and Models
Velimir Vesselinov (Los Alamos National Laboratory)
Recent Advances in Machine Learning and Computational Methods for Geoscience
October 22, 2018 Geometric separation and applications to hyperspectral image analysis
Demetrio Labate (University of Houston)
Recent Advances in Machine Learning and Computational Methods for Geoscience
March 29, 2018 Macroscale implications of optical conductivity: Dispersion by homogenization and curvature renormalization
Dionisios Margetis (University of Maryland)
Workshop on Theory and Computation for Transport Properties in 2D Materials
March 26, 2018 Solid-state physics at the ballistic-to-hydrodynamic crossover. 1) Introduction
Andrew Lucas (Stanford University)
Workshop on Theory and Computation for Transport Properties in 2D Materials
March 26, 2018 Nonlinear optics and hydrodynamics of graphene
Michael Fogler (University of California, San Diego)
Workshop on Theory and Computation for Transport Properties in 2D Materials
March 26, 2018 Boltzmann-Langevin theory: Applications to Coulomb drag
Alex Levchenko (University of Wisconsin, Madison)
Workshop on Theory and Computation for Transport Properties in 2D Materials
March 27, 2018 Hydrodynamic approach to interacting electrons
Boris Narozhny (Karlsruhe Institute of Technology)
Workshop on Theory and Computation for Transport Properties in 2D Materials
March 27, 2018 Solid-state physics at the ballistic-to-hydrodynamic crossover. 2) Open problems
Andrew Lucas (Stanford University)
Workshop on Theory and Computation for Transport Properties in 2D Materials
March 28, 2018 Charge transport in crystals at finite-temperature
Emil Prodan (Yeshiva University)
Workshop on Theory and Computation for Transport Properties in 2D Materials
March 28, 2018 Chiral response of twisted bilayer graphene
Tobias Stauber (Consejo Superior de Investigaciones Científicas (CSIC))
Workshop on Theory and Computation for Transport Properties in 2D Materials
March 29, 2018 Chiral plasmon in gapped Dirac systems
Tony Low (University of Minnesota, Twin Cities)
Workshop on Theory and Computation for Transport Properties in 2D Materials
March 29, 2018 Modeling and simulation of incommensurate 2D materials
Eric Cances (École Nationale des Ponts-et-Chaussées (ENPC))
Workshop on Theory and Computation for Transport Properties in 2D Materials
April 24, 2018 Spatiotemporal modelling of ocean surface velocities from drifters
Adam Sykulski (University of Lancaster)
Forecasting from Complexity
April 27, 2018 Optimal Dimension Reduction for Vector and Functional Time Series
Marc Hallin (Université Libre de Bruxelles)
Forecasting from Complexity
April 24, 2018 Network Estimation from Point Process Data
Rebecca Willett (University of Wisconsin, Madison)
Forecasting from Complexity
April 27, 2018 Regularized Estimation in High-dimensional Time Series Models
Sumanta Basu (Cornell University)
Forecasting from Complexity
April 24, 2018 Robust maximum association estimators
Peter Filzmoser (Technische Universität Wien)
Forecasting from Complexity
April 26, 2018 Half-spectral space-time models
Joe Guinness (North Carolina State University)
Forecasting from Complexity
April 25, 2018 Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of The US Banking Sector
Alain Hecq (Universiteit Maastricht (Rijksuniversiteit Limburg))
Forecasting from Complexity
April 27, 2018 Relevant parameter changes in structural break models
Arnaud Dufays (Laval University)
Forecasting from Complexity
April 24, 2018 Sequential change-point detection for Hawkes processes over networks
Yao Xie (Georgia Institute of Technology)
Forecasting from Complexity
April 23, 2018 Complexity vs Parsimony: The Challenge of Building Effective Nonlinear Dynamical Spatio-Temporal Forecasting Model
Chris Wikle (University of Missouri)
Forecasting from Complexity
April 25, 2018 Verification within Complexity: Comparing Spatial Fields
Eric Gilleland (National Center for Atmospheric Research)
Forecasting from Complexity
April 23, 2018 Fast spike train inference via l0 optimization
Daniela Witten (University of Washington)
Forecasting from Complexity
April 25, 2018 Deep Spatial Learning for Forensic Geolocation with Microbiome Data
Brian Reich (North Carolina State University)
Forecasting from Complexity
April 23, 2018 The local partial autocorrelation function and its use in forecasting
Rebecca Killick (University of Lancaster)
Forecasting from Complexity
April 25, 2018 Nonparametric inference for Hawkes processes. Applications for estimating functional connectivity graphs of neurons.
Vincent Rivoirard (Université Paris-Dauphine)
Forecasting from Complexity
April 23, 2018 High-dimensional Linear Regression for Dependent Observations with Application to Nowcasting
Ruey Tsay (University of Chicago)
Forecasting from Complexity
April 25, 2018 Dynamic Filtering of Time-Varying Sparse Signals
Christopher Rozell (Georgia Institute of Technology)
Forecasting from Complexity
April 23, 2018 Sparse Identification and Estimation of High-Dimensional Vector AutoRegressive Moving Averages
Ines Wilms (Katholieke Universiteit Leuven)
Forecasting from Complexity
April 26, 2018 Inference for Spatio-Temporal Changes of Arctic Sea Ice
Noel Cressie (University of Wollongong)
Forecasting from Complexity
April 23, 2018 School of Math Ordway Distinguished Lecture: Applied Random Matrix Theory
Joel Tropp
Forecasting from Complexity
April 26, 2018 Stochastic Simulation of Predictive Space-Time Scenarios of Wind Speed Using Observations and Physical Models
Mihai Anitescu (Argonne National Laboratory)
Forecasting from Complexity
April 24, 2018 Assessing Internal Climate Variability with Few Ensemble Runs
Dorit Hammerling (NCAR)
Forecasting from Complexity
April 26, 2018 Non-parametric Sparse Additive Auto-regressive Network Models
Garvesh Raskutti (University of Wisconsin, Madison)
Forecasting from Complexity
April 24, 2018 Generating Calibrated Ensembles of Physically Realistic, High-Resolution Precipitation Forecast Fields Based on GEFS Model Output
Michael Scheuerer (National Oceanographic and Atmospheric Administration (NOAA))
Forecasting from Complexity
April 25, 2018 Estimation of inhomogeneous point processes: Theory and applications
Mark Davenport (Georgia Institute of Technology)
Forecasting from Complexity
April 27, 2018 Spatio-Temporal Point Process Models for Ambulance Demand
David S. Matteson (Cornell University)
Forecasting from Complexity
April 26, 2018 Multi-class modelling for muscle level prediction of beef eating quality
Garth Tarr (University of Sydney)
Forecasting from Complexity
April 26, 2018 Dynamic Shrinkage Processes
Daniel Kowal (Rice University)
Forecasting from Complexity
April 24, 2018 Bayesian Calibration of Multistate Stochastic Simulators
Oksana Chkrebtii (The Ohio State University)
Forecasting from Complexity
April 26, 2018 Fast Simulation based Estimation for Complex Models
Maria-Pia Victoria-Feser (Universite de Geneve)
Forecasting from Complexity
April 24, 2018 Spectral analysis for comparison of second-order flow structure in DNS simulations
Charlotte Haley (Argonne National Laboratory)
Forecasting from Complexity
April 26, 2018 Geometric Statistics for High-Dimensional Data Analysis
Singdhansu (Ansu) Chatterjee (University of Minnesota, Twin Cities)
Forecasting from Complexity
March 05, 2018 Local keynote lecture: Reinforcement learning for functional state space with application to Type 1 Diabetes
Susan Wei (University of Minnesota, Twin Cities)
Women in Data Science (WiDS) Conference
March 05, 2018 Local keynote lecture: Ways of Knowing in HCI
Haiyi Zhu (University of Minnesota, Twin Cities)
Women in Data Science (WiDS) Conference
August 15, 2018 Computation with Degree-Rips Bifiltrations
Roy Zhao (University of California, Berkeley)
Tutorial on Multiparameter Persistence, Computation, and Applications
August 15, 2018 Statistics for a Computational Topologist Part II
Brittany Terese Fasy (Montana State University)
Tutorial on Multiparameter Persistence, Computation, and Applications
August 15, 2018 Two-Parameter Persistence for Virtual Ligand Screening
Bryn Keller (Intel Corporation)
Tutorial on Multiparameter Persistence, Computation, and Applications
August 13, 2018 Introduction to Persistent Homology
Matthew Wright (St. Olaf College)
Tutorial on Multiparameter Persistence, Computation, and Applications
August 13, 2018 Applications of Persistence
Lori Ziegelmeier (Macalester College)
Tutorial on Multiparameter Persistence, Computation, and Applications
August 13, 2018 Computation of Persistent Homology, Part I
Ulrich Bauer (Technical University of Munich)
Tutorial on Multiparameter Persistence, Computation, and Applications
August 13, 2018 An Introduction to Multi-D Persistent Homology
Michael Lesnick (Princeton University)
Tutorial on Multiparameter Persistence, Computation, and Applications
August 13, 2018 Morse Theory and Persistent Homology Computation
Gregory Henselman (University of Pennsylvania)
Tutorial on Multiparameter Persistence, Computation, and Applications
August 14, 2018 Computation of Persistent Homology, Part II
Ulrich Bauer (Technical University of Munich)
Tutorial on Multiparameter Persistence, Computation, and Applications
August 14, 2018 Quiver Representation Theory + Multiparameter Persistence
Magnus Botnan (Vrije Universiteit Amsterdam)
Tutorial on Multiparameter Persistence, Computation, and Applications
August 14, 2018 Visualizing Two-parameter Persistence
Matthew Wright (St. Olaf College)
Tutorial on Multiparameter Persistence, Computation, and Applications
August 14, 2018 Computing Minimal Presentations of Bipersistence Modules in Cubic Time
Michael Lesnick (Princeton University)
Tutorial on Multiparameter Persistence, Computation, and Applications
August 14, 2018 Statistics for a Computational Topologist, Part I
Brittany Terese Fasy (Montana State University)
Tutorial on Multiparameter Persistence, Computation, and Applications
August 15, 2018 Multiparameter Interleavings
Magnus Botnan (Vrije Universiteit Amsterdam)
Tutorial on Multiparameter Persistence, Computation, and Applications
August 15, 2018 Software Tutorial
Michael Lesnick (Princeton University), Matthew Wright (St. Olaf College)
Tutorial on Multiparameter Persistence, Computation, and Applications
February 22, 2018 Factor Augmented Vector Autoregressive Models under High Dimensional Scaling
George Michailidis (University of Florida)
Frontiers in Forecasting
February 21, 2018 A longitudinal Bayesian model for spectral analysis of neuroimaging time series data
Mark Fiecas (University of Minnesota, Twin Cities)
Frontiers in Forecasting
February 22, 2018 Methods for estimating network change points for multivariate time series
Ivor Cribben (University of Alberta)
Frontiers in Forecasting
February 21, 2018 Predictive effect of economic and market variations on structural breaks in credit market
Haipeng Xing (State University of New York, Stony Brook (SUNY))
Frontiers in Forecasting
February 23, 2018 Models for Time Series of Counts with Shape Constraints
Richard Davis (Columbia University)
Frontiers in Forecasting
February 23, 2018 High-dimensiona Classification for Spatially Dependent Data
Tapabrata (Taps) Maiti (Michigan State University)
Frontiers in Forecasting
February 23, 2018 Sequential change-point detection based on nearest neighbors
Hao Chen (University of California, Davis)
Frontiers in Forecasting
February 21, 2018 Global multivariate point pattern models for rain type occurrence and its extension to spatio-temporal domain
Mikyoung Jun (Texas A & M University)
Frontiers in Forecasting
February 23, 2018 High-Dimensional Bayesian Geostatistics
Sudipto Banerjee (University of California, Los Angeles)
Frontiers in Forecasting
February 21, 2018 Learning Connectivity Networks from High-Dimensional Point Processes
Ali Shojaie (University of Washington)
Frontiers in Forecasting
February 23, 2018 Lensing and de-lensing the cosmic microwave background
Ethan Anderes (University of California, Davis)
Frontiers in Forecasting
February 21, 2018 Variable Targeting and Reduction in High-Dimensional Vector Autoregressions
Tucker McElroy (U.S. Bureau of the Census)
Frontiers in Forecasting
February 21, 2018 Semi-parametric Dynamic Max-copula Model for Multivariate Time Series
Zhengjun Zhang (University of Wisconsin, Madison)
Frontiers in Forecasting
February 21, 2018 Dynamic landmark prediction for genetic mixture models
Tanya Garcia (Texas A & M University)
Frontiers in Forecasting
February 21, 2018 Sparse Vector Autoregressive Models
Christophe Croux (EDHEC Business School)
Frontiers in Forecasting
February 22, 2018 Extended ensemble Kalman filters for high-dimensional hierarchical state-space models
Matthias Katzfuss (Texas A & M University)
Frontiers in Forecasting
February 22, 2018 Forecasting large collections of times series
George Athanasopoulos (Monash University)
Frontiers in Forecasting
February 22, 2018 Interpretable Deep Learning Models for Forecasting
Yan Liu (University of Southern California)
Frontiers in Forecasting
February 22, 2018 Panel: Advanced Business Analytics and Forecasting at BASF
Sven Serneels (BASF Corporation)
Frontiers in Forecasting
February 22, 2018 High-dimensional Multivariate Time Series with Additional Structure
Katherine Ensor (Rice University)
Frontiers in Forecasting
February 22, 2018 Spectral analysis of high-dimensional time series with applications to the mean-variance frontier
Alexander Aue (University of California, Davis)
Frontiers in Forecasting
February 23, 2018 Conditional Adaptive Bayesian Spectral Analysis of Nonstationary Biomedical Time Series
Robert Krafty (University of Pittsburgh)
Frontiers in Forecasting
May 31, 2018 A Mathematical Model of the Bivalent Binding of Thrombin to Fibrin
Karin Leiderman (Colorado School of Mines)
Workshop for Women in Mathematical Biology
May 31, 2018 Flagellated micro-swimmers in a viscous fluid
Sookkyung Lim (University of Cincinnati)
Workshop for Women in Mathematical Biology

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