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February 25, 2019 |
Strong Coresets for k-Median and Subspace Approximation: Goodbye Dimension
David Woodruff (Carnegie Mellon University) |
Data Science Seminar |
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February 22, 2019 |
Industrial Problems in Digital Retail
Samantha Schumacher (Target Corporation) |
Industrial Problems Seminar |
|
February 18, 2019 |
Graph Convolutional Neural Network via Scattering
Dongmian Zou (University of Minnesota, Twin Cities) |
Data Science Seminar |
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February 11, 2019 |
Style Transfer by Relaxed Optimal Transport and Self-Similarity
Greg Shakhnarovich (Toyota Technological Institute at Chicago) |
Data Science Seminar |
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February 08, 2019 |
Using Wearable Sensors to Predict Pain States
Kyle Srivastava (Boston Scientific) |
Industrial Problems Seminar |
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February 04, 2019 |
Gromov-Monge Quasi Metrics and Distance Distributions
Tom Needham (The Ohio State University) |
Data Science Seminar |
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January 28, 2019 |
Data-Driven Distributionally Robust Appointment Scheduling
Guanglin Xu (University of Minnesota, Twin Cities) |
Data Science Seminar |
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January 25, 2019 |
Probabilistic Analysis of Operational Transit Data to Create Insights for Planners
Eric Lind (Metro Transit) |
Industrial Problems Seminar |
|
December 10, 2018 |
Stratifying High-Dimensional Data Based on Proximity to the Convex Hull Boundary
Lori Ziegelmeier (Macalester College) |
Data Science Seminar |
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December 07, 2018 |
When Seeing is not Believing: New Forensics Algorithms to Detect Image Manipulations
Michael Albright (Honeywell) |
Industrial Problems Seminar |
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December 06, 2018 |
Thompson Sampling for learning in online decision making
Shipra Agrawal (Columbia University) |
From Theory to Practice: Data-driven Supply Chain Management |
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December 06, 2018 |
Statistical State Compression and Primal-Dual Pi Learning
Mengdi Wang (Princeton University) |
From Theory to Practice: Data-driven Supply Chain Management |
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December 06, 2018 |
Inventory Repositioning in On-Demand Product Rental Networks
Xiaobo Li (National University of Singapore) |
From Theory to Practice: Data-driven Supply Chain Management |
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December 06, 2018 |
Simple Bayesian Algorithms for Pure-exploration
Dan Russo (Columbia University) |
From Theory to Practice: Data-driven Supply Chain Management |
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December 05, 2018 |
Revenue Management and Pricing with Strategic (Forward-looking) Customers
Yiwei Chen (University of Cincinnati) |
From Theory to Practice: Data-driven Supply Chain Management |
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December 05, 2018 |
Online Learning and Decision-Making under Generalized Linear Model with High-Dimensional Data
Mike Wei (University at Buffalo (SUNY)) |
From Theory to Practice: Data-driven Supply Chain Management |
|
December 05, 2018 |
Interpreting Predictive Models for Human-in-the-Loop Analytics
Hamsa Bastani (Wharton School of the University of Pennsylvania) |
From Theory to Practice: Data-driven Supply Chain Management |
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December 05, 2018 |
Managing a newsvendor network under uncertainty: case study of a pharmacy retailer in India
Chaithanya Bandi (Northwestern University) |
From Theory to Practice: Data-driven Supply Chain Management |
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December 04, 2018 |
The Secret Behind Fast Delivery - From Inventory Management Perspective
Rong Yuan (JD.COM) |
From Theory to Practice: Data-driven Supply Chain Management |
|
December 04, 2018 |
Reducing Exploration in Personalized Decision-Making
Mohsen Bayati (Stanford University) |
From Theory to Practice: Data-driven Supply Chain Management |
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December 04, 2018 |
Data-driven Supply Chain Management at Huawei
Tianxiao Chen (Huawei Technologies) |
From Theory to Practice: Data-driven Supply Chain Management |
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December 03, 2018 |
Absenteeism Prediction and Extra-board Driver Scheduling for Bus Transit Operations
Qie He (University of Minnesota, Twin Cities) |
From Theory to Practice: Data-driven Supply Chain Management |
|
December 03, 2018 |
The Supply Chain Management Challenges of the Future in Agriculture
Jorge Guanter (Cargill, Inc.) |
From Theory to Practice: Data-driven Supply Chain Management |
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December 03, 2018 |
Inventory Balancing with Online Learning
Xinshang Wang (Massachusetts Institute of Technology) |
From Theory to Practice: Data-driven Supply Chain Management |
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December 03, 2018 |
Retail Supply Chain at Target Scale
Kaveh Khodjasteh (Target Corporation) |
From Theory to Practice: Data-driven Supply Chain Management |
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November 29, 2018 |
Interpolative Decomposition and its Applications (Math Dept Colloquium Lecture)
Lexing Ying (Stanford University) |
Data Science Seminar |
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November 28, 2018 |
Convex Relaxation Approaches for Strictly Correlated Density Functional Theory
Lexing Ying (Stanford University) |
Data Science Seminar |
|
November 26, 2018 |
Solving PDEs with Deep Learning
Lexing Ying (Stanford University) |
Data Science Seminar |
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November 19, 2018 |
Lipschitz Regularized Deep Neural Networks Converge and are Robust to Adversarial Perturbations
Adam Oberman (McGill University) |
Data Science Seminar |
|
November 16, 2018 |
Some Remarks
Robert Langlands (Institute for Advanced Study) |
Sixth Abel Conference: A Mathematical Celebration of Robert P. Langlands |
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November 16, 2018 |
Constructing and characterizing a local Langlands correspondence
Michael Harris (Columbia University) |
Sixth Abel Conference: A Mathematical Celebration of Robert P. Langlands |
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November 16, 2018 |
Advanced Dynamics Systems Analysis and Synthesis for Future Storage Challenges
Raye Sosseh (Seagate Technology) |
Industrial Problems Seminar |
|
November 16, 2018 |
Harish-Chandra characters and the local Langlands correspondence
Tasho Kaletha (University of Michigan) |
Sixth Abel Conference: A Mathematical Celebration of Robert P. Langlands |
|
November 16, 2018 |
Endoscopy and geometry
Ngo Bao Chau (University of Chicago) |
Sixth Abel Conference: A Mathematical Celebration of Robert P. Langlands |
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November 15, 2018 |
Shimura Varieties
Sophie Morel (Princeton University) |
Sixth Abel Conference: A Mathematical Celebration of Robert P. Langlands |
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November 15, 2018 |
The geometric Langlands correspondence and electromagnetic duality
Edward Frenkel (University of California, Berkeley) |
Sixth Abel Conference: A Mathematical Celebration of Robert P. Langlands |
|
November 15, 2018 |
The p-adic Langlands program
Matthew Emerton (University of Chicago) |
Sixth Abel Conference: A Mathematical Celebration of Robert P. Langlands |
|
November 15, 2018 |
Residue distributions and harmonic analysis on reductive groups
Eric Opdam (Universiteit van Amsterdam) |
Sixth Abel Conference: A Mathematical Celebration of Robert P. Langlands |
|
November 14, 2018 |
The Stable Trace Formula
James Arthur (University of Toronto) |
Sixth Abel Conference: A Mathematical Celebration of Robert P. Langlands |
|
November 14, 2018 |
Functoriality and Beyond Endoscopy
Salim Altug (Boston University) |
Sixth Abel Conference: A Mathematical Celebration of Robert P. Langlands |
|
November 14, 2018 |
On an analytic theory of automorphic forms for complex algebraic curves
Edward Frenkel (University of California, Berkeley) |
Sixth Abel Conference: A Mathematical Celebration of Robert P. Langlands |
|
November 14, 2018 |
Eisenstein Series and L-functions
Freydoon Shahidi (Purdue University) |
Sixth Abel Conference: A Mathematical Celebration of Robert P. Langlands |
|
November 12, 2018 |
Learning from Highly Correlated Features using Graph Total Variation
Rebecca Willett (University of Chicago) |
Data Science Seminar |
|
November 09, 2018 |
A novel method to identify and prioritize drugs for an individual (N=1) based on clinical and multi-omics data
Krishna Kalari (Mayo Clinic) |
Statistical and Computational Challenges in Precision Medicine |
|
November 09, 2018 |
Efficient Discovery of Heterogeneous Treatment Effects via Anomalous Pattern Detection
Edward McFowland (University of Minnesota, Twin Cities) |
Statistical and Computational Challenges in Precision Medicine |
|
November 09, 2018 |
C-learning: A New Classification Framework to Estimate Optimal Dynamic Treatment Regimes
Min Zhang (University of Michigan) |
Statistical and Computational Challenges in Precision Medicine |
|
November 09, 2018 |
Outcome-Weighted Learning for Personalized Medicine with Multiple Treatment Options
Donglin Zeng (University of North Carolina, Chapel Hill) |
Statistical and Computational Challenges in Precision Medicine |
|
November 09, 2018 |
Personalized risk prediction using longitudinal data: Applications in obstetrics and cancer
Paul Albert (National Cancer Institute) |
Statistical and Computational Challenges in Precision Medicine |
|
November 08, 2018 |
Statistical Analysis of SMART Studies via Arti fcial Randomization
Abdus Wahed (University of Pittsburgh) |
Statistical and Computational Challenges in Precision Medicine |
|
November 08, 2018 |
Quantile-optimal Treatment Regimes with Censored Data
Lan Wang (University of Minnesota, Twin Cities) |
Statistical and Computational Challenges in Precision Medicine |
|
November 08, 2018 |
HASS: Hybrid Algorithm for Subgroup Search via ADMM and EM Algorithms
Peter Song (University of Michigan) |
Statistical and Computational Challenges in Precision Medicine |
|
November 08, 2018 |
Estimating and testing targeted mediation effect in the presence of high-dimensional mediators
Lei Liu (Washington University School of Medicine) |
Statistical and Computational Challenges in Precision Medicine |
|
November 08, 2018 |
Statistical Inference of Covariate-Adjusted Randomized Experiment
Feifang Hu (George Washington University) |
Statistical and Computational Challenges in Precision Medicine |
|
November 08, 2018 |
Adaptive testing on a high-dimensional parameter with application to precision medicine
Wei Pan (University of Minnesota, Twin Cities) |
Statistical and Computational Challenges in Precision Medicine |
|
November 08, 2018 |
Learning Multi-relations in Biomedical Networks and Tensors
Rui Kuang (University of Minnesota, Twin Cities) |
Statistical and Computational Challenges in Precision Medicine |
|
November 08, 2018 |
Quantile Regression in Genetic studies
Ying Wei (Columbia University) |
Statistical and Computational Challenges in Precision Medicine |
|
November 08, 2018 |
Group and Individual Non-Gaussian Components Analysis
David S. Matteson (Cornell University) |
Statistical and Computational Challenges in Precision Medicine |
|
November 08, 2018 |
Clustering Mixed-Type Data
Marianthi Markatou (University at Buffalo (SUNY)) |
Statistical and Computational Challenges in Precision Medicine |
|
November 08, 2018 |
Keynote
Eric Laber (North Carolina State University) |
Statistical and Computational Challenges in Precision Medicine |
|
November 07, 2018 |
Poster session and reception
Zhengling Qi (University of North Carolina, Chapel Hill)
|
Statistical and Computational Challenges in Precision Medicine |
|
November 07, 2018 |
Matched Learning for Optimizing Individualized Treatment Strategies Using Electronic Health Records
Yuanjia Wang (Columbia University) |
Statistical and Computational Challenges in Precision Medicine |
|
November 07, 2018 |
Selection of the Optimal Personalized Treatment from Multiple Treatments with Multivariate Outcome Measures
Somnath Datta (University of Florida) |
Statistical and Computational Challenges in Precision Medicine |
|
November 07, 2018 |
Digital Health and Big Data for Drug Development
Ray Liu (Takeda Pharmaceuticals Inc.) |
Statistical and Computational Challenges in Precision Medicine |
|
November 07, 2018 |
Analysis of implantable cardiac device diagnostics to tailor management of heart failure
Tracy Bergemann (Medtronic) |
Statistical and Computational Challenges in Precision Medicine |
|
November 07, 2018 |
Estimating Causal (Personalized) Effects within Regulatory Tobacco Randomized Controlled Trials
David Vock (University of Minnesota, Twin Cities) |
Statistical and Computational Challenges in Precision Medicine |
|
November 07, 2018 |
Thinking Causally About High-Dimensional Databases
Debashis Ghosh (University of Colorado Denver) |
Statistical and Computational Challenges in Precision Medicine |
|
November 07, 2018 |
Optimal treatment and timing of routine surveillance in children after allogeneic hematopoietic cell transplantaion
Zhezhen Jin (Columbia University) |
Statistical and Computational Challenges in Precision Medicine |
|
November 07, 2018 |
A Bayesian Imputation Approach to Optimizing Dynamic Treatment Regimes
Thomas Murray (University of Minnesota, Twin Cities) |
Statistical and Computational Challenges in Precision Medicine |
|
November 07, 2018 |
How good is your selected subgroup?
Xuming He (University of Michigan) |
Statistical and Computational Challenges in Precision Medicine |
|
November 05, 2018 |
Solving Infinite Dimensional Optimization Problems with Convergence Guarantees
Matthew Jacobs (University of California, Los Angeles) |
Data Science Seminar |
|
November 02, 2018 |
Using Electronic Health Record Data for Clinical Analyses: Early Learnings
Joao Montero (Medtronic) |
Industrial Problems Seminar |
|
October 29, 2018 |
A Picture of the Energy Landscape of Deep Neural Networks
Pratik Chaudhari (California Institute of Technology) |
Data Science Seminar |
|
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 26, 2018 |
Primal-dual algorithms and their applications
Ming Yan (Michigan State University) |
Recent Advances in Machine Learning and Computational Methods for Geoscience |
|
October 26, 2018 |
Addressing spatial uncertainty during remote sensing data analysis
Alina Zare (University of Florida) |
Recent Advances in Machine Learning and Computational Methods for Geoscience |
|
October 25, 2018 |
On the Use of Gaussian Process Models for Problems in CubeSat Data Interpolation
Eric Miller (Tufts University) |
Recent Advances in Machine Learning and Computational Methods for Geoscience |
|
October 25, 2018 |
Matched-pair machine learning for hyperspectral target detection
Amanda Ziemann (Los Alamos National Laboratory) |
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 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 25, 2018 |
Machine Learning Applied to Discern Fault Characteristics
Paul Johnson (Los Alamos 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 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 24, 2018 |
Harmonization and Fusion of Global Scale Data
Nathan Longbotham (Descartes Labs, Inc.) |
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 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 |
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 |
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 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 23, 2018 |
On the effectiveness of joint inversion
Jodi Mead (Boise State University) |
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 23, 2018 |
Information-theoretic approaches for multiscale simulations and data assimilation
Francesca Boso (Stanford University) |
Recent Advances in Machine Learning and Computational Methods for Geoscience |
|
October 23, 2018 |
Machine learning in pre- and post-imaging seismic data with different roles of wave physics
Weichang Li (Aramco Services Company) |
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 |
Inverse methods for estimating fracture transmissibilities in porous media
Clint Dawson (The University of Texas at Austin) |
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 |
|
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 22, 2018 |
Large Sample Asymptotics of Graph-based Methods in Machine Learning: Mathematical Analysis and Implications
Nicolas Garcia-Trillos (University of Wisconsin, Madison) |
Data Science Seminar |
|
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 22, 2018 |
Multiscale methods for neural image processing
Tom Goldstein (University of Maryland) |
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 |
