December 06, 2018 
Thompson Sampling for learning in online decision making Shipra Agrawal (Columbia University) 
From Theory to Practice: Datadriven Supply Chain Management 
December 06, 2018 
Statistical State Compression and PrimalDual Pi Learning Mengdi Wang (Princeton University) 
From Theory to Practice: Datadriven Supply Chain Management 
December 06, 2018 
Inventory Repositioning in OnDemand Product Rental Networks Xiaobo Li (National University of Singapore) 
From Theory to Practice: Datadriven Supply Chain Management 
December 06, 2018 
Simple Bayesian Algorithms for Pureexploration Dan Russo (Columbia University) 
From Theory to Practice: Datadriven Supply Chain Management 
December 05, 2018 
Revenue Management and Pricing with Strategic (Forwardlooking) Customers Yiwei Chen (University of Cincinnati) 
From Theory to Practice: Datadriven Supply Chain Management 
December 05, 2018 
Online Learning and DecisionMaking under Generalized Linear Model with HighDimensional Data Mike Wei (University at Buffalo (SUNY)) 
From Theory to Practice: Datadriven Supply Chain Management 
December 05, 2018 
Interpreting Predictive Models for HumanintheLoop Analytics Hamsa Bastani (Wharton School of the University of Pennsylvania) 
From Theory to Practice: Datadriven Supply Chain Management 
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: Datadriven Supply Chain Management 
December 04, 2018 
The Secret Behind Fast Delivery  From Inventory Management Perspective Rong Yuan (JD.COM) 
From Theory to Practice: Datadriven Supply Chain Management 
December 04, 2018 
Reducing Exploration in Personalized DecisionMaking Mohsen Bayati (Stanford University) 
From Theory to Practice: Datadriven Supply Chain Management 
December 04, 2018 
Datadriven Supply Chain Management at Huawei Tianxiao Chen (Huawei Technologies) 
From Theory to Practice: Datadriven Supply Chain Management 
December 03, 2018 
Absenteeism Prediction and Extraboard Driver Scheduling for Bus Transit Operations Qie He (University of Minnesota, Twin Cities) 
From Theory to Practice: Datadriven Supply Chain Management 
December 03, 2018 
The Supply Chain Management Challenges of the Future in Agriculture Jorge Guanter (Cargill, Inc.) 
From Theory to Practice: Datadriven Supply Chain Management 
December 03, 2018 
Inventory Balancing with Online Learning Xinshang Wang (Massachusetts Institute of Technology) 
From Theory to Practice: Datadriven Supply Chain Management 
December 03, 2018 
Retail Supply Chain at Target Scale Kaveh Khodjasteh (Target Corporation) 
From Theory to Practice: Datadriven Supply Chain Management 
November 29, 2018 
Interpolative Decomposition and its Applications (Math Dept Colloquium Lecture) Lexing Ying (Stanford University) 
Data Science Seminar 
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 
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 
November 16, 2018 
Constructing and characterizing a local Langlands correspondence Michael Harris (Columbia University) 
Sixth Abel Conference: A Mathematical Celebration of Robert P. Langlands 
November 16, 2018 
Advanced Dynamics Systems Analysis and Synthesis for Future Storage Challenges Raye Sosseh (Seagate Technology) 
Industrial Problems Seminar 
November 16, 2018 
HarishChandra 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 
November 15, 2018 
Shimura Varieties Sophie Morel (Princeton University) 
Sixth Abel Conference: A Mathematical Celebration of Robert P. Langlands 
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 padic 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 Lfunctions 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 multiomics 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 
Clearning: 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 
OutcomeWeighted 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 Artifcial Randomization Abdus Wahed (University of Pittsburgh) 
Statistical and Computational Challenges in Precision Medicine 
November 08, 2018 
Quantileoptimal 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 highdimensional mediators Lei Liu (Washington University School of Medicine) 
Statistical and Computational Challenges in Precision Medicine 
November 08, 2018 
Statistical Inference of CovariateAdjusted Randomized Experiment Feifang Hu (George Washington University) 
Statistical and Computational Challenges in Precision Medicine 
November 08, 2018 
Adaptive testing on a highdimensional parameter with application to precision medicine Wei Pan (University of Minnesota, Twin Cities) 
Statistical and Computational Challenges in Precision Medicine 
November 08, 2018 
Learning Multirelations 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 NonGaussian Components Analysis David S. Matteson (Cornell University) 
Statistical and Computational Challenges in Precision Medicine 
November 08, 2018 
Clustering MixedType 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 HighDimensional 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 
Primaldual 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 
Matchedpair 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 SemiSupervised Machine Learning for Image Analysis of MultiModal 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 
GEOAI  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 
Spaceborne 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 
Informationtheoretic 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 postimaging 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 Graphbased Methods in Machine Learning: Mathematical Analysis and Implications Nicolas GarciaTrillos (University of Wisconsin, Madison) 
Data Science Seminar 
October 22, 2018 
Fast and Accurate MaximumLikelihood Estimation of Parameterized Spectral Densities that Jointly Characterize Bivariate TwoDimensional 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 machinelearning era for geophysical imaging Monica Maceira (Oak Ridge National Laboratory) 
Recent Advances in Machine Learning and Computational Methods for Geoscience 
October 19, 2018 
Lunch breakout group on the future of Clp/Cbc Ted Ralphs (Lehigh University) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 19, 2018 
Reports from breakout groups Ted Ralphs (Lehigh University) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 19, 2018 
Panel: Research software challenges Matthias Koeppe (University of California, Davis), Ted Ralphs (Lehigh University), Matthew Saltzman (Clemson University) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 18, 2018 
Reports from breakout groups Matthias Koeppe (University of California, Davis) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 18, 2018 
Track 2: Special interest group on interfaces Matthew Saltzman (Clemson University) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 18, 2018 
Track 1: handson tutorial  Bug squashing: from TRAC to Github Ted Ralphs (Lehigh University) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 18, 2018 
Lunch  on your own or with breakout group on the future of the COINOR Foundation Matthew Saltzman (Clemson University) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 18, 2018 
Panel: Developing generic interfaces Oscar Dowson (Northwestern University), Horand Gassmann (Dalhousie University), Bjarni Kristjansson (Maximal Software, Ltd), Matthew Saltzman (Clemson University) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 18, 2018 
COINOR interfaces: OSI, CGL Matthew Saltzman (Clemson University) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 17, 2018 
Reports from breakout groups Matthias Koeppe (University of California, Davis) 
COIN fORgery: Developing Open Source Tools for Operations Research 