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 
October 17, 2018 
Handson tutorial  Developing Python bindings William Hart (Sandia National Laboratories), Carl Laird (Sandia National Laboratories), JeanPaul Watson (Sandia National Laboratories) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 17, 2018 
Lunch  on your own or with breakout group on licensing issues Matthew Saltzman (Clemson University) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 17, 2018 
Python bindings for Ipopt Carl Laird (Sandia National Laboratories) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 17, 2018 
Alternative packages for Python bindings William Hart (Sandia National Laboratories) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 17, 2018 
Python bindings: Overview and challenges JeanPaul Watson (Sandia National Laboratories) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 16, 2018 
Reports from breakout groups Matthias Koeppe (University of California, Davis) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 16, 2018 
Track 2: Special interest group on bigpicture documentation issues Giacomo Nannicini (IBM) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 16, 2018 
Track 1: handson tutorial  Unit testing and CI in C++/Python Ted Ralphs (Lehigh University) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 16, 2018 
Lunch  on your own or with breakout group on website management Andrew Mason (University of Auckland) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 16, 2018 
Documentation in the modern age Giacomo Nannicini (IBM) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 16, 2018 
Some Perspectives on Testing and Continuous Integration for Open Source Software William Hart (Sandia National Laboratories), John Siirola (Sandia National Laboratories) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 16, 2018 
Continuous integration and testing in COINOR Ted Ralphs (Lehigh University) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 15, 2018 
Track 1: handson tutorial  Version Control Ted Ralphs (Lehigh University) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 15, 2018 
Track 2: handson tutorial  Build System Stefan Vigerske (GAMS Development Corporation) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 15, 2018 
Free Component Analysis Raj Nadakuditi (University of Michigan) 
Data Science Seminar 
October 15, 2018 
Build Tools Stefan Vigerske (GAMS Development Corporation) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 15, 2018 
Project Management Ted Ralphs (Lehigh University) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 15, 2018 
COINOR and the Optimization Suite Ted Ralphs (Lehigh University) 
COIN fORgery: Developing Open Source Tools for Operations Research 
October 12, 2018 
Artificial Intelligence in a Material World Jennifer Schumacher (3M) 
Industrial Problems Seminar 
October 08, 2018 
A PDE Approach to a Prediction Problem Involving Randomized Strategies Nadejda Drenska (University of Minnesota, Twin Cities) 
Data Science Seminar 
October 05, 2018 
SampleBased Optimal Pricing Omar Besbes (Columbia University) 
Forging a New Discipline: Datadriven Supply Chain Management 
October 05, 2018 
Design of Information Sharing Mechanisms in Service Systems Krishnamurthy Iyer (Cornell University) 
Forging a New Discipline: Datadriven Supply Chain Management 
October 05, 2018 
Where to find the next passenger on ehailing platform?  A reinforcement learning approach Sharon Di (Columbia University) 
Forging a New Discipline: Datadriven Supply Chain Management 
October 05, 2018 
OmniChannel Order Fulfillment: From Concept to Practice Vivek Farias (Massachusetts Institute of Technology) 
Forging a New Discipline: Datadriven Supply Chain Management 
October 04, 2018 
Dynamic Assortment Planning under Various Discrete Choice Models Xi Chen (New York University) 
Forging a New Discipline: Datadriven Supply Chain Management 
October 04, 2018 
Closing the Gap: A Learning Algorithm for the LostSales Inventory System with Lead Times Cong Shi (University of Michigan) 
Forging a New Discipline: Datadriven Supply Chain Management 
October 04, 2018 
Distributionally Robust Linear and Discrete Optimization with Marginals Karthik Natarajan (Singapore University of Technology and Design) 
Forging a New Discipline: Datadriven Supply Chain Management 
October 04, 2018 
Joint pricing and inventory control models with long lead times Xin Chen (University of Illinois at UrbanaChampaign) 
Forging a New Discipline: Datadriven Supply Chain Management 
October 04, 2018 
Smart Predict, then Optimize Adam Elmachtoub (Columbia University) 
Forging a New Discipline: Datadriven Supply Chain Management 
October 04, 2018 
Approximation Algorithms for Network Revenue Management Huseyin Topaloglu (Cornell University) 
Forging a New Discipline: Datadriven Supply Chain Management 
October 04, 2018 
Behavioral Analytics for Myopic Agents Philip Kaminsky (University of California, Berkeley) 
Forging a New Discipline: Datadriven Supply Chain Management 
October 03, 2018 
A Conditional Gradient Approach for Nonparametric Estimation of Mixing Distributions Srikanth Jagabathula (New York University) 
Forging a New Discipline: Datadriven Supply Chain Management 
October 03, 2018 
Online Assortment Optimization with Reusable Resources Vineet Goyal (Columbia University) 
Forging a New Discipline: Datadriven Supply Chain Management 
October 03, 2018 
Interpretable Optimal Stopping Velibor Misic (University of California, Los Angeles) 
Forging a New Discipline: Datadriven Supply Chain Management 