Campuses:

Talks

Date Title / Speaker Event
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
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: hands-on 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 COIN-OR 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 COIN-OR 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 Hands-on tutorial - Developing Python bindings
William Hart (Sandia National Laboratories), Carl Laird (Sandia National Laboratories), Jean-Paul 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
Jean-Paul 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 1: hands-on tutorial - Unit testing and CI in C++/Python
Ted Ralphs (Lehigh University)
COIN fORgery: Developing Open Source Tools for Operations Research
October 16, 2018 Track 2: Special interest group on big-picture documentation issues
Giacomo Nannicini (IBM)
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 COIN-OR
Ted Ralphs (Lehigh University)
COIN fORgery: Developing Open Source Tools for Operations Research
October 15, 2018 Track 1: hands-on tutorial - Version Control
Ted Ralphs (Lehigh University)
COIN fORgery: Developing Open Source Tools for Operations Research
October 15, 2018 Track 2: hands-on 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 COIN-OR 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 Sample-Based Optimal Pricing
Omar Besbes (Columbia University)
Forging a New Discipline: Data-driven Supply Chain Management
October 05, 2018 Design of Information Sharing Mechanisms in Service Systems
Krishnamurthy Iyer (Cornell University)
Forging a New Discipline: Data-driven Supply Chain Management
October 05, 2018 Where to find the next passenger on e-hailing platform? - A reinforcement learning approach
Sharon Di (Columbia University)
Forging a New Discipline: Data-driven Supply Chain Management
October 05, 2018 Omni-Channel Order Fulfillment: From Concept to Practice
Vivek Farias (Massachusetts Institute of Technology)
Forging a New Discipline: Data-driven Supply Chain Management
October 04, 2018 Dynamic Assortment Planning under Various Discrete Choice Models
Xi Chen (New York University)
Forging a New Discipline: Data-driven Supply Chain Management
October 04, 2018 Closing the Gap: A Learning Algorithm for the Lost-Sales Inventory System with Lead Times
Cong Shi (University of Michigan)
Forging a New Discipline: Data-driven 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: Data-driven Supply Chain Management
October 04, 2018 Joint pricing and inventory control models with long lead times
Xin Chen (University of Illinois at Urbana-Champaign)
Forging a New Discipline: Data-driven Supply Chain Management
October 04, 2018 Smart Predict, then Optimize
Adam Elmachtoub (Columbia University)
Forging a New Discipline: Data-driven Supply Chain Management
October 04, 2018 Approximation Algorithms for Network Revenue Management
Huseyin Topaloglu (Cornell University)
Forging a New Discipline: Data-driven Supply Chain Management
October 04, 2018 Behavioral Analytics for Myopic Agents
Philip Kaminsky (University of California, Berkeley)
Forging a New Discipline: Data-driven 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: Data-driven Supply Chain Management
October 03, 2018 Online Assortment Optimization with Reusable Resources
Vineet Goyal (Columbia University)
Forging a New Discipline: Data-driven Supply Chain Management
October 03, 2018 Interpretable Optimal Stopping
Velibor Misic (University of California, Los Angeles)
Forging a New Discipline: Data-driven Supply Chain Management

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