Name: 2019 Joint Statistical Meetings
Start: 2019-07-27T07:00:00+00:00
End: 2019-08-01
Location: Colorado Convention Center

Legend:
CC = Colorado Convention Center H = Hyatt Regency Denver at Colorado Convention Center
* = applied session ! = JSM meeting theme

Activity Details

CE_11C	Sun, 7/28/2019, 8:30 AM - 5:00 PM	CC-405
Statistical Network Analysis and Applications in Biology (ADDED FEE) — Professional Development Continuing Education Course
ASA, Section on Statistical Learning and Data Science
Instructor(s): Ali Shojaie, University of Washington; George Michailidis, University of Florida
This one-day course will provide a practical introduction to statistical network analysis methods for biological application. The course will cover four classes of methods: statistical methods for network data analysis; inference methods for undirected networks; inference methods for directed networks; and differential network analysis. The methods covered include methods that are widely used in biological applications and, in particular, in the analysis of -omics data, as well as recent developments in statistical machine learning. Throughout, the emphasis will be on practical applications of network analysis methods, as well as their limitations, including validation of results. Case studies using publicly available data will be used to describe various statistical network analysis methods. The course is based on two short courses taught by the instructors: Since 2012, Dr. Shojaie has taught a short course titled “Pathway and Network Analysis of Omics Data” at the University of Washington. This 2.5-day course has been well received with more than 40 participants in each offering. Dr. Michailidis has taught a 1.5-day short course from 2014-2017 on the statistical analysis of metabolomics data that included a substantial portion on network analysis, and has attracted approximately 50 participants in each offering.
8:30 AM	Statistical Network Analysis and Applications in Biology (ADDED FEE) Ali Shojaie, University of Washington; George Michailidis, University of Florida

8 * !	Sun, 7/28/2019, 2:00 PM - 3:50 PM	CC-207
Machine Learning Methods and Applications: Making an Impact in Biomedical Research — Invited Papers
Section on Statistical Learning and Data Science, Biometrics Section, Section on Statistical Computing
Organizer(s): Juanjuan Fan, San Diego State University
Chair(s): Xiangrong Yin, University of Kentucky
2:05 PM	Finite Mixture Clustering of Risk Behaviors for an Infectious Disease Joseph Kang, Centers for Disease Control and Prevention (CDC)
2:30 PM	RELIEF-Based Feature Selection for Heterogeneous Treatment Effects with Massive Data Presentation Xiaogang Su, University of Texas, El Paso
2:55 PM	Matching Methods for Observational Data with Small Group Sizes and Mising Covariates Presentation Juanjuan Fan, San Diego State University; Afrooz Jahedi, San Diego State University; Tristan Hillis, San Diego State University; Ralph-Axel Mueller, San Diego State University
3:20 PM	Post-Market Surveillance of Arthroplasty Device Components Using Machine Learning Guy Cafri, Johnson & Johnson
3:45 PM	Floor Discussion

22 * !	Sun, 7/28/2019, 2:00 PM - 3:50 PM	CC-707
Testing and Evaluation of High-Dimensional Models — Topic Contributed Papers
Section on Bayesian Statistical Science, Section on Nonparametric Statistics, Section on Statistical Learning and Data Science
Organizer(s): Steve MacEachern, The Ohio State University
Chair(s): Juhee Lee, University of California, Santa Cruz
2:05 PM	Detection of Common-Variance Subspace and Its Application to Classification Jiae Kim, The Ohio State University; Steve MacEachern, The Ohio State University
2:25 PM	Horseshoes, Shape Mixing, and Ultra-Sparse Locally Adaptive Shrinkage Presentation Andrew Womack, Indiana University
2:45 PM	Comparing and Combining Forecast Distributions Having Different Dimensions Catherine Forbes, Monash University
3:05 PM	Inconvenient Diagnostics and Corrections for Convenience Samples Eloise Kaizar, Ohio State University
3:25 PM	Model Misspecification and Familial Null Hypotheses Presentation Steve MacEachern, The Ohio State University
3:45 PM	Floor Discussion

38	Sun, 7/28/2019, 2:00 PM - 3:50 PM	CC-210/212
Advances in Variable Selection — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Joanne C Beer, University of Pennsylvania
2:05 PM	Simultaneous Confidence Regions for Coefficients in High-Dimensional Linear Models Presentation Xiaorui Zhu, University of Cincinnati; Peng Wang, University of Cincinnati; Yichen Qin, University of Cincinnati
2:20 PM	Functional Variable Selection with Correlated Functional Covariates and Longitudinal Responses Presentation Rebecca North, NCSU Statistics; Jonathan Stallrich, North Carolina State University; Ana-Maria Staicu, North Carolina State University; Helen Huang, NCSU Biomedical Engineering; Dustin Crouch, University of Tennessee, Knoxville; Mechanical, Aerospace, and Biomedical Engineering
2:35 PM	Feature Selection in Large Data with Heteroscedastic Errors Presentation Yiying Fan, Cleveland State University
2:50 PM	A New Information Criterion for Model Selection Jie Ding, University of Minnesota; Vahid Tarokh, Duke University; Yuhong Yang, University of Minnesota
3:05 PM	Floor Discussion

45 !	Sun, 7/28/2019, 4:00 PM - 5:50 PM	CC-709
Emerging Methods for Network Testing and Related Problems — Invited Papers
IMS, Section on Statistical Learning and Data Science, Section on Statistics in Defense and National Security
Organizer(s): Eric Kolaczyk, Boston University
Chair(s): Elizabeth Upton, Boston University
4:05 PM	Goodness-of-Fit Tests for 3 Variants of the Stochastic Block Model Presentation Vishesh Karwa, Temple University; Debdeep Pati, Texas A&M University; Sonja Petrovic, Illinois Institute of Technology; Liam Solus, KTH, Sweden; Mateja Raic, University of Illinois at Chicago; Dane Wilburne, ICERM, Brown University; Nikita Alexeev, unknown; Robert Williams, Texas A&M University; Bowei Yan, University of Texas
4:30 PM	A Broad Perspective on Network Testing Sofia C Olhede, University College London; Patrick J Wolfe, Purdue University
4:55 PM	Signal Detection in Spiked Random Matrix and Network Models Zongming Ma, University of Pennsylvania; Debapratim Banerjee, University of Pennsylvania
5:20 PM	Discussant: Daniel L Sussman, Boston University
5:40 PM	Floor Discussion

59 * !	Sun, 7/28/2019, 4:00 PM - 5:50 PM	CC-605
Deep Learning in Statistics: Really?! — Topic Contributed Papers
Section on Statistical Learning and Data Science, Section on Statistical Computing, Biometrics Section, Text Analysis Interest Group
Organizer(s): Wei Pan, University of Minnesota
Chair(s): Wei Pan, University of Minnesota
4:05 PM	Embedding Learning Presentation Ben Dai, University of Minnesota; Xiaotong Shen, University of Minnesota
4:25 PM	Deep Learning in Pathological Image Analysis Guanghua Xiao, UT Southwestern Medical Center; Shidan Wang, UT Southwestern Medical Center
4:45 PM	Complex Disease Risk Prediction via a Deep Learning Method Chong Wu, Florida State University
5:05 PM	Incorporating Biological Network to Build Deep Learning Models for Gene Expression Data Tianwei Yu, Emory University; Yunchuan Kong, Emory University
5:25 PM	Graph Convolutional Neural Networks for Multiple Gene Networks HU Yang, Central University of Finance and Economics; Wei Pan, University of Minnesota
5:45 PM	Floor Discussion

62 * !	Sun, 7/28/2019, 4:00 PM - 5:50 PM	CC-702
Data Fusion: An Exploration of Practical Aspects — Topic Contributed Papers
Section on Physical and Engineering Sciences, Section on Statistical Learning and Data Science, Section on Statistics in Defense and National Security
Organizer(s): Emily Casleton, Los Alamos National Laboratory
Chair(s): Kimberly Kaufeld, Los Alamos National Laboratory
4:05 PM	Bayesian Analysis of Multivariate One-Way ANOVA Model Zhuoqiong He
4:25 PM	Data Fusion with Transition-Constrained Diarization Goran Konjevod, Lawrence Livermore National Laboratory; Jason Lenderman, LLNL
4:45 PM	Computational and Interpretational Considerations for Multivariate Analytics in Nuclear Nonproliferation Multisensor Arrays Presentation Marylesa Howard, Nevada National Security Site; Aaron Luttman, Nevada National Security Site; Bethany Goldblum, University of California Berkeley; Christopher Stewart, University of California Berkeley; Zoe Gastelum, Sandia National Laboratories; Boian Alexandrov, Los Alamos National Laboratory; Margaret Hoeller, Nevada National Security Site; Daniel J. Champion, Nevada National Security Site
5:05 PM	Data Fusion and Feature Selection to Inform the State of a Nuclear Reactor Presentation Nidhi Parikh, Los Alamos National Laboratory; Garrison Flynn, Los Alamos National Laboratory; Adin Egid, Los Alamos National Laboratory; Emily Casleton, Los Alamos National Laboratory
5:25 PM	Integrated Statistical Learning and Feature Selection for Improved Biomarker Discovery Presentation Lisa Bramer, Pacific Northwest National Laboratory; Bobbie-Jo Webb-Robertson, Pacific Northwest National Laboratory; Sarah Reehl, Pacific Northwest National Laboratory
5:45 PM	Floor Discussion

79	Sun, 7/28/2019, 4:00 PM - 5:50 PM	CC-502
Functional Data Analysis: Methods and Applications — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Shrijita Bhattacharya, Michigan State University
4:05 PM	Hypothesis Testing in Functional Linear Concurrent Regression Rahul Ghosal, North Carolina State University; Arnab Maity, North Carolina State University
4:20 PM	Multivariate Functional Data Clustering with Variable Selection and an Application to Sensory Data Zhongnan Jin, Virginia Tech; Yili Hong, Virginia Tech
4:35 PM	Estimating Plant Growth Curves and Derivatives by Modeling Crowdsourced Imaged-Based Data Haozhe Zhang, Iowa State University; Dan Nettleton, Iowa State University; Stefan Hey, Iowa State University; Talukder Jubery, Iowa State University; Patrick Schnable, Iowa State University
4:50 PM	Historical and Restricted Function-On-Function Regression Models Presentation Ruiyan Luo, Georgia State University; Xin Qi, Georgia State University
5:05 PM	Statistical Analysis of Partially Observed Shapes in Two Dimensions with Applications in Biological Anthropology Presentation 1 Presentation 2 Gregory Matthews; Ofer Harel, Dept of Statistics, U of Connecticut; Juliet Brophy, Louisiana State University ; George Thiruvathukal, Loyola University Chicago
5:20 PM	Functional Regression for Highly Densely Observed Data with Novel Regularization Presentation Xin Qi, Georgia State University; Ruiyan Luo, Georgia State University
5:35 PM	A Novel Nonparametric Clustering Method for Longitudinal Data Junyi Zhou, Indiana University; Ying Zhang, University of Nebraska Medical Center

80	Sun, 7/28/2019, 4:00 PM - 5:50 PM	CC-503
Graphical Models and Causal Inference — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Sai Kumar Popuri, Demand Forecasting Group at Walmart Labs
4:05 PM	Learning Latent Network Structure from High-Dimensional Multivariate Point Processes Presentation Biao Cai, University of Miami; Emma Jingfei Zhang, University of Miami; Yongtao Guan, University of Miami
4:20 PM	Causal Inference Under Network Interference with Noise Wenrui Li, Boston University; Eric Kolaczyk, Boston University; Daniel L Sussman, Boston University
4:35 PM	Gaussian DAGs on Network Data Presentation Hangjian Li, UCLA; Qing Zhou, UCLA
4:50 PM	Per-Family Error Rate Control for Gaussian Graphical Model via Knockoffs Siliang Gong, University of Pennsylvania; Qi Long, University of Pennsylvania; Weijie Su, University of Pennsylvania
5:05 PM	Using Cyclic Structure to Improve Inference on Networks Presentation Behnaz Moradijamei, Kansas State University; Michael Higgins, Kansas State University
5:20 PM	Estimation in Additive Exposure Models Kelly Kung, Boston University; Daniel L Sussman, Boston University
5:35 PM	Bayesian Framework for Predictive and Causal Modeling Using BART Presentation Yizhen Xu, Brown University; Tao Liu, Brown University; Rami Kantor, Brown University; Ann Mwangi, Moi University; Michael Daniels, University of Florida; Joseph Hogan, Brown University

103 !	Mon, 7/29/2019, 8:30 AM - 10:20 AM	CC-205
New Developments on Statistical Machine Learning — Invited Papers
IMS, Section on Statistical Learning and Data Science, International Chinese Statistical Association
Organizer(s): Jianqing Fan, Princeton Univeristy
Chair(s): Yingying Fan, University of Southern California
8:35 AM	Deep Knockoffs Machines Presentation Emmanuel Candes, Stanford University; Yaniv Romano, Stanford University; Matteo Sesia, Stanford University
9:00 AM	Statistical and Computational Guarantees of EM with Random Initialization Harrison H. Zhou, Yale Uinversity ; Yihong Wu, Yale University
9:25 AM	Single-Index Thresholding in Quantile Regression Presentation Huixia Judy Wang, The George Washington University; Yingying Zhang, Fudan University; Zhongyi Zhu, Fudan University
9:50 AM	Transfer Learning for Nonparametric Classification T. Tony Cai, The Wharton School, University of Pennsylvania
10:15 AM	Floor Discussion

112	Mon, 7/29/2019, 8:30 AM - 10:20 AM	CC-109
Statistical Challenges in the Processing and Analysis of Mobile Health Data — Invited Papers
Section on Statistics in Epidemiology, Section on Statistical Learning and Data Science, Biometrics Section
Organizer(s): Joseph Rigdon, Stanford University
Chair(s): Summer Han, Stanford University
8:35 AM	SMART for Health App Recommenders Ying Kuen Ken Cheung, Columbia University
8:55 AM	Precision Medicine in Mobile Health Using V-Learning Presentation Daniel Luckett, University of North Carolina at Chapel Hill; Eric B Laber, NC State University; Anna Kahkoska, University of North Carolina at Chapel Hill; David Maahs, Stanford University; Elizabeth Mayer-Davis, University of North Carolina at Chapel Hill; Michael Kosorok, University of North Carolina at Chapel Hill
9:15 AM	Design and Sample Size Considerations for Multi-Level Motivational Messages in Micro-Randomized Trials Bibhas Chakraborty, Duke-National University of Singapore Medical School
9:35 AM	Parameterizing Exploration Presentation Jesse Clifton, NC State University; Lili Wu, North Carolina State University; Eric B Laber, NC State University
9:55 AM	Statistical Challenges in the Processing and Analysis of Accelerometer Data Manisha Desai, Stanford University Quantitative Sciences Unit
10:15 AM	Floor Discussion

113 * !	Mon, 7/29/2019, 8:30 AM - 10:20 AM	CC-702
New Developments on Data Integration and Data Fusion — Topic Contributed Papers
Section on Statistical Learning and Data Science, Biometrics Section, ENAR
Organizer(s): Gen Li, Columbia University
Chair(s): Gen Li, Columbia University
8:35 AM	Bayesian Nonparametric Clustering Analysis with an Incorporation of Biological Network for High-Dimensional Multi-Scale Molecular Data Yize Zhao, Yale University
8:55 AM	Integrative linear Discriminant Analysis with Guaranteed Error Rate Improvement Quefeng Li, University of North Carolina Chapel Hill; Lexin Li, University of California at Berkeley
9:15 AM	Insights into Impact of DNA Copy Number Alteration and Methylation on the Proteogenomic Landscape of Human Ovarian Cancer via a Multi-Omics Integrative Analysis Presentation Jiayi Ji; Xiaoyu Song, Icahn School of Medicine at Mount Sinai
9:35 AM	Sparse Semiparametric Canonical Correlation Analysis for Data of Mixed Types Irina Gaynanova, Texas A&M Univeristy; Grace Yoon, Texas A&M University; Raymond J. Carroll, Texas A & M University
9:55 AM	A Double Core Tensor Factorization and Its Applications to Heterogeneous Data George Michailidis, University of Florida
10:15 AM	Floor Discussion

122 !	Mon, 7/29/2019, 8:30 AM - 10:20 AM	CC-603
Novel Statistical Methods in the Analysis of Big Data — Topic Contributed Papers
Section on Statistical Computing, International Chinese Statistical Association, Section on Statistical Learning and Data Science
Organizer(s): Elizabeth Schifano, University of Connecticut
Chair(s): Ming-Hui Chen, University of Connecticut
8:35 AM	Online Updating of Survival Analysis Elizabeth Schifano, University of Connecticut; Jing Wu, University of Rhode Island; Ming-Hui Chen, University of Connecticut; Jun Yan, University of Connecticut
8:55 AM	Optimal Subsampling: Sampling with Replacement Vs Poisson Sampling HaiYing Wang, University of Connecticut; Jiahui Zou, Academy of Mathematics and Systems Science, Chinese Academy of Sciences
9:15 AM	Leverage Score Sampling for Multidimensional Streaming Time Series Shuyang Bai, University of Georgia; Rui Xie, University of Georgia; Ping Ma, University of Georgia; Wenxuan Zhong, University of Georgia; Zengyan Wang, University of Georgia
9:35 AM	Subsampled Information Criterion for Bayesian Model Selection in Big Data Setting Guanyu Hu, University of Connecticut; Lijiang Geng, University of Connecticut ; Yishu Xue, University of Connecticut
9:55 AM	Modified Multidimensional Scaling Qiang Sun, University of Toronto
10:15 AM	Floor Discussion

127	Mon, 7/29/2019, 8:30 AM - 10:20 AM	CC-501
SPEED: Statistical Learning and Data Science Speed Session 1, Part 1 — Contributed Speed
Section on Statistical Learning and Data Science
Chair(s): Ali Shojaie, University of Washington
Poster Presentations for this session.
8:35 AM	Comparing Time Series Graphical Lasso and Sparse VAR Algorithms Aramayis Dallakyan, Texas A&M University; Rakheon Kim, Texas A&M University; Mohsen Pourahmadi, Texas A&M University
8:40 AM	Using Factor Analysis in Variable Selection and Clustering of US Mass Shooting Incidents Presentation 1 Presentation 2 John McMorris; Yew-Meng Koh, Hope College
8:45 AM	Model Selection for Mixture of Experts Using Group Fused Lasso Presentation Tuan Do, University of South Carolina; Karl Gregory, University of South Carolina
8:50 AM	Deep Learning and MARS: a Connection Presentation Sophie Langer, Technische Universitaet Darmstadt; Michael Kohler, Technische Universitaet Darmstadt; Adam Krzyzak, Concordia University
8:55 AM	Distance and Kernel Measures of Conditional Independence Tianhong Sheng, The Pennsylvania State University; Bharath Sriperumbudur, The Pennsylvania State University
9:00 AM	Sparse Functional Principal Component Analysis in High Dimensions Xiaoyu Hu, peking university; Fang Yao, peking university
9:05 AM	Activation Adaptation in Neural Networks Presentation Vahid Partovi Nia, Huawei Technologies, Ecole Polytechnique de Montreal; Farnoush Farhadi, Ericsson ; Andrea Lodi, Ecole Polytechnique de Montreal
9:10 AM	Multiple Imputation Versus Machine Learning: Predictive Models to Facilitate Analyzes of Association Between Contemporaneous Medicaid/CHIP Enrollment Status and Health Measures Jennifer Rammon, National Center for Health Statistics/CDC; Yulei He, CDC; Jennifer Parker, CDC/NCHS/OAE/SPB
9:15 AM	A Greedy-Type Variable Selection Procedure for Selecting High-Dimensional Cox Models Chien-Tong Lin; Yu-Jen Cheng, National Tsing Hua University; Ching-Kang Ing, National Tsin Hua University
9:20 AM	Cross-Validation for Correlated Data Presentation Assaf Rabinowicz, Tel-Aviv University; Saharon Rosset, Tel Aviv University
9:30 AM	Inference for Measurement Error Model Under High-Dimensional Settings Presentation Mengyan Li, Penn State University; Yanyuan Ma, The Pennsylvania State University
9:35 AM	Does T-SNE Identify False Structure? Implications of Clusterability on T-SNE Maps Presentation Paul Harmon, Montana State University; Mark Greenwood, Montana State University; Tristan Anacker, Montana State University
9:40 AM	Visual Diagnostics of a Model Explainer: Tools for the Assessment of LIME Explanations from Random Forests Presentation Katherine Goode, Iowa State University; Heike Hofmann, Iowa State University
9:45 AM	Quantile Regression Under Memory Constraint Presentation Yichen Zhang, New York University; Xi Chen, New York University; Weidong Liu, Shanghai Jiaotong University
9:50 AM	Equilibrium Metrics for Dynamic Supply-Demand Networks Presentation Fan Zhou, University of North Carolina at Chapel Hill; Hongtu Zhu, DiDi Chuxing and UNC-Chapel Hill; Jieping Ye, Didi Chuxing
9:55 AM	Topological Survival Analysis for the Comparison of Random Fields Presentation Hollie Johnson
10:00 AM	Curve Registration to Identify Circadian Rhythm Chronotypes in Accelerometer Data Presentation Erin McDonnell, Columbia University; Julia Wrobel, Columbia University; Jeff Goldsmith, Columbia University; Vadim Zipunnikov, Johns Hopkins University
10:05 AM	Mallows Model Averaging of Support Vector Machine Classfiers and Regressors Presentation Francis Kiwon, McMaster University
10:10 AM	To Select or Not to Select? Variable Selection in the Estimation of Drug Use Prevalence in Denmark Presentation Anne Helby Petersen, University of Copenhagen; Niels Keiding, University of Copenhagen
10:15 AM	Efficient Randomized Algorithms for Continuous Space Reinforcement Learning Presentation 1 Presentation 2 Mohamad Kazem Shirani Faradonbeh, University of Florida; Ambuj Tewari, University of Michigan; George Michailidis, University of Florida

136	Mon, 7/29/2019, 8:30 AM - 10:20 AM	CC-701
Recent Advances in Dimension Reduction — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Linda Ng Boyle, University of Washington
8:35 AM	Signal-Plus-Noise Matrix Models: Eigenvector Deviations and Fluctuations Joshua Cape, Johns Hopkins University; Minh Tang, Johns Hopkins University; Carey E Priebe, Johns Hopkins University
8:50 AM	Representative Approach for Big Data Dimension Reduction with Binary Responses Xuelong Wang, University of Illinois at Chicago
9:05 AM	A Sufficient Dimension Reduction Method via Expectation of Conditional Difference Qingcong Yuan, Miami University; Wenhui Sheng, Marquette University; Xiangrong Yin, University of Kentucky
9:20 AM	GMDR: Generalized Matrix Decomposition Regression Yue Wang, Fred Hutchinson Cancer Center; Ali Shojaie, University of Washington; Timothy Randolph, Fred Hutchinson Cancer Research Center; Jing Ma, Fred Hutchinson Cancer Center
9:35 AM	Matrix-Free Likelihood Methods for Exploratory Factor Analysis with High-Dimensional Gaussian Data Fan Dai, Iowa State University; Somak Dutta, Iowa State University; Ranjan Maitra, Iowa State University
9:50 AM	Principal Component-Guided Sparse Regression Presentation Kenneth Tay, Stanford University; Jerome Friedman, Stanford University; Robert Tibshirani, Stanford University
10:05 AM	High-Dimensional Prediction with Sparse Principal Components Presentation Lei Ding, Indiana University Bloomington; Daniel McDonald, Indiana University Bloomington

CE_16C	Mon, 7/29/2019, 8:30 AM - 5:00 PM	CC-407
A First Step into Deep Learning for Computer Vision (ADDED FEE) — Professional Development Continuing Education Course
ASA, Section on Statistical Learning and Data Science
Instructor(s): Robert Blanchard, SAS; Brett Wujek, SAS Institute Inc.; Sarah Kalicin, Intel Corporation
At the heart of the artificial intelligence revolution is the significant advancement in deep learning technology. Deep learning – more directly, the application of complex, sophisticated deep neural network architectures – has shown impressive promise in solving problems that were previously considered infeasible to solve. In particular, image classification and object detection have become extremely accurate, and the ability to understand, process and generate natural language from speech and text has provided a whole new level of interaction of humans with computers and devices. In this workshop you will learn the fundamentals of deep learning from the ground up, and hands-on exercises with deep learning software, using SAS Viya, will have you building and applying convolutional neural networks for image classification. Students should have some familiarity with SAS, R, and/or Python and predictive modeling techniques.
8:30 AM	A First Step into Deep Learning for Computer Vision (ADDED FEE) Brett Wujek, SAS Institute Inc.; Robert Blanchard, SAS; Sarah Kalicin, Intel Corporation

139 !	Mon, 7/29/2019, 10:30 AM - 12:20 PM	CC-707
Precision Medicine in High-Dimensional Settings — Invited Papers
Association of Health Services Research, Section on Statistical Learning and Data Science, Academy for Health Services Research and Health Policy
Organizer(s): Ashkan Ertefaie, University of Rochester
Chair(s): Ashkan Ertefaie, University of Rochester
10:35 AM	Adaptive Designs for Learning Optimal Individualized Treatment Rules Presentation Mark van der Laan, UC Berkeley
11:00 AM	Minimax Optimal Causal Inference in a High-Dimensional Discrete Model Edward Kennedy, Carnegie Mellon University
11:25 AM	A Sparse Random Projection-Based Test for Overall Qualitative Treatment Effects Chengchun Shi, North Carolina State University; Wenbin Lu, North Carolina State University; Rui Song, North Carolina State University
11:50 AM	Discussant: Eric B Laber, NC State University
12:10 PM	Floor Discussion

141 * !	Mon, 7/29/2019, 10:30 AM - 12:20 PM	CC-607
Statistical Understanding of Deep Learning — Invited Papers
Section on Statistical Learning and Data Science, International Chinese Statistical Association
Organizer(s): Will Wei Sun, Purdue University
Chair(s): Will Wei Sun, Purdue University
10:35 AM	Stein Neural Sampler Guang Cheng, Purdue Statistics; Tianyang Hu, Purdue Statistics; Zixiang Chen, Tsinghua Statistics; Hanxi Sun, Purdue Statistics; Jincheng Bai, Purdue Statistics; Mao Ye, Purdue Statistics
11:00 AM	ALMOND: Adaptive Latent Modeling and Optimization via Neural Networks and Langevin Diffusion Presentation Xiao Wang, Purdue University; Yixuan Qiu, Carnegie Mellon University
11:25 AM	Some Statistical Insights into Deep Learning Hao Wu, University of Southern California; Yingying Fan, University of Southern California; Jinchi Lv, University of Southern California
11:50 AM	Data-Dependent Regularization and Generalization Bounds of Deep Neural Networks Presentation Tengyu Ma, Stanford University
12:15 PM	Floor Discussion

149 * !	Mon, 7/29/2019, 10:30 AM - 12:20 PM	CC-702
Government Cybersecurity Research: Statistical Challenges and Opportunities — Invited Papers
Section on Statistics in Defense and National Security, Section on Statistical Learning and Data Science, Government Statistics Section
Organizer(s): Justin Newcomer, Sandia National Laboratories
Chair(s): Lyndsay Shand, Sandia National Laboratories
10:35 AM	A Broad Overview of AI/ML and Cybersecurity Presentation 1 Presentation 2 Presentation 3 Adam Cardinal-Stakenas, National Security Agency
11:00 AM	Latent Feature Models for Network Link Prediction with Labelled Nodes Presentation Melissa Turcotte, Los Alamos National Laboratory
11:25 AM	Analyzing Cyber Networks Using Spectral Embedding and a Kernel-Based Procrustes Algorithm Presentation David Marchette, NSWCDD
11:50 AM	Dynamic Model Updating for Streaming Classification and Clustering Alexander Foss, Sandia National Laboratories
12:15 PM	Floor Discussion

150 * !	Mon, 7/29/2019, 10:30 AM - 12:20 PM	CC-603
Recent Advances in Nonparametric Statistical Methods for Complex Data — Invited Papers
Section on Nonparametric Statistics, IMS, Section on Statistical Learning and Data Science
Organizer(s): Lingzhou Xue, Penn State University and National Institute of Statistical Sciences
Chair(s): Danning Li, Penn State University
10:35 AM	Statistical Approach to Topological Data Analysis Kenji Fukumizu, Institute of Statistical Mathematics
11:00 AM	Dimension Reduction for Functional Databased on Weak Conditional Moments Presentation Bing Li, The Pennsylvania State University; Jun Song, University of North Carolina at Charlotte
11:25 AM	Nonconvex Statistical Learning for the Dimensionality Reduction of High-Dimensional Data Lingzhou Xue, Penn State University and National Institute of Statistical Sciences; Shiqian Ma, University of California, Davis; Hui Zou, University of Minnesota
11:50 AM	Detecting Rare and Weak Spikes in Large Covariance Matrices Zheng Tracy Ke, Harvard University
12:15 PM	Floor Discussion

160 *	Mon, 7/29/2019, 10:30 AM - 12:20 PM	CC-110
Editor's Choice: Papers Published in the American Statistician During 2018 — Topic Contributed Papers
Biometrics Section, Section on Bayesian Statistical Science, Section on Statistical Learning and Data Science
Organizer(s): Daniel Jeske, University of California, Riverside
Chair(s): Daniel Jeske, University of California, Riverside
10:35 AM	Abandon Statistical Significance Blakeley McShane, Northwestern University; Andrew Gelman, Columbia University; Christian Robert, Ceremade - Université Paris-Dauphine ; David Gal, University of Illinois at Chicago; Jennifer Tackett, Northwestern University
10:55 AM	On Mixture Alternatives and Wilcoxon’s Signed-Rank Test Presentation Jonathan Rosenblatt, Ben Gurion University of the Negev; Yoav Benjamini, Tel Aviv University
11:15 AM	A Bayesian Survival Analysis of a Historical Dataset: How Long Do Popes Live? Presentation Luciana Dalla Valle, University of Plymouth; Julian Stander, University of Plymouth; Mario Cortina-Borja, UCL GOS Institute of Child Health
11:35 AM	Guns and Suicides Presentation Danilo Santa Cruz Coelho, Instituto de Pesquisa Econômica Aplicada; Daniel Cerqueira, Instituto de Pesquisa Econômica Aplicada; Marcelo Fernandes, Sao Paulo School of Economics, FGV; Jony Pinto Junior, Universidade Federal Fluminense
11:55 AM	Forecasting at Scale Sean Taylor , Facebook
12:15 PM	Floor Discussion

163 * !	Mon, 7/29/2019, 10:30 AM - 12:20 PM	CC-708
Methods for Complex Data: The Next Generation — Topic Contributed Papers
Business and Economic Statistics Section, Section on Statistical Learning and Data Science, Business Analytics/Statistics Education Interest Group, IMS
Organizer(s): David Matteson, Cornell University
Chair(s): Ines Wilms, Maastricht University
10:35 AM	Structured Shrinkage Priors Presentation Maryclare Griffin, Cornell University Center for Applied Mathematics; Peter Hoff, Duke University
10:55 AM	High-Dimensional Causal Discovery with Non-Gaussian Data Presentation Y. Samuel Wang, University of Chicago; Mathias Drton, University of Washington
11:15 AM	Projection pursuit based generalized betas accounting for higher order co-moment effects in financial market analysis Presentation Sven Serneels, Aspen Technology
11:35 AM	Learning Local Dependence in Ordered Data Guo Yu, University of Washington; Jacob Bien, University of Southern California
11:55 AM	Sequential Change-Point Detection for High-Dimensional and Non-Euclidean Data Lynna Chu, University of California, Davis; Hao Chen, University of California, Davis
12:15 PM	Floor Discussion

166 * !	Mon, 7/29/2019, 10:30 AM - 12:20 PM	CC-703
New Developments for Using R in the Biopharmaceutical Industry — Topic Contributed Panel
Section for Statistical Programmers and Analysts, Biopharmaceutical Section, Section on Statistical Learning and Data Science
Organizer(s): Kuolung Hu, Ionis Pharmaceuticals, Inc.
Chair(s): Marianne Miller, Eli Lilly and Company
10:35 AM	Open-Source Tools for Monitoring Clinical Trial Safety – Taking it to the Next Level Towards Cross-Functional Collaboration Presentation 1 Presentation 2 Presentation 3
Panelists:	Eric Nantz, Eli Lilly Jeremy Wildfire, RHO, Inc Min Lee, Amgen Paul Schuette, FDA Satha Thill, AbbVie
12:10 PM	Floor Discussion

178	Mon, 7/29/2019, 10:30 AM - 12:20 PM	CC-712
Novel Applications and Extensions of Dimension Reduction Methods — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Timothy I. Cannings, University of Edinburgh
10:35 AM	Comparison of Simple and Complex Predictive Models Applied to the National Surveys on Drug Use and Health Presentation Georgiy Bobashev, Research Triangle Institute; Emily Hadley, RTI International
10:50 AM	Graph-Based Dependency Criterion with Applications in Biology Presentation Salimeh Yasaei Sekeh, University of Michigan; Alfred O. Hero, University of Michigan
11:05 AM	Bi-Orthogonal Tensor Decomposition for Image Style Matching Presentation Yutong Li, University of Illinois at Urbana-Champaign; Ruoqing Zhu, University of Illinois Urbana-Champaign; Annie Qu, University of Illinois at Urbana-Champaign
11:20 AM	Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models Yuqi Gu, University of Michigan; Gongjun Xu, University of Michigan
11:35 AM	Sparse Generalized Principal Component Analysis: Algorithms and Their Applications Jianhao Zhang, Ohio State University; Yoonkyung Lee, Ohio State University
11:50 AM	Tensor on Tensor Regression with Various Low-Rank Regression Parameters and Elliptically Contoured Distributed Errors Presentation Carlos Llosa, Iowa State University; Ranjan Maitra, Iowa State University
12:05 PM	Application of Personalized Growth Curve in Customer Life Time Value Estimation via Embedding Presentation Liang Xie, Didi Chuxing

181	Mon, 7/29/2019, 10:30 AM - 11:15 AM	CC-Hall C
SPEED: Statistical Learning and Data Science Speed Session 1, Part 2 — Contributed Poster Presentations
Section on Statistical Learning and Data Science
Chair(s): Ali Shojaie, University of Washington
Oral Presentations for this session.
1:	Comparing Time Series Graphical Lasso and Sparse VAR Algorithms Aramayis Dallakyan, Texas A&M University; Rakheon Kim, Texas A&M University; Mohsen Pourahmadi, Texas A&M University
2:	Using Factor Analysis in Variable Selection and Clustering of US Mass Shooting Incidents John McMorris; Yew-Meng Koh, Hope College
3:	Model Selection for Mixture of Experts Using Group Fused Lasso Tuan Do, University of South Carolina; Karl Gregory, University of South Carolina
4:	Deep Learning and MARS: a Connection Sophie Langer, Technische Universitaet Darmstadt; Michael Kohler, Technische Universitaet Darmstadt; Adam Krzyzak, Concordia University
5:	Distance and Kernel Measures of Conditional Independence Tianhong Sheng, The Pennsylvania State University; Bharath Sriperumbudur, The Pennsylvania State University
6:	Sparse Functional Principal Component Analysis in High Dimensions Xiaoyu Hu, peking university; Fang Yao, peking university
7:	Activation Adaptation in Neural Networks Vahid Partovi Nia, Huawei Technologies, Ecole Polytechnique de Montreal; Farnoush Farhadi, Ericsson ; Andrea Lodi, Ecole Polytechnique de Montreal
8:	Multiple Imputation Versus Machine Learning: Predictive Models to Facilitate Analyzes of Association Between Contemporaneous Medicaid/CHIP Enrollment Status and Health Measures Jennifer Rammon, National Center for Health Statistics/CDC; Yulei He, CDC; Jennifer Parker, CDC/NCHS/OAE/SPB
9:	A Greedy-Type Variable Selection Procedure for Selecting High-Dimensional Cox Models Chien-Tong Lin; Yu-Jen Cheng, National Tsing Hua University; Ching-Kang Ing, National Tsin Hua University
10:	Cross-Validation for Correlated Data Assaf Rabinowicz, Tel-Aviv University; Saharon Rosset, Tel Aviv University
11:	Inference for Measurement Error Model Under High-Dimensional Settings Mengyan Li, Penn State University; Yanyuan Ma, The Pennsylvania State University
12:	Does T-SNE Identify False Structure? Implications of Clusterability on T-SNE Maps Paul Harmon, Montana State University; Mark Greenwood, Montana State University; Tristan Anacker, Montana State University
13:	Visual Diagnostics of a Model Explainer: Tools for the Assessment of LIME Explanations from Random Forests Katherine Goode, Iowa State University; Heike Hofmann, Iowa State University
14:	Quantile Regression Under Memory Constraint Yichen Zhang, New York University; Xi Chen, New York University; Weidong Liu, Shanghai Jiaotong University
15:	Equilibrium Metrics for Dynamic Supply-Demand Networks Fan Zhou, University of North Carolina at Chapel Hill; Hongtu Zhu, DiDi Chuxing and UNC-Chapel Hill; Jieping Ye, Didi Chuxing
16:	Topological Survival Analysis for the Comparison of Random Fields Hollie Johnson
17:	Curve Registration to Identify Circadian Rhythm Chronotypes in Accelerometer Data Erin McDonnell, Columbia University; Julia Wrobel, Columbia University; Jeff Goldsmith, Columbia University; Vadim Zipunnikov, Johns Hopkins University
18:	Mallows Model Averaging of Support Vector Machine Classfiers and Regressors Francis Kiwon, McMaster University
19:	To Select or Not to Select? Variable Selection in the Estimation of Drug Use Prevalence in Denmark Anne Helby Petersen, University of Copenhagen; Niels Keiding, University of Copenhagen
20:	Efficient Randomized Algorithms for Continuous Space Reinforcement Learning Mohamad Kazem Shirani Faradonbeh, University of Florida; Ambuj Tewari, University of Michigan; George Michailidis, University of Florida
Oral Presentations for this session.

211 * !	Mon, 7/29/2019, 2:00 PM - 3:50 PM	CC-605
Getting to the Slope of Enlightenment with EHR Data — Invited Papers
Section on Statistical Computing, Section on Statistical Learning and Data Science, Biometrics Section
Organizer(s): Jeffrey Leek, Johns Hopkins Bloomberg School of Public Health
Chair(s): Jeffrey Leek, Johns Hopkins Bloomberg School of Public Health
2:05 PM	Handling Sampling and Selection Bias in Phenome-Wide Association Studies Presentation Bhramar Mukherjee, University of Michigan
2:30 PM	Complex Data in, Nuanced Answers Out: Lessons Learned Analyzing Electronic Health Record Data in Oncology Sandra Griffith, Flatiron Health
2:55 PM	Challenges in Augmenting Randomized Trials with Observational Health Records Presentation Lucy D'Agostino McGowan, Johns Hopkins Bloomberg School of Public Health
3:20 PM	Discussant: Sherri Rose, Harvard Medical School
3:45 PM	Floor Discussion

212 * !	Mon, 7/29/2019, 2:00 PM - 3:50 PM	CC-505
Scientifically and Clinically Motivated Statistical Methods for Human Brain Data Analysis — Invited Papers
Section on Statistics in Imaging, Mental Health Statistics Section, Section on Statistical Learning and Data Science
Organizer(s): Tingting Zhang, University of Virginia
Chair(s): Dehan Kong, University of Toronto
2:05 PM	A Bayesian Stochastic-Blockmodel-Based Approach for Mapping Epileptic Brain Networks Tingting Zhang, University of Virginia
2:25 PM	Covariate-Adjusted Region-Referenced Generalized Functional Linear Model for EEG Data Damla Senturk, UCLA; Aaron Scheffler, UCLA; Donatello Telesca, UCLA; Catherine Sugar, UCLA; Shafali Jeste, UCLA; Abigail Dickinson, UCLA; Charlotte DiStefano, UCLA
2:45 PM	Characterizing the Longitudinal Behavior of Multiple Sclerosis Lesions on Structural Magnetic Resonance Images Presentation Elizabeth Sweeney, Weill Cornell
3:05 PM	Using Neuroimaging to Study Pain Presentation Martin Lindquist, Johns Hopkins University
3:25 PM	Brain Connectivity-Informed Adaptive Regularization for Generalized Outcomes Jaroslaw Harezlak, Indiana University School of Public Health; Damian Brzyski, Wroclaw Technological University; Marta Karas, Johns Hopkins School of Public Health; Beau Ances, Washington University School of Medicine; Joaquin Goni, Purdue University; Mario Dzemidzic, Indiana University School of Medicine; Timothy Randolph, Fred Hutchinson Cancer Research Center
3:45 PM	Floor Discussion

228 * !	Mon, 7/29/2019, 2:00 PM - 3:50 PM	CC-607
Interpreting Machine Learning Models: Opportunities, Challenges, and Applications — Topic Contributed Papers
Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, Section on Statistical Computing
Organizer(s): Vijayan Nair, Wells Fargo & University of Michigan, Ann Arbor
Chair(s): Joel B. Brodsky, Wells Fargo
2:05 PM	Understanding the Effects of Predictor Variables in Black-Box Supervised Learning Models Presentation Daniel W Apley, Northwestern University
2:25 PM	Deep Insights into Explainability and Interpretability of Machine Learning Algorithms and Applications to Risk Management Presentation Jie Chen, Wells Fargo
2:45 PM	Increasing Trust and Interpretability in Machine Learning with Model Debugging Presentation Patrick Hall, H2O.ai
3:05 PM	Detecting Interpretable Insights from Large-Scale Time Series Data Qing Feng, Facebook; Sean Taylor , Facebook
3:25 PM	Floor Discussion

248	Mon, 7/29/2019, 2:00 PM - 3:50 PM	CC-502
Machine Learning in Science and Industry — Contributed Papers
Section on Statistical Learning and Data Science, Text Analysis Interest Group
Chair(s): Jean Feng, University of Washington
2:05 PM	Music Classification Based on Sequential Naive Bayes and Music Score Data Tunan Ren, Guanghua School of Management; Hansheng Wang, Guanghua School of Management, Peking University, Beijing, China; Feifei Wang, School of Statistics, Renmin University of China, Beijing, China
2:20 PM	A Statistical and Machine Learning Framework for New Energy Vehicle Ride Sharing System Kaixian Yu, Didi Chuxing; Jinliang Deng, Hong Kong University of Science and Technology; Chengchun Shi, North Carolina State University; Rui Song, North Carolina State University; Qiang Yang, Hong Kong University of Science and Technology; Jieping Ye, Didi Chuxing; Hongtu Zhu, DiDi Chuxing and UNC-Chapel Hill
2:35 PM	Using Machine Learning to Assign North American Industry Classification System Codes to Establishments Based on Business Description Write-Ins Presentation Brian Dumbacher, U.S. Census Bureau; Anne Russell, U.S. Census Bureau
2:50 PM	Using a Network-Based Approach to Identify Gene Signatures That Predict Cancer Survival Minya Pu, University of California, San Diego; Judith Varner, University of California, San Diego; Karen Messer, University of California, San Diego
3:05 PM	A Machine-Learning Approach to Extract Remote-Sensing Features for Predicting Crop Yield Presentation Luca Sartore, National Institute of Statistical Sciences; Arthur Rosales, National Agricultural Statistics Service; David Johnson, National Agricultural Statistics Service; Mary Frances Dorn, Los Alamos National Laboratory; Clifford Spiegelman, Texas A&M University
3:20 PM	Dynamic Tensor Response Regression for Early Diagnosis of Alzheimer’s Disease Jie Zhou; Will Wei Sun, Purdue University; Lexin Li, University of California at Berkeley
3:35 PM	A Novel Method for Evaluating Co-Dependencies of Phenotypic Susceptibility to Multiple Antimicrobials Within and Between Bacterial Species in an Ecological Niche Heman Shakeri, Kansas State University; Victoriya Volkova, Kansas State University; Majid Jaberi-Douraki, Kansas State University

256	Mon, 7/29/2019, 2:00 PM - 3:50 PM	CC-Hall C
Contributed Poster Presentations: Section on Statistical Learning and Data Science — Contributed Poster Presentations
Section on Statistical Learning and Data Science, Text Analysis Interest Group
Chair(s): Wendy Meiring, University of California At Santa Barbara
65:	Accounting for Established Predictors with the Multi-Step Elastic Net Elizabeth C Chase, University of Michigan; Phil Boonstra, University of Michigan
66:	Big, Bad Matrices: a Constructive Approach Garrett Mulcahy, Purdue University; Thomas Sinclair, Purdue University
67:	Bimodal Sentiment Analysis of Service Calls YANAN JIA, Businessolver
68:	Feature Selection for High-Dimensional Clustering by Hidden Markov Model with Variable Blocks(HMM-VB) Beomseok Seo, Penn State University; Jia Li, Penn State University; Lynn Lin, Penn State University
69:	On the Selection of Regression Model Using Machine Learning Asanao Shimokawa, Tokyo University of Science; Etsuo Miyaoka, Tokyo University of Science
70:	Training Students Concurrently in Data Science and Team Science: Results and Lessons Learned from Multi-Institutional Interdisciplinary Student-Led Research Teams 2012-2018 Brent Ladd, Purdue University; Mark Ward, Purdue University
71:	Predicting Traffic Intensity with Deep Learning and Semantic Segmentation Logan Bradley-Trietsch, Purdue University; Xiao Wang, Purdue University
72:	Combining Machine Learning and Statistical Modeling to Identify Risk Factors of Hospital Mortality and Directionality for Patients with Acute Respiratory Distress Syndrome (ARDS) Meng Zhang, Feinstein Institute for Medical Research; Michael Qiu, Feinstein Institue for Medical Research; Molly Stewart, Feinstein Institue for Medical Research; Jamie Hirsch, Feinstein Institue for Medical Research; Negin Hajizadeh, Feinstein Institue for Medical Research
73:	Time Series Models to Forecast Mail Volume Xuemei Pan; Mary Pritts, IBM
75:	Testing Global Dynamics in C. Elegans Anastasia Dmitrienko, Columbia University; John Cunningham, Columbia University; Sean Bittner, Columbia University
76:	Testing for High-Dimensional Network Parameters in Auto-Regressive Models Lili Zheng, University of Wisconsin-Madison; Garvesh Raskutti, University of Wisconsin-Madison
77:	On the Non-Asymptotic and Sharp Lower Tail Bounds of Random Variables Yuchen Zhou, University of Wisconsin-Madison; Anru Zhang, University of Wisconsin-Madison
78:	A Computational Approach to the Structure of Subtraction Games Kali Lacy, Purdue University; Bret Benesh, College of Saint Benedict/Saint John's University; Jamylle Carter, Diablo Valley College; Deidra Coleman, Wofford College; Douglas Crabill, Purdue University; Jack Good, Purdue University; Michael Smith, Purdue University; Jennifer Travis, Lone Star College; Mark Ward, Purdue University
79:	Combining Materials and Data Science Haydn Schroader, Purdue University; Alejandro Strachan, Purdue University; Saaketh Desai, Purdue University; Juan Carlos Verduzco Gastelum, Purdue University; David Farache, Purdue University
80:	Computational and Theoretical Analysis of Novel Dimensionality Reduction Algorithms in Data Mining Brandon Guo Brandon Guo
81:	A Natural Language Processing Algorithm for Medication Extraction from Electronic Health Records Using the R Programming Language: MedExtractR Hannah L Weeks, Vanderbilt University; Cole Beck, Vanderbilt University Medical Center; Elizabeth McNeer, Vanderbilt University; Joshua C Denny, Vanderbilt University; Cosmin A Bejan, Vanderbilt University; Leena Choi, Vanderbilt University Medical Center
82:	Question Answering Using a Domain Specific Knowledge Base Mitchell Kinney, University of Minnesota - Twin Cities
83:	Propensity Score Analysis Using Machining Learning Techniques with Data Sets Involving Correlation of Covariates, Clustering, and Complex Outcome Functions and Propensity Scores Li He, Clemson University; William C. Bridges Jr., Clemson University
84:	Connecting Diverse Data with the Power of Natural Language Processing Methods Tracy Schifeling, Bluprint; Murat Tasan, Bluprint
85:	Performance of Latent Dirichlet Allocation with Different Topic and Document Structures Haotian Feng, Clemson University
86:	Using Push-Forward and Pullback Measures for Parameter Identification and Distribution Estimation Tian Yu Yen, University of Colorado At Denver; Michael Pilosov, University of Colorado At Denver
87:	Using Machine Learning to Incorporate Nutrition into Cardiovascular Mortality Risk Prediction Joseph Rigdon, Stanford University; Sanjay Basu, Stanford University
88:	Gender Differences in Authorship of Invited Commentary Articles in Medical Journals Emma Thomas, Harvard University; Bamini Jayabalasingham, Elsevier, Inc.; Thomas Collins, Elsevier, Inc.; Jeroen Geertzen, Elsevier, Inc.; Chinh Bui, Elsevier; Francesca Dominici, Harvard T.H. Chan School of Public Health
89:	Open Category Detection with PAC Guarantees Si Liu, Oregon State University; Risheek Garrepalli, Oregon State University; Thomas G. Dietterich, Oregon State University; Alan Fern, Oregon State University; Dan Hendrycks, UC Berkeley
90:	Statistical Inference in a High-Dimensional Binary Regression Problem with Noisy Responses Hyebin Song
91:	Personalized HeartSteps: a Reinforcement Learning Algorithm for Optimizing Physical Activity Peng Liao, University of Michigan; Susan Murphy, Harvard University; Predrag Klasnja, University of Michigan; Kristjan Greenewald, IBM
92:	Aggregated Single-Study Learners for Generalizable Predictions Boyu Ren; Lorenzo Trippa, Dana-Farber Cancer Institute; Giovanni Parmigiani, Dana-Farber Cancer Institute
93:	Recursive Optimization Using Diagonalized Hessian Estimate and Its Application in EM Shiqing Sun; James C. Spall, Applied Physics Laboratory

218967	Mon, 7/29/2019, 4:00 PM - 5:30 PM	CC-206
Section on Statistical Learning and Data Science (SLDS) Business Meeting — Other Cmte/Business
Section on Statistical Learning and Data Science
Chair(s): Tian Zheng, Columbia University

274 !	Tue, 7/30/2019, 8:30 AM - 10:20 AM	CC-201
Macroeconomic Forecasting and Policy in Data Rich Digital Age Environments — Invited Papers
Business and Economic Statistics Section, Section on Risk Analysis, Section on Statistical Learning and Data Science
Organizer(s): Arnab Bhattacharjee, Heriot-Watt University
Chair(s): Liqian Cai, Liberty Mutual
8:35 AM	Some High-Diemnesional Techniques for Analyzing Spatial and Other Complex Economic Data Taps Maiti, Michigan State University
8:55 AM	Prediction and Causal Inference Using Linear Regularized Regression with an Application to Commuting in Ireland Achim Ahrens, Economic and Social Research Institute; Christian B Hansen, University of Chicago Booth School of Business; Mark E Schaffer, Heriot-Watt University
9:15 AM	Financial Stress Scenario Development in a Data-Rich Environment - a Practitioner's View Xin Wang, HSBC/ IHS Markit
9:35 AM	Google Trends and the Macroeconomy: a Bayesian Mixed Frequency Approach Arnab Bhattacharjee, Heriot-Watt University; David Kohns, Heriot-Watt University
9:55 AM	Inference in High-Dimensional Models Without Regularization Ying Zhu, UC San Diego; Kaspar Wuthrich, UC San Diego
10:15 AM	Floor Discussion

278 * !	Tue, 7/30/2019, 8:30 AM - 10:20 AM	CC-504
Emerging Ideas in Predictive Inference — Invited Papers
Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, Section on Statistical Computing
Organizer(s): Lucas Mentch, University of Pittsburgh
Chair(s): Yifan Cui, University of Pennsylvania
8:35 AM	Predictive Inference with Random Forests Lucas Mentch, University of Pittsburgh
9:00 AM	Forward Stability and Model Path Selection Nicholas Kissel, University of Pittsburgh; Lucas Mentch, University of Pittsburgh
9:25 AM	Relaxing the Assumptions of Model-X Knockoffs Lucas Janson, Harvard University; Dongming Huang, Harvard University
9:50 AM	Recent Advances in Conformal Prediction Presentation Larry Wasserman, Carnegie Mellon University
10:15 AM	Floor Discussion

279 * !	Tue, 7/30/2019, 8:30 AM - 10:20 AM	CC-207
Bioinformatics: Accomplishments and Challenges — Invited Papers
Caucus for Women in Statistics, Section on Statistics in Genomics and Genetics, Section on Statistical Learning and Data Science
Organizer(s): Nusrat Jahan, James Madison University
Chair(s): Nusrat Jahan, James Madison University
8:35 AM	Optimal Permutation Recovery and Estimation of Bacterial Growth Dynamics Presentation Hongzhe Li, University of Pennsylvania
9:00 AM	Estimating Somatic Variant Richness in the Cancer Genome Ronglai Shen, Memorial Sloan-Kettering Cancer Center; Saptarshi Chakraborty, Memorial Sloan-Kettering Cancer Center; Colin Begg, Memorial Sloan Kettering Cancer Center
9:25 AM	Integrative Network Modeling Approaches to Precision Cancer Medicine Kim-Anh Do, University of Texas M.D. Anderson Cancer Center
9:50 AM	Statistical Inference of Chromatin 3D Structures from DNA Methylation Data Shili Lin, The Ohio State University
10:15 AM	Floor Discussion

289 * !	Tue, 7/30/2019, 8:30 AM - 10:20 AM	CC-708
Assessing the Quality of Integrated Data — Topic Contributed Papers
Government Statistics Section, Survey Research Methods Section, Section on Statistical Learning and Data Science
Organizer(s): Lisa Mirel, CDC/NCHS
Chair(s): Jeffrey Gonzalez, Bureau of Labor Statistics
8:35 AM	Practical Diagnostic Tools for Data Linkage Method Presentation MoonJung Cho, U.S. Bureau of Labor Statistics; Justin McIllece, U.S. Bureau of Labor Statistics
8:55 AM	Balancing Data Confidentiality and Research Needs: NCHS Linked Mortality Files Lisa Mirel, CDC/NCHS; Cordell Golden, CDC/NCHS/OAE/SPB; Cindy Zhang, CDC/NCHS/OAE/SPB
9:15 AM	Tools for Evaluating Quality of State and Local Administrative Data Presentation Zachary H Seeskin, NORC at the University of Chicago; Gabriel Ugarte, NORC at the University of Chicago; Rupa Datta, NORC at the University of Chicago
9:35 AM	The Implications of Misreporting for Longitudinal Studies of SNAP Erik Scherpf, USDA Economic Research Service; Brian Stacy , USDA Economic Research Service
9:55 AM	Discussant: Jennifer Parker, CDC/NCHS/OAE/SPB
10:15 AM	Floor Discussion

294	Tue, 7/30/2019, 8:30 AM - 10:20 AM	CC-502
SPEED: Statistical Learning and Data Science Speed Session 2, Part 1 — Contributed Speed
Section on Statistical Learning and Data Science, Text Analysis Interest Group
Chair(s): Sumanta Basu, Cornell University
Poster Presentations for this session.
8:35 AM	Three-Dimensional Radial Visualization of High-Dimensional Continuous or Discrete Data Presentation Yifan Zhu, Iowa State University; Fan Dai, Iowa State University; Ranjan Maitra, Iowa State University
8:40 AM	Semi-Supervised, Dynamic Class-Informative Feature Learning Presentation 1 Presentation 2 Presentation 3 Presentation 4 Vincent Pisztora
8:45 AM	An Imputation Approach for Fitting Random Survival Forests with Interval-Censored Survival Data Presentation Warren Keil; Tyler Cook, University of Central Oklahoma
8:50 AM	Diagnostic Accuracy Evaluation of Diagnostic Assessment Model in Longitudinal Data: a Simulation Study of Neural Network Approach Presentation Chi Chang, Michigan State University; Harlan McCaffery, University of Michigan
8:55 AM	Smoothing Random Forest Presentation Benjamin LeRoy, Carnegie Mellon University; Max G'Sell, Carnegie Mellon University
9:00 AM	Aggregated Pairwise Classification of Statistical Shapes Presentation 1 Presentation 2 Presentation 3 Min Ho Cho, The Ohio State University
9:05 AM	Statistical Optimality of Interpolated Nearest Neighbor Algorithms Presentation Yue Xing, Purdue University; Qifan Song, Purdue University; Guang Cheng, Purdue Statistics
9:10 AM	Ground Truth? Understanding How Humans Label Records and the Impact of Uncertainty Presentation 1 Presentation 2 Kayla Frisoli, Carnegie Mellon University; Rebecca Nugent, Carnegie Mellon University
9:15 AM	Block-Wise Partitioning for Extreme Multi-Label Classification Presentation Yuefeng Liang, UC Davis; Thomas C. M. Lee, UC Davis; Cho-Jui Hsieh, UCLA
9:20 AM	A Statistical Model for Tropical Cyclone Genesis and Assessing Its Differences Between Basins and Climates Presentation Arturo Fernandez, University of California - Berkeley
9:30 AM	Discovery of Gene Regulatory Networks Using Adaptively Selected Gene Perturbation Experiments Presentation Michele Zemplenyi, Harvard University; Jeffrey Miller, Harvard TH Chan School of Public Health
9:35 AM	Stagewise Generalized Estimating Equations for Varying Coefficient Models Gregory Vaughan, Bentley University; Yicheng Kang, Bentley University
9:40 AM	Stacked Ensemble Learning for Propensity Score Methods in Observational Studies Presentation Maximilian Autenrieth, San Diego State University and Ulm University; Richard Levine, San Diego State University; Juanjuan Fan, San Diego State University; Maureen Guarcello, San Diego State University
9:45 AM	Predicting Sub-Cellular Location of Plant Protein Using Supervised Machine Learning Presentation David Arthur; Benjamin Annan, Youngstown State University; Eric Quayson, Youngstown State University; Jack Min, Youngstown State University; Guang-Hwa Andy Chang, Youngstown State University
9:50 AM	Floor Discussion

307	Tue, 7/30/2019, 8:30 AM - 10:20 AM	CC-505
Novel Approaches for Analyzing Dynamic Networks — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Joshua Cape, Johns Hopkins University
8:35 AM	Random Graph Hidden Markov Models for Percolation in Noisy Dynamic Networks Xiaojing Zhu; Eric Kolaczyk, Boston University; Heather Shappell, Johns Hopkins University
8:50 AM	Bayesian Estimation of the Latent Dimension and Communities in Stochastic Blockmodels Francesco Sanna Passino, Imperial College London; Nicholas A. Heard, Imperial College London
9:05 AM	Anomaly Detection in Time-Varying Networks Presentation Lata Kodali, Virginia Tech; Leanna House, Virginia Tech; Srijan Sengupta, VIrginia Tech; William H. Woodall, Virginia Tech
9:20 AM	Estimating Latent Space Models for Network Data with Multivariate Response Variables Presentation Xuefei Zhang, University of Michigan; Ji Zhu, University of Michigan; Gongjun Xu, University of Michigan
9:35 AM	Developing New Statistical Pattern Recognition and System Identification Techniques for Partial Discharge Analysis Presentation 1 Presentation 2 Pramoda Sachinthana Jayasinghe, University of Manitoba; Mohammad Jafari Jozani, University of Manitoba; Behzad Kordi, University of Manitoba
9:50 AM	Nonparametric Anomaly Detection on Time Series of Graphs Presentation Dorcas Ofori-Boateng; Yulia Gel, University of Texas at Dallas; Ivor Cribben, University of Alberta
10:05 AM	Dynamic Stochastic Mirror Descent with Statistical Applications Shih-Kang Chao, University of Missouri-Columbia; Guang Cheng, Purdue Statistics

328 * !	Tue, 7/30/2019, 10:30 AM - 12:20 PM	CC-112
Integrative Approaches for Statistical Analysis of Data from Multiple Sources — Topic Contributed Papers
ENAR, Section on Statistical Learning and Data Science, Biometrics Section
Organizer(s): Irina Gaynanova, Texas A&M Univeristy
Chair(s): Irina Gaynanova, Texas A&M Univeristy
10:35 AM	Dynamic Systems Approach to Deep Learning with Different Types of Data Sets and Its Application to Prediction of Alzheimer’s Disease Presentation Momiao Xiong, University of Texas School of Public Health; Helen Engle, University of Texas School of Public Health; Yuanyuan Liu, University of Texas School of Public Health; Zhouxuan Li, University of Texas School of Public Health; Qiyang Ge, University of Texas School of Public Health; Shudi Li, University of Texas School of Public Health; Shan Liu, University of Texas School of Public Health
10:55 AM	Data Integration Using Joint and Individual Non-Gaussian Component Analysis Presentation Benjamin Risk, Emory University; Irina Gaynanova, Texas A&M Univeristy
11:15 AM	Integrative Factorization of Bidimensionally Linked Matrices Presentation 1 Presentation 2 Eric Lock, University of Minnesota; Jun Young Park, University of Minnesota
11:35 AM	SIDA: a New Discriminant Analysis Method for Multi-Type, Multi-Class Data Sandra Safo, University of Minnesota; Eun Jeong Min, University of Pennsylvania
11:55 AM	Targeted Integrative Learning via a Distance Segmented Regression Kun Chen, University of Connecticut; Yang Song, Vertex Pharmaceuticals Inc.; Biju Wang, University of Connecticut
12:15 PM	Floor Discussion

329	Tue, 7/30/2019, 10:30 AM - 12:20 PM	CC-704
SLDS Student Paper Awards — Topic Contributed Papers
Section on Statistical Learning and Data Science
Organizer(s): Ali Shojaie, University of Washington
Chair(s): Genevera Allen, Rice University
10:35 AM	Learning Optimal Individualized Decision Rules with Risk Control Zhengling Qi
10:55 AM	Joint Association and Classification Analysis of Multi-View Data Yunfeng Zhang, Texas A&M University; Irina Gaynanova, Texas A&M Univeristy
11:15 AM	Community Detection with Dependent Connectivity Presentation Yubai Yuan, University of Illinois at Urbana-Champaign; Annie Qu, University of Illinois at Urbana-Champaign
11:35 AM	Nonlinear Variable Selection via Deep Neural Networks Presentation Yao Chen, Purdue University; Qingyi Gao, Purdue University; Faming Liang, Purdue University; Xiao Wang, Purdue University
11:55 AM	Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization Presentation Michael Weylandt, Rice University; John Nagorski, Rice University, Department of Statistics; Genevera Allen, Rice University
12:15 PM	Floor Discussion

350	Tue, 7/30/2019, 10:30 AM - 12:20 PM	CC-706
New Methods for Time Series and Longitudinal Data — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Jean De Dieu Tapsoba, Fred Hutchinson Cancer Research Center
10:35 AM	Regularized Estimation of VAR_X Models Sagnik Halder
10:50 AM	An Efficient Two Step Algorithm for High-Dimensional Change Point Regression Models Without Grid Search Presentation Abhishek Kaul, Washington State University; Venkata K Jandhyala, Washington State University; Stergios B Fotopoulos, Washington State University
11:05 AM	Joint Estimation of Structured Multivariate VAR Modeling Peiliang Bai, University of Florida; George Michailidis, University of Florida
11:20 AM	Root Cause Detection Among Anomalous Time Series Using Temporal State Alignment Sayan Chakraborty, Zillow Group Inc.
11:35 AM	Recurrent Neural Networks for ARMA Model Selection Bei Chen, IBM Research; Beat Buesser, IBM Research; Kelsey DiPietro, University of Notre Dame
11:50 AM	Time Series Analysis with Unsupervised Learning Meihui Guo, National Sun Yat-Sen University; Ke-Jie Chen, National Sun Yat-sen University; Cheng Han Chua, National Sun Yat-sen University
12:05 PM	Classification of Longitudinal Unbalanced Data: Growth Mixture Models Vs Conventional Cluster Analysis on Approximated Values at Common Time Points Mosammat Tanbin; Benjamin E. Leiby, Thomas Jefferson University; Md Jobayer Hossain, Nemours children Healthcare Systems

353	Tue, 7/30/2019, 10:30 AM - 11:15 AM	CC-Hall C
SPEED: Statistical Learning and Data Science Speed Session 2, Part 2 — Contributed Poster Presentations
Section on Statistical Learning and Data Science, Text Analysis Interest Group
Chair(s): Ali Shojaie, University of Washington
Oral Presentations for this session.
1:	Three-Dimensional Radial Visualization of High-Dimensional Continuous or Discrete Data Yifan Zhu, Iowa State University; Fan Dai, Iowa State University; Ranjan Maitra, Iowa State University
3:	An Imputation Approach for Fitting Random Survival Forests with Interval-Censored Survival Data Warren Keil; Tyler Cook, University of Central Oklahoma
4:	Diagnostic Accuracy Evaluation of Diagnostic Assessment Model in Longitudinal Data: a Simulation Study of Neural Network Approach Chi Chang, Michigan State University; Harlan McCaffery, University of Michigan
5:	Smoothing Random Forest Benjamin LeRoy, Carnegie Mellon University; Max G'Sell, Carnegie Mellon University
6:	Aggregated Pairwise Classification of Statistical Shapes Min Ho Cho, The Ohio State University
7:	Statistical Optimality of Interpolated Nearest Neighbor Algorithms Yue Xing, Purdue University; Qifan Song, Purdue University; Guang Cheng, Purdue Statistics
8:	Ground Truth? Understanding How Humans Label Records and the Impact of Uncertainty Kayla Frisoli, Carnegie Mellon University; Rebecca Nugent, Carnegie Mellon University
9:	Block-Wise Partitioning for Extreme Multi-Label Classification Yuefeng Liang, UC Davis; Thomas C. M. Lee, UC Davis; Cho-Jui Hsieh, UCLA
10:	A Statistical Model for Tropical Cyclone Genesis and Assessing Its Differences Between Basins and Climates Arturo Fernandez, University of California - Berkeley
11:	Discovery of Gene Regulatory Networks Using Adaptively Selected Gene Perturbation Experiments Michele Zemplenyi, Harvard University; Jeffrey Miller, Harvard TH Chan School of Public Health
12:	Stagewise Generalized Estimating Equations for Varying Coefficient Models Gregory Vaughan, Bentley University; Yicheng Kang, Bentley University
13:	Stacked Ensemble Learning for Propensity Score Methods in Observational Studies Maximilian Autenrieth, San Diego State University and Ulm University; Richard Levine, San Diego State University; Juanjuan Fan, San Diego State University; Maureen Guarcello, San Diego State University
14:	Predicting Sub-Cellular Location of Plant Protein Using Supervised Machine Learning David Arthur; Benjamin Annan, Youngstown State University; Eric Quayson, Youngstown State University; Jack Min, Youngstown State University; Guang-Hwa Andy Chang, Youngstown State University
15:	Semi-Supervised, Dynamic Class-Informative Feature Learning Vincent Pisztora
Oral Presentations for this session.

380 * !	Tue, 7/30/2019, 2:00 PM - 3:50 PM	CC-301
Curious Roles of Latent Variables in Prediction and Inference — Invited Papers
Mental Health Statistics Section, Section on Statistical Learning and Data Science, Biometrics Section
Organizer(s): Booil Jo, Stanford University
Chair(s): Xiao-Li Meng, Harvard University
2:05 PM	Integrated Principal Components Analysis Tiffany M Tang, University of California at Berkeley; Genevera Allen, Rice University
2:25 PM	Forecasting Future Smoking-Related Mortality in 69 Countries: The Vital Role of Latent Variables Yicheng Li, University of Washington; Adrian Raftery, University of Washington
2:45 PM	Latent Variables in Causal Inference: Interpretation and Challenges Presentation Tyler VanderWeele, Harvard University
3:05 PM	Discussant: Robert Tibshirani, Stanford University
3:25 PM	Discussant: Mark van der Laan, UC Berkeley
3:45 PM	Floor Discussion

385 !	Tue, 7/30/2019, 2:00 PM - 3:50 PM	CC-607
Leo Breiman Award — Invited Papers
Section on Statistical Learning and Data Science
Organizer(s): Ali Shojaie, University of Washington
Chair(s): Xiaotong Shen, University of Minnesota
2:05 PM	Restricted Boltzmann Machines and Truncated Gaussian Distributions Yichao Wu, The University of Illinois at Chicago
2:50 PM	Integrating "two Cultures" in Data Science: Predictability, Computability, and Stability (PCS) Presentation Bin Yu, UC Berkeley
3:35 PM	Floor Discussion

388 * !	Tue, 7/30/2019, 2:00 PM - 3:50 PM	CC-603
Building Bridges for Data Science Education — Invited Panel
Section on Statistics and Data Science Education, Section on Statistical Computing, Section on Statistical Learning and Data Science
Organizer(s): Mine Cetinkaya-Rundel, Duke University
Chair(s): Beth Chance, Cal Poly - San Luis Obispo
2:05 PM	Building Bridges for Data Science Education Presentation
Panelists:	Mine Cetinkaya-Rundel, Duke University Michael Posner, Villanova University Jeff Forbes, Duke University Andrea Danyluk, Williams College
3:45 PM	Floor Discussion

390 * !	Tue, 7/30/2019, 2:00 PM - 3:50 PM	CC-506
Advanced Fault Detection and Attribution in Large and Complex Data Streams — Topic Contributed Papers
Quality and Productivity Section, Section on Physical and Engineering Sciences, Section on Statistical Learning and Data Science
Organizer(s): Amanda S Hering, Baylor University
Chair(s): Amanda S Hering, Baylor University
2:25 PM	Multiple Tensor-On-Tensor Regression: An Approach for Modeling Processes with Heterogeneous Sources of Data Kamran Paynabar, Georgia Institute of Technology; Mostafa Resisi, Georgia Tech; Hao Yan, Arizona State University; Jianjun Shi, Georgia Tech
2:45 PM	A Fault Detection Strategy Based on Wavelet Multiscale Representation of the Process Fouzi Harrou, King Abdullah University of Science and Technology; Ying Sun, King Abdullah University of Science and Technology
3:05 PM	Fault Detection Using PCA at a Municipal Wastewater Treatment Facility Presentation Kathryn Blair Newhart, Colorado School of Mines; Tzahi Cath, Colorado School of Mines; Amanda S Hering, Baylor University
3:25 PM	Fault Attribution in a Complex, Nonstationary, and Temporally Dependent Wastewater Treatment System Presentation Molly Klanderman, Baylor University
3:45 PM	Floor Discussion

391 * !	Tue, 7/30/2019, 2:00 PM - 3:50 PM	CC-106
Leveraging Disparate Sources of Data and Machine Learning to Improve Causal Inference — Topic Contributed Papers
ENAR, Section on Statistical Learning and Data Science, Social Statistics Section
Organizer(s): Johann A Gagnon-Bartsch, University of Michigan; Jann Spiess, Postdoctoral Research, Microsoft Research
Chair(s): Johann A Gagnon-Bartsch, University of Michigan
2:05 PM	Transfer Learning for Estimating Causal Effects Using Neural Networks Soeren Kuenzel; Jasjeet Sekhon, UC Berkeley; Bradly Reinhold Stadie, UC Berkeley; Nikita Vemuri, UC Berkeley
2:25 PM	ReLOOP: Precise Unbiased Estimation in Randomized Experiments Using Observational Auxilliary Data Presentation Adam Sales, University of Texas At Austin; Johann A Gagnon-Bartsch, University of Michigan; Anthony Botelho, Worcester Polytechnic Institute; Neil T Heffernan, Worcester Polytechnic Institute; Edward Wu, University of Michigan; Luke Miratrix, Harvard University
2:45 PM	Machine Learning for Estimating Causal Effects from High-Dimensional Observational Data Presentation Fredrik Johansson, MIT
3:05 PM	Bayesian Inference for Sample Surveys in the Presence of High-Dimensional Auxiliary Information Presentation Yutao Liu, Columbia University; Andrew Gelman, Columbia University; Qixuan Chen, Columbia University
3:25 PM	Manipulation Proof Machine Learning Daniel Bjorkegren, Brown University; Joshua Blumenstock, University of California Berkeley
3:45 PM	Floor Discussion

392 !	Tue, 7/30/2019, 2:00 PM - 3:50 PM	CC-705
Large-Scale Data Analysis via Spectral Methods — Topic Contributed Papers
IMS, Section on Statistical Learning and Data Science
Organizer(s): Edgar Dobriban, University of Pennsylvania
Chair(s): Edgar Dobriban, University of Pennsylvania
2:05 PM	Bootstrapping Spectral Statistics in High Dimensions Miles Lopes, UC Davis; Alexander Aue, University of California, Davis; Andrew Blandino, UC Davis
2:25 PM	Unsupervised Ensemble Learning: a Spectral Approach Boaz Nadler, Weizmann Institute of Science
2:45 PM	Distributed Ridge Regression in High Dimensions Yue Sheng, University of Pennsylvania; Edgar Dobriban, University of Pennsylvania
3:05 PM	"Spectral Algorithms for High-Dimensional Data Analysis: What Have We Learned" Matan Gavish, Hebrew Univ of Jerusalem
3:25 PM	Joint Behavior of Large Autocovariance Matrices Presentation Arup Bose, Indian Statistical Institute
3:45 PM	Floor Discussion

413	Tue, 7/30/2019, 2:00 PM - 3:50 PM	CC-712
Network Analysis and Network-Based Modeling — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Boxiang Wang, University of Iowa
2:05 PM	Mixed Network Modeling for Network Simulation Presentation Fairul Mohd-Zaid, Air Force Research Labs; Wright Shamp, Florida State University
2:20 PM	Two-Stage Spectral Co-Clustering for Matched Communities Hyesun Yoo, University of Michigan; Ji Zhu, University of Michigan
2:35 PM	Second-Order Models for Exchangeable Relational Data Presentation 1 Presentation 2 Presentation 3 Frank Marrs, Colorado State University; Bailey Fosdick, Colorado State University
2:50 PM	Network Heterogeneity and Strength of Connections Presentation Sandipan Roy, University of Bath; Subhadeep Mukhopadhyay, Temple University
3:05 PM	Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks Jesus Arroyo, Johns Hopkins University; Daniel L Sussman, Boston University; Carey E Priebe, Johns Hopkins University; Vince Lyzinski, University of Massachusetts Amherst
3:20 PM	Prediction from Networks with Node Features with Application to Neuroimaging Daniel Kessler, University of Michigan; Elizaveta Levina, University of Michigan; Keith Levin, University of Michigan
3:35 PM	Floor Discussion

444 !	Wed, 7/31/2019, 8:30 AM - 10:20 AM	CC-505
Modern and Practical Solutions to Difficult High-Dimensional Regression Problems — Invited Papers
Section on Statistical Computing, International Association for Statistical Computing, Section on Statistical Learning and Data Science
Organizer(s): Maryclare Griffin, Cornell University Center for Applied Mathematics
Chair(s): Andee Kaplan, Duke University
8:35 AM	Informative Priors for Clustering Amy H Herring, Duke University; Sally Paganin, University of Padova; Andrew Olshan, UNC-Chapel Hill
8:55 AM	Bayesian Function-On-Scalars Regression for High-Dimensional Data Daniel R Kowal, Rice University; Daniel Bourgeois, Rice University
9:15 AM	Computationally-Efficient High-Dimensional Interaction Modeling Guo Yu, University of Washington; Ryan Tibshirani, Carnegie Mellon University; Jacob Bien, University of Southern California
9:35 AM	Data-Adaptive Additive Modeling Presentation Ashley Petersen, University of Minnesota; Daniela Witten, University of Washington
9:55 AM	Discussant: Tian Zheng, Columbia University
10:15 AM	Floor Discussion

454 * !	Wed, 7/31/2019, 8:30 AM - 10:20 AM	CC-506
Recommender Systems and Large-Margin Machines: From Statistics Perspectives — Topic Contributed Papers
Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, WNAR
Organizer(s): Helen Zhang, University of Arizona
Chair(s): Helen Zhang, University of Arizona
8:35 AM	Flexible Low-Rank Statistical Modeling with Missing Data and Side Information Presentation Rahul Mazumder, MIT; William Fithian, University of California at Berkeley
8:55 AM	Two Improvements to the Matrix Factorization Approach for Recommender Systems Presentation Mu Zhu, University of Waterloo
9:15 AM	Smooth Recommender Systems Ben Dai, University of Minnesota; Xiaotong Shen, University of Minnesota; Annie Qu, University of Illinois at Urbana-Champaign
9:35 AM	Discussant: Feng Liang, University of Illinois at Urbana Champaign
9:55 AM	Discussant: Boxiang Wang, University of Iowa
10:15 AM	Floor Discussion

471	Wed, 7/31/2019, 8:30 AM - 10:20 AM	CC-507
Advances in High-Dimensional Inference and Multiple Testing — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Rina Friedberg, Stanford University
8:35 AM	Testing High-Dimensional Null Hypothesis Against High-Dimensional Alternative for Generalized Linear Models Jinsong Chen, University of Illinois at Chicago; Hua Yun Chen, University of Illinois at Chicago
8:50 AM	High-Dimensional Inference via Adaptive Bayes Jiapeng Liu, Purdue Unversity; Yixuan Qiu, Carnegie Mellon University; Xiao Wang, Purdue University
9:05 AM	Cross Validation Importance Learning Chenglong Ye, University of Minnesota; Yuhong Yang, University of Minnesota
9:20 AM	Two-Sample Tests for Graphs with Applications in Neuroscience Xixi Hu, Indiana University Bloomington; Michael Trosset, Indiana University Bloomington; Minh Tang, Johns Hopkins University
9:35 AM	Optimal and Maximin Procedures for Multiple Testing Problems Presentation Saharon Rosset, Tel Aviv University; Ruth Heller, Tel-Aviv University; Amichai Painsky, Hebrew University Jerusalem; Ehud Aharoni, IBM Research
9:50 AM	Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models Fei Wang, CarGurus; Ling Zhou, Southwestern University of Finance and Economics; Lu Tang, University of Pittsburgh; Peter X.K. Song , School of Public Health, University of Michigan
10:05 AM	Hypothesis Testing for Vectorized Persistence Diagrams Chul Moon, Southern Methodist University; Sangjin Kim, The University of Texas at El Paso

482 * !	Wed, 7/31/2019, 10:30 AM - 12:20 PM	CC-702
Statistical Methods in the Analysis of High-Order Structural Data with Possible Structural Changes — Invited Papers
Section on Statistical Learning and Data Science, International Chinese Statistical Association, ENAR
Organizer(s): Peter X.K. Song , School of Public Health, University of Michigan
Chair(s): Peter X.K. Song , School of Public Health, University of Michigan
10:35 AM	Tensor Regression and Imaging-Based Inference Presentation Heping Zhang, Yale University; Long Feng, Yale University; Xuan Bi, University of Minnesota
11:00 AM	Individualized Multilayer Tensor Learning with An Application in Imaging Analysis Xiwei Tang, University of Virginia; Xuan Bi, University of Minnesota; Annie Qu, University of Illinois at Urbana-Champaign
11:25 AM	Simultaneous Change Point Detection and Structure Recovery for High-Dimensional Gaussian Graphical Models Yufeng Liu, University of North Carolina at Chapel Hill
11:50 AM	Generative Link Prediction for Incomplete Networks with Node Features Ji Zhu, University of Michigan
12:15 PM	Floor Discussion

485 * !	Wed, 7/31/2019, 10:30 AM - 12:20 PM	CC-102
Decision Making in Tech Giants Through A/B Testing, Prediction and Optimization — Invited Papers
Quality and Productivity Section, Section on Physical and Engineering Sciences, Section on Statistical Learning and Data Science
Organizer(s): Tirthankar Dasgupta, Rutgers University
Chair(s): Anqi Zhao, National University of Singapore
10:35 AM	A Multi-Objective Optimization for Web Based Ranking Problems Souvik Ghosh, LinkedIn Corporation
11:00 AM	Improving External Validity of A/B Testing Using Jackknife Presentation Yu Wang, University of California, Berkeley; Somit Gupta, Microsoft Corporation; Jiannan Lu, Microsoft Corporation; Ali Mahmoudzadeh, Microsoft Corporation; Sophia Liu, Microsoft Corporation
11:25 AM	Limitations of Design-Based Causal Inference and A/B Testing Under Arbitrary and Network Interference Guillaume Basse, UC Berkeley; Edoardo Airoldi, Temple
11:50 AM	Discussant: Edoardo M Airoldi, Harvard University
12:15 PM	Floor Discussion

517	Wed, 7/31/2019, 10:30 AM - 12:20 PM	CC-701
Deep Learning: Advances and Applications — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Devin Francom, Los Alamos
10:35 AM	Reinforcement Learning as a Solution to Systematic Social Bias in Deep Learning Presentation 1 Presentation 2 Presentation 3 Kathleen Gatliffe, University of Colorado Denver; Audrey E Hendricks, University of Colorado Denver
10:50 AM	Deep Model-X Knockoff Generator Through Latent Variables Ying Liu, Medical College of Wisconsin; Cheng Zheng, University of Wisconsin at Milwakee
11:05 AM	Online Batch Decision Making with High-Dimensional Covariates Chi-Hua Wang, Purdue University; Guang Cheng, Purdue Statistics
11:20 AM	Uncertainty-Aware Black-Box Predictors with Coverage Guarantees Presentation Jean Feng, University of Washington; Arjun Sondhi, University of Washington; Jessica Perry, University of Washington; Noah Simon, University of Washington
11:35 AM	Signed Graph Neural Network Mohammadreza Armandpour, Texas A&M University; Debdeep Pati, Texas A&M University
11:50 AM	A Two-Stage Approach to Evaluate Predictive Accuracy of Deep Neural Networks Georgianna Campbell, Naval Information Warfare Center Atlantic; Emily Nystrom, Naval Information Warfare Center Atlantic; Hunter R. Lake, Naval Information Warfare Center Atlantic
12:05 PM	Semi-Supervised Sequence Learning Using Deep Generative Models with Applications to Healthcare Data Weijing Tang, University of Michigan; Ji Zhu, University of Michigan

544 * !	Wed, 7/31/2019, 2:00 PM - 3:50 PM	CC-506
Dynamic Graphical Models and Networks with Applications — Invited Papers
International Indian Statistical Association, Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Sharmodeep Bhattacharyya, Oregon State University
Chair(s): Sharmodeep Bhattacharyya, Oregon State University
2:05 PM	Mixed Membership Stochastic Blockmodels for Heterogeneous Networks Yuguo Chen, University of Illinois at Urbana-Champaign
2:20 PM	On the CUSUM Changepoint Estimator for Network Data Shirshendu Chatterjee, City University of New York, City College; Sharmodeep Bhattacharyya, Oregon State University; Peter J Bickel, University of California, Berkeley; Soumendu Sundar Mukherjee, Indian statistical Institute
2:35 PM	Inference in Vector Autoregressive Models with Union of Intersections for Sparse, Accurate, and Predictive Dynamic Causal Networks at Scale Kristofer Bouchard, Lawrence Berkeley National Laboratory
2:50 PM	Network Modeling of High-Dimensional Time Series Sumanta Basu, Cornell University
3:05 PM	Discussant: Peter J Bickel, University of California, Berkeley
3:20 PM	Discussant: Sofia C Olhede, University College London
3:25 PM	Floor Discussion

554 * !	Wed, 7/31/2019, 2:00 PM - 3:50 PM	CC-503
Interdisciplinary Research and Leadership: How to Make an Impact in the Data Science Age — Invited Panel
IMS, Section on Statistical Learning and Data Science, Royal Statistical Society
Organizer(s): Bin Yu, UC Berkeley
Chair(s): Bin Yu, UC Berkeley
2:05 PM	Interdisciplinary Research and Leadership: How to Make an Impact in the Data Science Age
Panelists:	Alicia Carriquiry, Iowa State University Christopher Genovese, Statistics, CMU Hongyu Zhao, Yale Jasjeet Sekhon, UC Berkeley Simon Tavare, Inst of Cancer Dynamics and Statistics, Columbia University Tamara Tamara Greasby, Data Science at The Trade Desk
3:45 PM	Floor Discussion

559 !	Wed, 7/31/2019, 2:00 PM - 3:50 PM	CC-301
Randomized Algorithms for Optimization Problems in Statistics — Topic Contributed Papers
Section on Statistical Learning and Data Science, IMS, Section on Statistical Computing
Organizer(s): Miles Lopes, UC Davis
Chair(s): Miles Lopes, UC Davis
2:05 PM	Statistical Properties of Stochastic Gradient Descent Presentation Panagiotis Toulis, University of Chicago Booth School of Business; Jerry Chee, University of Chicago
2:25 PM	Randomized Sparse PCA Using the Variable Projection Method N. Benjamin Erichson, Univ of California - Berkeley
2:45 PM	Randomized Linear Algebra and Its Applications in Second-Order Optimization and Deep Learning Presentation Zhewei Yao, UC Berkeley
3:05 PM	Understanding the Acceleration Phenomenon via High-Resolution Differential Equations Weijie Su, University of Pennsylvania
3:25 PM	Random Projections for Faster Non-Convex Optimization Mert Pilanci, Stanford University

567 * !	Wed, 7/31/2019, 2:00 PM - 3:50 PM	CC-103
Digital Phenotyping – What Can Wearables and Smartphones Tell Us About Our Mental Health? — Topic Contributed Papers
Mental Health Statistics Section, Section on Statistical Learning and Data Science, Biometrics Section
Organizer(s): Samprit Banerjee, Weill Medical College, Cornell University
Chair(s): Ivan Diaz, Weill Medical College, Cornell University
2:05 PM	Digital Phenotyping: Opportunities and Challenges Presentation Jukka-Pekka Onnela
2:25 PM	Biostatistical Methods for Wearable and Implantable Technology (WIT) Presentation Ciprian Crainiceanu, Johns Hopkins University
2:45 PM	Functional Data Analysis Approaches for Analyzing Mobile Health Data Jihui Lee, Weill Cornell Medicine; Samprit Banerjee, Weill Medical College, Cornell University
3:05 PM	Clustering of Functional Data to Discover Patterns of Behavioral Trajectories Using Smartphone Data Samprit Banerjee, Weill Medical College, Cornell University; Jihui Lee, Weill Cornell Medicine
3:25 PM	Modeling Smartphone-Based Social Communication with Circadian Trends Presentation Ian Barnett, University of Pennsylvania; Grace Choi, University of Pennsylvania
3:45 PM	Floor Discussion

568 * !	Wed, 7/31/2019, 2:00 PM - 3:50 PM	CC-703
Experimentation at Scale: Current Challenges in A/B Testing — Topic Contributed Panel
Section on Statistics in Marketing, Section on Statistical Learning and Data Science, Committee on Applied Statisticians
Organizer(s): Martin Tingley, Netflix
Chair(s): Dean Eckles, MIT
2:05 PM	Experimentation at Scale: Current Challenges in A/B Testing Presentation
Panelists:	David Afshartous, Amazon Eytan Bakshy, Facebook Kathy Zhong, Google Martin Tingley, Netflix
3:40 PM	Floor Discussion

585 * !	Thu, 8/1/2019, 8:30 AM - 10:20 AM	CC-102
Exploiting Latent Structure for Network Inference — Invited Papers
Section on Statistical Computing, Section on Statistical Learning and Data Science, Section on Bayesian Statistical Science
Organizer(s): Avanti Athreya, Johns Hopkins University
Chair(s): Minh Tang, Johns Hopkins University
8:35 AM	Leveraging Exchangeability Assumptions to Make Inference in Regression with Network Outcomes Bailey Fosdick, Colorado State University
9:00 AM	Overlapping Clustering Models, and One (Class) SVM to Bind Them All. Purnamrita Sarkar, University of Texas, Austin
9:25 AM	'Statistics 101' for Network Data Objects Eric Kolaczyk, Boston University
9:50 AM	Consistency in Vertex Nomination Vince Lyzinski, University of Massachusetts Amherst
10:15 AM	Floor Discussion

588 !	Thu, 8/1/2019, 8:30 AM - 10:20 AM	CC-203
Statistical Analysis of Tensor Data — Invited Papers
Section on Nonparametric Statistics, ENAR, Section on Statistical Learning and Data Science
Organizer(s): Xin Zhang, Florida State University
Chair(s): Bing Li, The Pennsylvania State University
8:35 AM	Tensor Clustering for Dynamic Functional Connectivity Analysis Presentation Will Wei Sun, Purdue University; Lexin Li, University of California at Berkeley
8:55 AM	ISLET: Fast and Optimal Low-Rank Tensor Regression via Importance Sketching Anru Zhang, University of Wisconsin-Madison; Yuetian Luo, University of Wisconsin-Madison; Garvesh Raskutti, University of Wisconsin-Madison; Ming Yuan, Columbia University
9:15 AM	Getting Multiway Arrays in Order with Co-Manifold Learning Eric Chi, North Carolina State University; Gal Mishne, Yale University; Ronald Coifman, Yale University
9:35 AM	Covariate-Adjusted Tensor Classification in High Dimensions Qing Mai, Florida State University
9:55 AM	Model-Based Clustering of Tensor Data Xin Zhang, Florida State University
10:15 AM	Floor Discussion

591 !	Thu, 8/1/2019, 8:30 AM - 10:20 AM	CC-502
Recent Advances in the Bayesian Modeling of Large Scale Neuroimaging Data for Brain Activation and Connectivity — Invited Papers
Section on Bayesian Statistical Science, Section on Statistics in Imaging, Section on Statistical Learning and Data Science
Organizer(s): Rajarshi Guhaniyogi, University of California, SC
Chair(s): Donatello Telesca, UCLA
8:35 AM	Multi-Scale Factor Analysis of High-Dimensional Connectivity in Brain Networks Presentation 1 Presentation 2 Presentation 3 Hernando Ombao, King Abdullah University of Science and Technology (KAUST); Chee-Ming Ting, KAUST
9:00 AM	Bayesian Approaches for Dynamic Brain Connectivity Michele Guindani, University of California, Irvine; Marina Vannucci, Rice University; Erik Erhardt, University of New Mexico
9:25 AM	Bayesian Supervised Tensor Modeling for Large Scale Imaging Data Rajarshi Guhaniyogi, University of California, SC
9:50 AM	On the Bayesian Spatial Analysis of Brain Activation in fMRI John Kornak, University of California, San Francisco
10:15 AM	Floor Discussion

597 * !	Thu, 8/1/2019, 8:30 AM - 10:20 AM	CC-503
Vision 2020: Making Impact with Statistics in the Era of Data Science — Invited Panel
Committee of Presidents of Statistical Societies, ENAR, Section on Statistical Learning and Data Science
Organizer(s): Huixia Judy Wang, The George Washington University
Chair(s): Bhramar Mukherjee, University of Michigan
8:35 AM	Vision 2020: Making Impact with Statistics in the Era of Data Science Presentation
Panelists:	Hadley Wickham, RStudio Jeffrey Leek, Johns Hopkins Bloomberg School of Public Health John Quackenbush, Harvard University Rachel Schutt, BlackRock Tian Zheng, Columbia University Xiao-Li Meng, Harvard University
10:15 AM	Floor Discussion

598 * !	Thu, 8/1/2019, 8:30 AM - 10:20 AM	CC-113
Statistical Learning with Unconventional Missing Data — Topic Contributed Papers
International Chinese Statistical Association, Section on Statistical Learning and Data Science, IMS
Organizer(s): Gen Li, Columbia University
Chair(s): Jiayi Ji, Icahn School of Medicine at Mount Sinai
8:35 AM	Generalized Integrative Principal Component Analysis for Multi-Type Data with Block-Wise Missing Structure Presentation Gen Li, Columbia University; Eric Lock, University of Minnesota; Huichen Zhu, Columbia University
8:55 AM	How Not to Estimate the Nonignorable Missingness Mechanism Jiwei Zhao, State University of New York At Buffalo
9:15 AM	Using Multivariate Mixed-Effects Selection Models for Analyzing Batch-Processed Proteomics Data with Non-Ignorable Missingness Presentation Lin Chen, University of Chicago; Jiebiao Wang, Carnegie Mellon University; Pei Wang, Icahn School of Medicine at Mount Sinai; Donald Hedeker, University of Chicago
9:35 AM	Floor Discussion

600 !	Thu, 8/1/2019, 8:30 AM - 10:20 AM	CC-111
Less Can Be More: Smart Sampling in Data and Engineering Sciences — Topic Contributed Papers
Section on Physical and Engineering Sciences, Quality and Productivity Section, Section on Statistical Learning and Data Science
Organizer(s): Xinwei Deng, Virginia Tech; C. Devon Lin, Queen's University
Chair(s): Xinwei Deng, Virginia Tech
8:35 AM	Replication or Exploration? Sequential Design for Stochastic Simulation Experiments Presentation Robert Gramacy, Virginia Tech; Mickael Binois, Argonne National Laboratory; Jiangeng Huang, Virginia Tech; Mike Ludkovsku, UC Santa Barbara
8:55 AM	Choosing the Best Partition for the Output from a Large-Scale Simulation Presentation Emily Casleton, Los Alamos National Laboratory; Chelsea Challacombe, University of California-San Diego; Jonathan Woodring, Los Alamos National Laboratory
9:15 AM	Support Points: An Optimal and Model-Free Method for Subsampling Big Data Roshan Vengazhiyil, Georgia Institute of Technology; Simon Mak, Georgia Institute of Technology
9:35 AM	Varying Coefficient Frailty Models with Applications in Single Molecular Experiments Presentation 1 Presentation 2 Presentation 3 Jiazhao Zhang, Rutgers University; Ying Hung, Rutgers University; Tirthankar Dasgupta, Rutgers University
9:55 AM	Meta-Modeling for ICU Contamination Transmission Simulations: Using Smart Sampling and Machine Learning to Link Data to Simulation Parameters Presentation Ben Haaland, University of Utah; Damon Toth, University of Utah; Molly Leecaster, University of Utah
10:15 AM	Floor Discussion

601 *	Thu, 8/1/2019, 8:30 AM - 10:20 AM	CC-705
Recent Advances in Variable Selection for Linear and Nonlinear Models — Topic Contributed Papers
Biometrics Section, Section on Statistical Learning and Data Science, IMS
Organizer(s): Marinela Capanu, Memorial Sloan Kettering Cancer Center
Chair(s): Colin Begg, Memorial Sloan Kettering Cancer Center
8:35 AM	Optimized Variable Selection via Repeated Data Splitting Presentation Marinela Capanu, Memorial Sloan Kettering Cancer Center; Colin Begg, Memorial Sloan Kettering Cancer Center; Mithat Gonen, Memorial Sloan Kettering Cancer Center
8:55 AM	Thresholding Least-Squares for High-Dimensional Regression Models Presentation Mihai Giurcanu
9:15 AM	Metropolized Knockoff Sampling Presentation Stephen Bates, Stanford; Emmanuel Candes, Stanford University; Lucas Janson, Harvard University; Wenshuo Wang, Harvard University
9:35 AM	Nonuniformity of P-Values Can Occur Early in Diverging Dimensions Presentation Emre Demirkaya, University of Southern California
9:55 AM	Model Selection Bias Invalidates Goodness of Fit Tests Presentation Joshua Loftus, New York University
10:15 AM	Floor Discussion

602 * !	Thu, 8/1/2019, 8:30 AM - 10:20 AM	CC-201
Game Analytics: How Data Science Transforms the Game Industry — Topic Contributed Papers
Section on Statistical Learning and Data Science, Section on Statistics in Marketing, Committee on Applied Statisticians, Business Analytics/Statistics Education Interest Group
Organizer(s): Qiaolin Chen, Tencent
Chair(s): Dong Xi, Novartis
8:35 AM	Machine Learning and Big Data Analytics at Tencent Games Qiaolin Chen; Xu Cheng, Tencent; Jiachun Du, Tencent; Botao Li, Tencent; Zeng Zhao, Tencent
8:55 AM	Combining Advanced Statistics and Machine Learning to Improve Games at Ubisoft Presentation Antoine Rebecq; Jean-Michel Daignan, Ubisoft
9:15 AM	Product Diffusion on a Dynamic Matching Platform: The Case of a MMOG Chenyu Yang, University of Rochester
9:35 AM	Online Skill Rating Algorithms Nicolas Grenon-Godbout; Jonathan Dumas , Ubisoft; Simon Fontaine, Ubisoft; Gabrielle Rit, Ubisoft; Timothy Park, Ubisoft
9:55 AM	Discussant: Xiaoyang Yang, Riot Games
10:15 AM	Floor Discussion

611	Thu, 8/1/2019, 8:30 AM - 10:20 AM	CC-103
Applications in Business and Markets — Contributed Papers
Section on Statistical Learning and Data Science, Text Analysis Interest Group
Chair(s): Ya-Hui Kate Hsu, Celgene
8:35 AM	Using Simple Descriptive Statistics to Drive Critical Decision Making Peter John De Chavez, PepsiCo
8:50 AM	Interactive Visualization for Predictive Analytics Mia L. Stephens, SAS Institute / JMP Division; Ruth Hummel, SAS Institute, JMP Division
9:05 AM	For the Love of Crocs: Text Mining Product Reviews Presentation Ruth Hummel, SAS Institute, JMP Division; Mia L. Stephens, SAS Institute / JMP Division
9:20 AM	Customer Classification Using XGBoost: Accurate and Scalable Prediction of Customer Cluster Membership Presentation Joseph Retzer, ACT-MRSolutions; Ewa Nowakowska, ey
9:35 AM	Analysis of Break-Points in Non-Stationary Time Series Jean Remy Habimana, University of Arkansas
9:50 AM	Impact of Exports and Imports on Economic Growth of Nepal Presentation Mitra Lal Devkota, University of North Georgia; Humnath Panta, Brenau University
10:05 AM	Floor Discussion

618	Thu, 8/1/2019, 8:30 AM - 10:20 AM	CC-101
Machine Learning for Big Data — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Chad He, Fred Hutchinson Cancer Research Center
8:35 AM	ON SUPPLEMENTING TRAINING DATA BY HALF-SAMPLING Presentation William Heavlin, Google, Inc.
8:50 AM	Complexity Analysis for Glucose Dynamics Presentation Xiaohua Douglas Zhang, University of Macau
9:05 AM	Integrative OMICs Analysis in Quantifying Tissue Specificity Meng Wang, Stanford University; Lihua Jiang, Stanford University; Hua Tang, Stanford University; Michael Snyder, Stanford University
9:20 AM	Patient Factors at Diagnosis and Overall Risk of Mortality in US Population-Based Pediatric Oncology: An Evaluation Using SEER Data Fatima Boukari, Delaware State University; Md Jobayer Hossain, Nemours children Healthcare Systems
9:35 AM	Using Smart Card Data to Quantify the Disruption Impact on Urban Metro Systems Nan Zhang, Imperial College London; Daniel Graham, Imperial College London; Jose M. Carbo, Imperial College London; Daniel Hörcher, Imperial College London
9:50 AM	Relative Importance of Predictors of Artificial Neural Network Modeling Results with Applications to Evaluating Vasopressor Treatments for Subarachnoid Hemorrhage (SAH) Patients Presentation Duo Yu, University of Texas Health Science Center at Houston; Hulin MI Wu, University of Texas Health Science Center at Houston
10:05 AM	Floor Discussion

629 * !	Thu, 8/1/2019, 10:30 AM - 12:20 PM	CC-207
The Impacts of Measurement Error in Scientific Discoveries — Invited Papers
Section on Statistics in Epidemiology, Biometrics Section, Section on Statistical Learning and Data Science
Organizer(s): Xiangrong Kong, Johns Hopkins University
Chair(s): Kellie Archer, Ohio State University
10:35 AM	Covariate Measurement Error Models, Past Developments and Modern Advancements Presentation Jeffrey S Buzas, University of Vermont
10:50 AM	Weighted Causal Inference Methods with Misclassified Outcomes Grace Yi, University of Waterloo
11:05 AM	Bayesian Adjustment for Measurement Error: Bridging the Gap Between Concepts and Scientific Impact Paul Gustafson, University of British Columbia
11:20 AM	Measurement Error Correction for Change in Nutrient Intake Presentation Bernard Rosner, Channing Division of Network Medicine, Harvard Medical School
11:35 AM	The Centrality of Measurement Error Modeling to Advances in Nutritional Epidemiology Presentation Sharon I. Kirkpatrick, University of Waterloo
11:50 AM	Discussant: Leonard Stefanski, NCSU
12:05 PM	Floor Discussion

630 * !	Thu, 8/1/2019, 10:30 AM - 12:20 PM	CC-111
Machine Learning in the Criminal Justice System — Invited Papers
Committee on Law and Justice Statistics, Statistics and Public Policy, Section on Statistical Learning and Data Science
Organizer(s): Ben Wender
Chair(s): Alfred O. Hero, University of Michigan
10:35 AM	Do We Need Black Box Models in Criminal Justice? Presentation Cynthia Rudin, Duke University
11:25 AM	Discussant: Alicia Carriquiry, Iowa State University
12:15 PM	Floor Discussion

633	Thu, 8/1/2019, 10:30 AM - 12:20 PM	CC-102
Foundations of Data Science: Privacy-Preserving Inference — Invited Papers
Business and Economic Statistics Section, Royal Statistical Society, IMS, Section on Statistical Learning and Data Science
Organizer(s): Sofia C Olhede, University College London
Chair(s): Guy Nason, University of Bristol
11:00 AM	Privacy-Preserving Technologies Meet Machine Learning Presentation Jeannette Wing, Columbia University, Data Science Institute
11:25 AM	Privacy-Preserving Prediction Presentation 1 Presentation 2 Presentation 3 Cynthia Dwork, Harvard University; Vitaly Feldman, Google
11:50 AM	Discussant: Patrick J Wolfe, Purdue University
12:15 PM	Floor Discussion

634 !	Thu, 8/1/2019, 10:30 AM - 12:20 PM	CC-502
Recent Advancements in Distance and Kernel-Based Metrics and Related Learning Methods — Invited Papers
Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, National Science Foundation
Organizer(s): Shubhadeep Chakraborty, Texas A&M University
Chair(s): Soutrik Mandal, National Cancer Institute
10:35 AM	Generalizing Distance Covariance to Measure and Test Multivariate Mutual Dependence via Complete and Incomplete V-Statistics David Matteson, Cornell University; Ze Jin, Facebook
11:00 AM	A New Framework for Distance Metrics in High Dimension Presentation 1 Presentation 2 Xianyang Zhang, Texas A&M University; Shubhadeep Chakraborty, Texas A&M University
11:25 AM	Classification with Imperfect Training Labels Presentation Timothy I. Cannings, University of Edinburgh; Yingying Fan, University of Southern California; Richard Samworth, University of Cambridge
11:50 AM	Distance Metrics for Measuring Joint Dependence with Application to Causal Inference Shubhadeep Chakraborty, Texas A&M University; Xianyang Zhang, Texas A&M University
12:15 PM	Floor Discussion

639 * !	Thu, 8/1/2019, 10:30 AM - 12:20 PM	CC-503
Women in Data Science: a Small N Sample — Invited Panel
Section for Statistical Programmers and Analysts, Section on Statistical Learning and Data Science, Caucus for Women in Statistics
Organizer(s): Maria A Terres, Waymo
Chair(s): Maria A Terres, Waymo
10:35 AM	Women in Data Science: a Small N Sample Presentation
Panelists:	Moorea Brega, Pattern Ag Molly Davies, Stitch Fix Mary Beth Broadbent, Google/YouTube Cheryl Flynn, AT&T Research Labs Clara Yuan, Convoy Inc.
12:05 PM	Floor Discussion

641 * !	Thu, 8/1/2019, 10:30 AM - 12:20 PM	CC-109
Recent Advances in Density Mixture Modeling and EM-Like Algorithms: Frequentist and Bayesian Views — Topic Contributed Papers
Section on Nonparametric Statistics, Section on Statistical Learning and Data Science, International Indian Statistical Association
Organizer(s): Michael Levine, Purdue University
Chair(s): Matthew Reimherr, Penn State University
10:35 AM	An Asynchronous Distributed Expectation Maximization Algorithm for Massive Data: The DEM Algorithm Sanvesh Srivastava, University of Iowa; Chuanhai Liu, Purdue University; Glen DePalma, Purdue University
10:55 AM	A Regularization Based Approach to Estimation of a Two Component Nonparametric Density Mixture with a Known Component Presentation Michael Levine, Purdue University; Zuofeng Shang, IUPUI; Zhou Shen, J.P. Morgan
11:15 AM	Singularity Structures of Mixture Models: Statistical and Computational Perspective Presentation Nhat Ho, University of California, Berkeley
11:35 AM	Prediction Risk in Linear Regression Models Under Global-Local Mixture Priors Presentation Anindya Bhadra, Purdue University; Jyotishka Datta, University of Arkansas; Yunfan Li, Purdue University; Nicholas Polson, University of Chicago; Brandon Willard, University of Chicago
11:55 AM	Mixture Methods for Panel Data Models Presentation Stephane Bonhomme, University of Chicago
12:15 PM	Floor Discussion

642 * !	Thu, 8/1/2019, 10:30 AM - 12:20 PM	CC-106
Advanced Statistical Methods for Large Data Sets — Topic Contributed Papers
Social Statistics Section, International Chinese Statistical Association, Lifetime Data Science Section, Section on Statistical Learning and Data Science
Organizer(s): Xingqiu Zhao, The Hong Kong Polytechnic University
Chair(s): Jianguo Sun, University of Missouri
10:35 AM	Distributed Learning with Minimum Error Entropy Principle Xin Guo, The Hong Kong Polytechnic University; Ting Hu, Wuhan University; Qiang Wu, Middle Tennessee State University
10:55 AM	Entropy Learning for Dynamic Treatment Regimes Presentation Binyan Jiang
11:15 AM	Efficient Fused Learning for Distributed Imbalanced Data Yuanyuan Lin
11:35 AM	Penalized Interaction Estimation for Ultrahigh Dimensional Quadratic Regression Cheng Wang, Shanghai Jiao Tong University
11:55 AM	Penalized Generalized Empirical Likelihood with a Diverging Number of General Estimating Equations for Censored Data Presentation Xingqiu Zhao, The Hong Kong Polytechnic University; Niansheng Tang, Yunnan University; Xiaodong Yan, Shandong University
12:15 PM	Floor Discussion

646 * !	Thu, 8/1/2019, 10:30 AM - 12:20 PM	CC-708
Applications of Deep Learning in Pharmaceutical Development — Topic Contributed Papers
Biopharmaceutical Section, Section on Statistical Learning and Data Science, Biometrics Section, Text Analysis Interest Group
Organizer(s): Xin Huang, AbbVie Inc.
Chair(s): Weili He, AbbVie
10:35 AM	Deep Learning-Based Histology Image Analysis for Patient Diagnosis and Selection Presentation Xin Huang, AbbVie Inc.; Liuqing Yang, AbbVie; Yan Sun, AbbVie; Mufeng Hu, AbbVie
10:55 AM	Leveraging Free Text Data for Decision Making in Drug Development Presentation Yan Sun, AbbVie; Jiyeong Jang, University of Illinois at Chicago; Xin Huang, AbbVie Inc.; Hongwei Wang, AbbVie Inc.; Weili He, AbbVie
11:15 AM	Diagnosis of Diabetic Retinopathy Using Medical Images and Deep Learning Method Xuanyao He, Eli Lilly and Company
11:35 AM	Discussant: Hongwei Wang, AbbVie Inc.
11:55 AM	Discussant: Mandy Jin, Merck & Co., Inc.
12:15 PM	Floor Discussion

659	Thu, 8/1/2019, 10:30 AM - 12:20 PM	CC-301
Recent Advances in Dimension Reduction and Clustering — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Yue Wang, Fred Hutchinson Cancer Center
10:35 AM	Dimension Reduction and Classification of Imbalanced Data Elizabeth Chou, National Chengchi University
10:50 AM	Gaussian Mixture Clustering Using Relative Tests of Fit Presentation 1 Presentation 2 Purvasha Chakravarti, Carnegie Mellon University; Larry Wasserman, Carnegie Mellon University; Sivaraman Balakrishnan, Carnegie Mellon University
11:05 AM	Matrix Completion Under Low-Rank Missing Mechanism Xiaojun Mao, Fudan University; Raymond Wong, Texas A&M University; Song Xi Chen, Peking University
11:20 AM	Bias in Joint Spectral Embeddings Benjamin Draves, Boston University ; Daniel L Sussman, Boston University
11:35 AM	Cluster Analysis via Random Partition Distributions David Dahl, Brigham Young University; Brandon Carter, Brigham Young University
11:50 AM	B-MuLe: Sparse Multi-View Representation Learning Problem with Application in Multi-Omics Studies Presentation Omid Shams Solari; James Bentley Brown, Uc Berkeley statistics
12:05 PM	Efficient Local Kernel Estimation Using Structured Random Forests Presentation Joshua Loyal, University of Illinois Urbana-Champaign; Ruoqing Zhu, University of Illinois Urbana-Champaign; Xin Zhang, Florida State University; Yifan Cui, University of Pennsylvania

660	Thu, 8/1/2019, 10:30 AM - 12:20 PM	CC-302
Machine Learning: Advances and Applications — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Brandon Greenwell, 84.51º
10:35 AM	A Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data Presentation Martin Slawski, George Mason Univ; Emanuel Ben-David, US Census Bureau
10:50 AM	Beyond Test Scores: Scaling Item Response Theory Modeling for Large-Volume Machine-Learning Applications Lauren Harrell, Google
11:05 AM	Using Machine Learning Algorithms to Reduce Data Collection Costs Presentation Gavin Corral, National Agricultural Statistics Service (NASS); Tyler Wilson, USDA, NASS
11:20 AM	Where Do I Begin? Tuning Support Vector Machines and Boosted Trees Presentation Jill Lundell, Utah State University
11:35 AM	Classification and Regression Tree Analysis for Participation in Surveys with Physical Measurements Presentation Kelly Diecker, ICF; Richard (Lee) Harding, ICF
11:50 AM	Regularized High-Dimensional Low Tubal Rank Tensor Regression and Its Applications Samrat Roy, University of Florida; George Michailidis, University of Florida
12:05 PM	Random Projection for Tensor Rejaul Karim, Michigan State University; Taps Maiti, Michigan State University

JSM 2019 Online Program

Activity Details

American Statistical Association