Name: 2020 Joint Statistical Meetings
Start: 2020-08-02T07:00:00+00:00
End: 2020-08-06

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* = applied session ! = JSM meeting theme

Activity Details

8 * !	Mon, 8/3/2020, 10:00 AM - 11:50 AM	Virtual
Recent Advances in Statistical Learning for High-Dimensional and Heterogeneous Complex Data — Invited Papers
Section on Statistical Learning and Data Science, International Chinese Statistical Association, Journal on Statistical Analysis and Data Mining, Section on Statistical Computing
Organizer(s): Ji Zhu, University of Michigan
Chair(s): Omid Shams Solari, UC Berkeley
10:05 AM	Heterogeneous Mediation Analysis for Causal Inference Fei Xue, University of Pennsylvania; Xiwei Tang, University of Virgina; Annie Qu, University of Illinois at Urbana-Champaign
10:25 AM	L-zero regularization in the causal mediation analysis Peter X.K. Song, University of Michigan; Wen Wang, University of Michigan
10:45 AM	Large matrix estimation for time series data Hai Shu, New York University; Bin Nan, University of California, Irvine
11:05 AM	High-dimensional factor regression for heterogeneous subpopulations Peiyao Wang, University of North Carolina at Chapel Hill; Quefeng Li, UNC Chapel Hill; Yufeng Liu, University of North Carolina at Chapel Hill; Dinggang Shen, University of North Carolina at Chapel Hill
11:25 AM	Correlation Tensor Decomposition and Its Application in Spatial Imaging Data Yujia Deng, University of Illinois, Urbana-Champaign; Xiwei Tang, University of Virgina; Annie Qu, University of California Irvine
11:45 AM	Floor Discussion

16 * !	Mon, 8/3/2020, 10:00 AM - 11:50 AM	Virtual
How Statistics and Data Science Help to Quantify Resilience of Power Systems — Invited Papers
Section on Statistics in Defense and National Security, Section on Risk Analysis, Section on Statistical Learning and Data Science
Organizer(s): Asim Dey, Princeton University and University of Texas at Dallas; Yulia Gel, University of Texas at Dallas
Chair(s): Yuzhou Chen, Southern Methodist Univ, Statistical Science Dept
10:05 AM	Topology-Based Machine-Learning for Modeling Power-System Responses to Contingencies Brian W Bush, NREL
10:30 AM	Geography and Network-of-Networks Properties Stephen J Young, Pacific Northwest National Laboratory
10:55 AM	Topological and Geometric Methods for Resilience Analysis of Power Grid Networks H. Vincent Poor, Princeton University; Yulia Gel, University of Texas at Dallas; Asim Dey, Princeton University and University of Texas at Dallas; Umar Islambekov, Bowling Green State University
11:20 AM	Floor Discussion

18 * !	Mon, 8/3/2020, 10:00 AM - 11:50 AM	Virtual
Emerging Issues in EHR: Methods to Enhance Knowledge Discovery and Real World Implementation — Invited Papers
Section on Statistics in Epidemiology, Biometrics Section, Section on Statistical Learning and Data Science
Organizer(s): Yong Chen, University of Pennsylvania
Chair(s): Rui Duan, University of Pennsylvania
10:05 AM	Modeling Heterogeneity and Missing Data in Electronic Health Records Ying Wei, Columbia University
10:35 AM	Statistical Challenges of Implementing Real-Time EHR-Based Risk Prediction Tools in Emergency Department Dandan Liu, Vanderbilt University
11:05 AM	Distributed Learning for Electronic Health Records Data Yong Chen, University of Pennsylvania
11:35 AM	Floor Discussion

28 * !	Mon, 8/3/2020, 10:00 AM - 11:50 AM	Virtual
Advances in Bayesian Theory and Methods on Network Data Modeling — Topic Contributed Papers
Section on Bayesian Statistical Science, Section on Statistical Learning and Data Science, International Society for Bayesian Analysis (ISBA)
Organizer(s): Yanxun Xu, Johns Hopkins University
Chair(s): Guanyu Hu, University of Connecticut
10:05 AM	Optimal Bayesian Estimation for Low-Rank Random Graphs Fangzheng Xie, Johns Hopkins University; Yanxun Xu, Johns Hopkins University
10:25 AM	Joint Bayesian Variable and DAG Selection Consistency for High-Dimensional Regression Models with Network-Structured Covariates Presentation Xuan Cao, University of Cincinnati; Kyoungjae Lee, Inha University
10:45 AM	Probabilistic Community Detection with Unknown Number of Communities Presentation Junxian Geng, Boehringer Ingelheim; Debdeep Pati, Texas A&M University; Anirban Battacharya, Texas A&M University
11:05 AM	Optimization for Bayesian Inference Leo Duan, University of Florida
11:25 AM	Discussant: Yanxun Xu, Johns Hopkins University
11:45 AM	Floor Discussion

68	Mon, 8/3/2020, 10:00 AM - 2:00 PM	Virtual
Modern Statistical Learning Methods — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Sai Li, University of Pennsylvania
	Comparison of the Performance of Different Recurrent Neural Network Models on Sequence Classification: A Simulation Study Presentation Dawei Liu, Biogen; Charlie Cao, Biogen
	Improved Strategies for Clustering Objects on Subsets of Attributes Maarten Kampert; Jacqueline Meulman, Leiden University, Stanford University ; Jerome Friedman, Stanford University
	Multilayer Recommender Systems Using with Dependent via Tensor Jiuchen Zhang, Univ of California in Irvine; Annie Qu, University of California Irvine; Yubai Yuan, University of Illinois at Urbana-Champaign
	Classification Method Optimization Brooke McGinley, Rowan University; Liam Doherty, Rowan University; Umashanger Thayasivam, Rowan University
	On the Consistent Estimation of Receiver Operating Characteristic (ROC) Curve Renxiong Liu, Ohio State University; Yunzhang Zhu, Ohio State University
	Simultaneous Or-Of-And Rules for Binary Classification Elena Khusainova, Yale University; Emily Dodwell, Data Science & AI Research, AT&T Labs; Ritwik Mitra, Data Science & AI Research, AT&T Labs

69	Mon, 8/3/2020, 10:00 AM - 2:00 PM	Virtual
Network Analysis — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Adam Rothman, University of Minnesota
	Bias-Variance Tradeoffs in Joint Spectral Embeddings Benjamin Draves, Boston University; Daniel L Sussman, Boston University
	Graph Matching via Mutual Nearest Neighbors Zihuan Qiao, Boston University; Daniel L Sussman, Boston University
	Statistical Analysis and Methods for Human Connectomes Daniel Kessler, University of Michigan; Elizaveta Levina, University of Michigan; Keith Levin, University of Michigan, Department of Statistics
	Estimating Latent Space Geometry of Network Formation Models Shane Lubold, Department of Statistics, University of Washington; Tyler McCormick, University of Washington; Arun Chandrasekhar, Stanford University
	Anomaly Detection on Complex Networks via Topological Data Analysis Ignacio Segovia-Dominguez, The University of Texas at Dallas; Dorcas Ofori-Boateng, NASA Jet Propulsion Laboratory; Cuneyt Gurcan Akcora, University of Manitoba; Murat Kantarcioglu, The University of Texas at Dallas; Yulia Gel, University of Texas at Dallas
	Network Structure Inference from Grouped Data Yunpeng Zhao, Arizona State Univ; Peter Bickel, University of California, Berkeley; Charles Weko, U.S. Army
	High-Order Embedding for Hyperlink Network Prediction Yubai Yuan, University of Illinois at Urbana-Champaign; Annie Qu, University of California Irvine

70	Mon, 8/3/2020, 10:00 AM - 2:00 PM	Virtual
Multivariate Statistical Methods — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Xinwei Zhang, Rutgers University
	A Semiparametric Approach to Inner Envelope Linquan Ma, University of Wisconsin-Madison; Hyunseung Kang, University of Wisconsin-Madison; Lan Liu, University of Minnesota at Twin Cities
	Multi-Categorical Crowdsourcing Using Subgroup Latent Factor Modeling Qi Xu, University of California, Irvine; Yubai Yuan, University of Illinois at Urbana-Champaign; Junhui Wang, City University of Hong Kong; Annie Qu, University of California Irvine
	Interpretable Recurrent Nonlinear Group Factor Analysis Lin Qiu; Vernon M. Chinchilli , The Pennsylvania State University ; Lin Lin, The Pennsylvania State University
	Analyzing Dynamic Stock Trading Network with Matrix Factor Models Ruofan Yu
	Automated Kronecker Product Approximation Chencheng Cai, Rutgers University; Rong Chen, Rutgers University; Han Xiao, Rutgers University
	Envelope Huber Regression Le Zhou, University of Minnesota; Dennis Cook, University of Minnesota; Hui Zou, University of Minnesota

105 * !	Mon, 8/3/2020, 1:00 PM - 2:50 PM	Virtual
Deep Learning and Statistical Modeling with Applications — Invited Papers
International Chinese Statistical Association, Section on Statistical Learning and Data Science, IMS
Organizer(s): Ji Zhu, University of Michigan
Chair(s): Ji Zhu, University of Michigan
1:05 PM	Deep Learning and Statistical Modeling with Applications Yingying Fan, USC
1:30 PM	Beyond Shallow Learning: New Results for Matrix Completion Jianqing Fan, Princeton University; Yuxin Chen, Princeton University; Cong Ma, Princeton University; Yulin Yan, Princeton University
1:55 PM	Statistical Challenges in Analyzing Two-Sided Marketplace HONGTU ZHU, DiDi
2:20 PM	From Classical Statistics to Modern Machine Learning Mikhail Belkin, Ohio State University
2:45 PM	Floor Discussion

119 *	Mon, 8/3/2020, 1:00 PM - 2:50 PM	Virtual
Statistical Learning Applications for Autonomous Systems in Defense and National Security — Topic Contributed Papers
Section on Statistics in Defense and National Security, Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Joseph D Warfield, Johns Hopkins University Applied Physics Laboratory
Chair(s): Justin T Newcomer, Sandia National Laboratories
1:05 PM	Tallis: A Statistical Approach for Dimension Reduction of Mixed-Type Variables Alexander Foss, Sandia National Laboratories
1:25 PM	Challenges in Test and Evaluation of AI-Enabled Systems in the DoD Jane Pinelis, DoD Joint Artificial Intelligence Center
1:45 PM	Multinomial Pattern Matching John Richards, Sandia National Laboratories
2:05 PM	Leveraging Machine Learning for Autonomy Testing and Evaluation Galen Mullins, Johns Hopkins University Applied Physics Laboratory
2:25 PM	Demystifying the Black Box: A Strategy for Testing AI-Enabled Systems Heather Wojton, Institute for Defense Analyses; Daniel Porter, Institute for Defense Analyses
2:45 PM	Floor Discussion

129 * !	Mon, 8/3/2020, 1:00 PM - 2:50 PM	Virtual
Advances in Graph Inference and Network Analysis — Topic Contributed Papers
Section on Statistical Learning and Data Science, Section on Statistics in Defense and National Security, Caucus for Women in Statistics, Section on Statistical Computing
Organizer(s): Joshua Cape, University of Michigan
Chair(s): Joshua Cape, University of Michigan
1:05 PM	Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace Jesus Arroyo; Avanti Athreya, Johns Hopkins University; Joshua Cape, University of Michigan; Guodong Chen, Johns Hopkins University; Carey Priebe, Johns Hopkins University; Joshua Vogelstein, Johns Hopkins University
1:25 PM	Network Community Detection Using Higher Order Interactions Xianshi Yu; Ji Zhu, University of Michigan
1:45 PM	Online Change Point Detection in Network Sequences Sharmodeep Bhattacharyya, Oregon State University; Shirshendu Chatterjee, City University of New York
2:05 PM	Estimation and Inference in Latent Structure Random Graphs Avanti Athreya, Johns Hopkins University; Minh Tang, NC State University; Youngser Park, Johns Hopkins University; Carey Priebe, Johns Hopkins University
2:25 PM	Posterior Predictive Distributions in Network Inference Anna Smith, University of Kentucky; Tian Zheng, Columbia University
2:45 PM	Floor Discussion

135 * !	Tue, 8/4/2020, 10:00 AM - 11:50 AM	Virtual
Move Non/Semiparametrics Forward in Causal Inference, Missing Data Analysis, and Data Integration — Invited Papers
Section on Nonparametric Statistics, Section on Statistical Learning and Data Science, International Chinese Statistical Association
Organizer(s): Jiwei Zhao, State University of New York at Buffalo
Chair(s): Jiwei Zhao, State University of New York at Buffalo
10:05 AM	Combining Multiple Observational Data Sources to Estimate Causal Effects Peng Ding, University of California, Berkeley; Shu Yang, North Carolina State University
10:30 AM	Learning When-to-Treat Policies Stefan Wager, Stanford University; Xinkun Nie, Stanford University; Emma Brunskill, Stanford University
10:55 AM	A Bayesian Nonparametric Approach for Estimating the Causal Effect of a Time-Varying/Dynamic Treatment Michael Daniels, University of Florida; Kumaresh Dhara, University of Florida; Jason Roy, Rutgers University
11:20 AM	Minimax Optimal Estimation of Heterogeneous Treatment Effects Edward Kennedy, Carnegie Mellon University
11:45 AM	Floor Discussion

142 * !	Tue, 8/4/2020, 10:00 AM - 11:50 AM	Virtual
Fairness and Equity in Clinical Risk Prediction: Healthcare Data for the Public Good — Invited Papers
Biometrics Section, Health Policy Statistics Section, Section on Statistical Learning and Data Science, Lifetime Data Science Section
Organizer(s): Yates Coley, Kaiser Permanente Washington Health Research Institute
Chair(s): Maricela Cruz, Kaiser Permanente Washington Health Research Institute
10:05 AM	Assessing Racial and Ethnic Fairness of a Suicide Risk Prediction Model Yates Coley, Kaiser Permanente Washington Health Research Institute; Eric Johnson, Kaiser Permanente Washington Health Research Institute; Susan Shortreed, Kaiser Permanente Washington Health Research Institute
10:25 AM	Can Individualized Risk Calculators Reduce Racial/Ethnic Disparities in Cancer Screening Guidelines? Hormuzd Katki, US National Cancer Institute; Corey Young, Morehouse School of Medicine; Li Cheung, US National Cancer Institute; Rebecca Landy, US National Cancer Institute
10:45 AM	Detecting Undercompensated Groups in Plan Payment Risk Adjustment Presentation Anna Zink, Harvard University; Sherri Rose, Harvard Medical School
11:05 AM	Discussant: Kristian Lum, HRDAG
11:25 AM	Discussant: Ruth Pfeiffer, National Cancer Institute
11:45 AM	Floor Discussion

150 *	Tue, 8/4/2020, 10:00 AM - 11:50 AM	Virtual
Operating a Private Statistical Consulting and Collaboration Practice: Words of Wisdom from Experts in the Field — Invited Panel
Section on Statistical Consulting, Section on Statistical Learning and Data Science, Section on Statistics and Data Science Education
Organizer(s): Harry Dean Johnson, Washington State University
Chair(s): Clark Kogan, Washington State University
10:05 AM	Operating a Private Statistical Consulting and Collaboration Practice: Words of Wisdom from Experts in the Field Presentation
Panelists:	Stephen Simon , P. Mean Consulting Elaine Eisenbeisz , OMEGA STATISTICS Karen Grace-Martin, The Analysis Factor Kim Love , K. R. Love Quantitative Consulting and Collaboration Nayak Polissar , The Mountain-Whisper-Light Statistics
11:40 AM	Floor Discussion

168	Tue, 8/4/2020, 10:00 AM - 11:50 AM	Virtual
SLDS Student Paper Awards — Topic Contributed Papers
Section on Statistical Learning and Data Science
Organizer(s): Genevera Allen, Rice University
Chair(s): Irina Gaynanova, Texas A&M University
10:05 AM	Statistical Inference for Networks of High-Dimensional Point Processes Xu Wang
10:25 AM	Classification Accuracy as a Proxy for Two-Sample Testing Ilmun Kim, Carnegie Mellon University; Aaditya Ramdas, Carnegie Mellon University; Aarti Singh, Carnegie Mellon University; Larry Wasserman, Carnegie Mellon University
10:45 AM	High-Dimensional Nonparametric Density Estimation via Max-Random Forest Yiliang Zhang, University of Pennsylvania; Qi Long, University of Pennsylvania; Weijie Su, University of Pennsylvania
11:05 AM	Testing for Association in Multi-View Network Data Lucy Gao, University of Washington; Daniela Witten, University of Washington; Jacob Bien, University of Southern California
11:25 AM	Learning Optimal Distributionally Robust Individualized Treatment Rules Presentation Weibin Mo, University of North Carolina at Chapel Hill; Zhengling Qi, George Washington University; Yufeng Liu, University of North Carolina at Chapel Hill
11:45 AM	Floor Discussion

170	Tue, 8/4/2020, 10:00 AM - 11:50 AM	Virtual
Data Science: How Federal Agencies Are Upskilling Staff for the Modern Data Environment — Topic Contributed Panel
Government Statistics Section, Section on Statistics and Data Science Education, Section on Statistical Learning and Data Science
Organizer(s): Rebecca Hutchinson, US Census Bureau
Chair(s): Stephanie Studds, US Census Bureau
10:05 AM	Data Science: How Federal Agencies Are Upskilling Staff for the Modern Data Environment
Panelists:	Rebecca Hutchinson, US Census Bureau Christian Moscardi, US Census Bureau Alex Measure, Bureau of Labor Statistics Bethany Blakey, General Services Administration
11:40 AM	Floor Discussion

173	Tue, 8/4/2020, 10:00 AM - 2:00 PM	Virtual
Recent Advances in Statistical Learning and Missing Data Handling — Contributed Papers
Korean International Statistical Society, Section on Statistical Learning and Data Science
Chair(s): Yimin Zhang, Villanova University
	Estimating High-Dimensional Covariance and Precision Matrices Under General Missing Dependence Seongoh Park, The Research Institute of Basic Sciences at Seoul National University; Xinlei (Sherry) Wang, Southern Methodist University; Johan Lim, Seoul National University
	A Generalized Kernel Two-Sample Test Hoseung Song, University of California, Davis; Hao Chen, University of California, Davis
	Imputation Approach Based on Latent Class Trajectory for Handling Missing Values in Self-Reported Data MinJae Lee, University of Texas Southwestern Medical Center
	Variable Selection with Metaheuristic Methods Myung Soon Song, Kutztown University of Pennsylvania; Francis J Vasco, Kutztown University of Pennsylvania; Yun Lu, Kutztown University of Pennsylvania; Kyle Callaghan, Kutztown University of Pennsylvania
	Sparse Machine Learning Methods for Regression with Regularized Tensor Product Kernel Hang Yu, University of North Carolina at Chapel Hill; Yuanjia Wang, Columbia University; Donglin Zeng, University of North Carolina at Chapel Hill

203	Tue, 8/4/2020, 10:00 AM - 2:00 PM	Virtual
Contemporary Machine Learning — Contributed Papers
Section on Statistical Learning and Data Science, Text Analysis Interest Group
Chair(s): Michael Lavine, Army Research Office
	Risk Minimization Under Sampling Bias Arising from Customer Interactions Scott Rome, Comcast; Michael Kreisel, Comcast
	An Optimal Approach in Adaptive Collection Reducing the Bias Tong Wang
	Transfer Learning for Auto-Coding Free-Text Survey Responses Peter Baumgartner, RTI International; Amanda Smith, RTI International; Murrey Olmsted, RTI International; Dawn Ohse, RTI International; Bucky Fairfax, RTI International
	Post-Hoc Mixture Models of the Best Linear Unbiased Predictors from Linear Mixed Effects Models to Classify Longitudinal Data with Haphazardly Spaced Intervals: A Simulation Study Md Hossain, Nemours Biomedical Research, A.I. DuPont Children's Hospital; Benjamin E Leiby, Division of Biostatistics
	Modeling Temporary Shocks with Latent Processes for High-Dimensional Demand Time Series Benedikt Sommer, Maersk ; Klaus Kähler Holst, Maersk Research & Data; Pierre Pinson, Technical University of Denmark
	Real-Time Regression Analysis of Streaming Clustered Data Sets Lan Luo, University of Michigan; Peter X.K. Song, University of Michigan

204	Tue, 8/4/2020, 10:00 AM - 2:00 PM	Virtual
Experimental Design — Contributed Papers
Section on Statistical Learning and Data Science, Quality and Productivity Section
Chair(s): Shan Ba, LinkedIn
	Generalization of Thompson Sampling for Multiple Categorical and Numerical Variables with Application for Fraud Detection Alex Zolotovitski, T-Mobile
	Design of Experiment-based Configuration of Hyperparameters Of An Artificial Neural Network Luca Pegoraro, University of Padova; Rosa Arboretti, University of Padova; Riccardo Ceccato, University of Padova; Luigi Salmaso, University of Padova
	How Twitter Makes Causal Inference If AB Test Fails Wutao Wei, Twitter
	SoftBlock: Efficient and Optimal Treatment Assignment for Experiments Peter Dimmery, Facebook; David Arbour, Adobe Research; Anup Rao, Adobe Research
	Satellite Images and Deep Learning to Indentify Discrepency in Mailing Addresses with Applications to Census 2020 in Houston Zhaozhuo Xu, Rice University; Beidi Chen, Rice University; Alan Ji, Rice University; Anshumali Shrivastava, Rice University
	The Future Is Linked: Making Predictions with Data Sets Linked to Synthetic Populations Emily Hadley, RTI International; Caroline Kery, RTI International; Georgiy Bobashev, RTI International; Lauren Grattan, RTI International
	Resampling Methods for FDR Control of A/B/N Tests with Arbitrary Dependencies Michael Rotkowitz, Lyft

205	Tue, 8/4/2020, 10:00 AM - 2:00 PM	Virtual
Applications of Machine Learning — Contributed Papers
Section on Statistical Learning and Data Science, Text Analysis Interest Group
Chair(s): Jennifer Green, Montana State University
	Summarizing and Extracting Insights from Consumer Review Data Jingting Hui, PepsiCo; Jason Parcon, PepsiCo
	Comparison of Machine Learning Methods with Traditional Models for Use of Public Trial Registry Data to Predict Sites Needed and Time from Study Start to Primary Completion Date Presentation Linghui Li, AstraZeneca; Gabriela Feldberg, AstraZeneca; Faisal Khan, AstraZeneca; Sandra Smyth, AstraZeneca; Karin Schiene, AstraZeneca
	Comparing Machine Learning and Penalized Regression for Predicting Diabetic Kidney Disease Progression: Evidence from the Chronic Renal Insufficiency Cohort (CRIC) Study Jing Zhang, Moores Cancer Center, University of California, San Diego; Tobias Fuhrer, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland; Brian Kwan, University of California, San Diego; Daniel Montemayor, Department of Medicine, University of Texas Health Science Center at San Antonio; Kumar Sharma, Department of Medicine, University of Texas Health Science Center at San Antonio; Loki Natarajan, University of California, San Diego
	Opportunities and Challenges in the Use of Smartphone and Smartwatch-Based Step Count Measures in Studies of Physical Activity and Health Briana Cameron, 23andMe; Teresa Filshtein Sonmez, 23andMe; Stella Aslibekyan, 23andMe; Robert Gentleman, 23andMe
	Improving Productionized Insights in Machine Learning Models Through Data-Quality Quantification Christopher Barbour, Atrium; Paul Harmon, Atrium; Eric Loftsgaarden, Atrium

206	Tue, 8/4/2020, 10:00 AM - 2:00 PM	Virtual
Machine Learning Methodology — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Erin E Blankenship, University of Nebraska-Lincoln
	Application of Stochastic Gradient Descent in Parameter Estimation for Models with Spatial Correlation Gan Luan, New Jersey Institute of Tech
	Interpreting Robust Optimization via Adversarial Influence Functions Linjun Zhang, Rutgers University; Zhun Deng, Harvard University; Jialiang Wang, Harvard University; Cynthia Dwork, Harvard University
	Regularized High-Dimensional Low Tubal Rank Tensor Regression Samrat Roy; George Michailidis, University of Florida
	Adversarial Networks for Robust Estimation Ziyue Wang, Rutgers University-New Brunswick ; Zhiqiang Tan, Rutgers University
	Validation of Neuro-Fuzzy Based Classifiers for Outcome from Longitudinal RCT Venkata Sukumar Gurugubelli, University of Massachusetts - Dartmouth
	Application of Machine Learning Imputation for Machine Learning Jonathan Lisic, Cigna

215	Tue, 8/4/2020, 10:00 AM - 2:00 PM	Virtual
Contributed Poster Presentations: Section on Statistical Learning and Data Science — Contributed Poster Presentations
Section on Statistical Learning and Data Science
1:	Identifying Pareto-Based Multiobjective Solutions for Subset Selection Joshua Lambert, University of Cincinnati
2:	Causal Effect Random Forest of Interaction Trees for Observational Data, Applied to Educational Interventions Juanjuan Fan, San Diego State University; Luo Li, San Diego State University; Xiaogang Su, University of Texas, El Paso; Richard Levine, San Diego State University
3:	Real-Time Classification of Atrial Fibrillation Using RR Intervals and Transition States Jericho Lawson, University of Arizona
4:	Statistical Invariance of Betti Numbers in the Thermodynamic Regime Siddharth Vishwanath, Penn State Univ
5:	Network Clustering with Entropy-Based Monte Carlo Method Qiannan Zhai, Texas Tech University; Fangyuan Zhang, Texas Tech University
6:	Context-Dependent Self-Exciting Point Processes: Models, Methods, and Risk Bounds in High Dimensions Lili Zheng, University of Wisconsin-Madison; Garvesh Raskutti, UW-Madison; Rebecca Willett, University of Chicago
7:	A Comparison of Machine Learning Models for Mortality Prediction: National Health and Nutrition Examination Survey (NHANES III) Zoran Bursac, Florida International University; Roy Williams, Florida International University; Miguel Alonso, Florida International University; Prasad Bhoite, Florida International University; Emir Veledar, Florida International University
8:	Incorporating Group Structure into Tree-Based Algorithms and Group Selection Through Importance Measures Jiabei Yang, Brown University School of Public Health; Emily Dodwell, Data Science & AI Research, AT&T Labs; Ritwik Mitra, Data Science & AI Research, AT&T Labs; DeDe Paul, Data Science & AI Research, AT&T Labs
9:	Stagewise Estimating Equations for Variable Selection with Longitudinal Rate Data Gregory Vaughan, Bentley University
10:	Weakly Supervised Chinese Meta-Pattern Discovery and NER via TopWORDS 2 Jiaze Xu, Tsinghua University; Ke Deng, Tsinghua University
11:	Random-Projection Based Classification from Big Tensor Data Peide Li
13:	Connectivity Based Outlier Detection Chang Liu, Rutgers University; Rong Chen, Rutgers University
14:	An Algorithm for Adjusted Kernel Linear Discriminant Analysis Lynn Huang, Iowa State University
15:	Deriving and Generalizing Kernel Linear Discriminant Analysis for Multiple Cases Jackson Maris
16:	Adjusting Factor Models for Concomitant Variables by Adversarial Learning Austin Talbot, Duke University; David Carlson, Duke University; David Dunson, Duke University
18:	Covariance Estimation for Matrix-Variate Data with Missing Values and Mean Structure Roger Fan, University of Michigan; Shuheng Zhou, University of California, Riverside; Byoungwook Jang, University of Michigan
19:	Estimating Sleep from Sparse Screen-On/Screen-Off Smartphone Data Melissa Martin, University of Pennsylvania
20:	Robust Extrinsic Framework for Manifold Valued Data Analysis Hwiyoung Lee, Florida State University
21:	Clustering High Needs/Complex Patients Using Latent Class Analysis Meghan Hatfield, Kaiser Permanente; Jodi McCloskey, Kaiser Permanente; Connie Uratsu, Kaiser Permanente; Richard Grant, Kaiser Permanente
22:	SuperMICE: Multiple Imputation by Chained SuperLearners Aaron Shev, University of California, Davis; Hannah Laqueur, University of California, Davis; Rose Kagawa, University of California, Davis
23:	Feature Selection for Support Vector Regression Using a Genetic Algorithm Shannon McKearnan, University of Minnesota; David Vock, University of Minnesota; Julian Wolfson, University of Minnesota
24:	Time Varying Estimation of Tensor-On-Tensor Regression with Application in fMRI Data Pratim Guha Niyogi, Michigan State University; Tapabrata (Taps) Maiti, Michigan State University
25:	An Analytical Approach for Prediction Involving Classification of Data with Complex Structure Li-Jung Liang, UCLA; Joseph Maurer, UCLA; Li Li, UCLA
26:	Social Network Distributed Autoregressive Distributed Lag Model Christopher Grubb, Virginia Tech; Shyam Ranganathan, Virginia Tech; Srijan Sengupta, Virginia Tech; Jennifer Van Mullekom, Virginia Tech
27:	Assessment of Data Reduction Models Including Autoencoders for Optimal Visualization, Interpretability and Speed Benedict Anchang, NIEHS
28:	Aggregate Estimation in Sufficient Dimension Reduction for Binary Responses Han Zhang, The University of Alabama; Qin Wang, The University of Alabama
29:	Tensor Clustering with Planted Structures: Statistical Optimality and Computational Limits Yuetian Luo, University of Wisconsin-Madison; Anru Zhang, University of Wisconsin-Madison
30:	Augmented Movelet Method for Activity Recognition Using Smartphone Gyroscope and Accelerometer Data Emily Huang, Wake Forest University Department of Mathematics and Statistics; Jukka-Pekka Onnela, Harvard University
31:	Robust Matrix Estimations Meet Frank-Wolfe Algorithms Naimin Jing, Temple University; Cheng Yong Tang, Temple University; Ethan Fang, Penn State University
32:	Optimal Transport for Stationary Markov Chains Kevin O'Connor, University of North Carolina, Chapel Hill; Andrew Nobel, University of North Carolina, Chapel Hill; Kevin McGoff, University of North Carolina, Charlotte
33:	Early Prediction of Alzheimer’s Disease with Deep Learning Using Data Integration of MRI Data and Clinical Data Lisa Neums, University of Kansas Medical Center; Jinxiang Hu, University of Kansas Medical Center; Jeffrey Thompson, University of Kansas Medical Center
34:	Anomaly Detection Methods for IoT Freeze Loss Patrick Toman, University of Connecticut - Department of Statistics; Ahmed Soliman, University of Connecticut; Nalini Ravishanker, University of Connecticut; Sanguthevar Rajasekaran, University of Connecticut; Nathan Lally, Hartford Steam Boiler; Yuchen Fama, Hartford Steam Boiler

235	Tue, 8/4/2020, 12:00 PM - 1:00 PM	Virtual
Section on Statistical Learning and Data Science P.M. Roundtable Discussion — Roundtables PM Roundtable Discussion
Section on Statistical Learning and Data Science
TL11:	Big Data Analytics in Healthcare: Collaboration Between Biostatisticians and Data Scientists Madhuchhanda Mazumdar, Icahn School of Medicine at Mount Sinai

242 * !	Tue, 8/4/2020, 1:00 PM - 2:50 PM	Virtual
Deep Learning Methods in Biomedical Studies — Invited Papers
WNAR, Section on Statistical Computing, Section on Statistical Learning and Data Science
Organizer(s): Wei Sun, Fred Hutchinson Cancer Research Center
Chair(s): Kin Yau Alex Wong, The Polytechnic University of Hong Kong
1:05 PM	Multi-Stage Sequential Deep Autoencoder-Based Monotone Nonlinear Dimensionality Reduction Methods Youyi Fong, Fred Hutchinson Cancer Research Center; Jun Xu, None
1:30 PM	A Computationally-Favorable Reframing of Proportional-Hazards Modeling for Large Time-to-Event Data Sets with Applications to Deep Learning Noah Simon, University of Washington
1:55 PM	Deep Learning Methods for Single Cell Data Denoising and Multimodal Imputation Nancy Zhang, The University of Pennsylvania
2:20 PM	Discussant: Wei Sun, Fred Hutchinson Cancer Research Center
2:40 PM	Floor Discussion

243 * !	Tue, 8/4/2020, 1:00 PM - 2:50 PM	Virtual
Thinking Beyond the P-Value: Advancing Bayesian Education for the Undergraduates — Invited Papers
Section on Bayesian Statistical Science, Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Jingchen (Monika) Hu, Vassar College
Chair(s): Jingchen (Monika) Hu, Vassar College
1:05 PM	Quiz: Are You a Bayesian? Alicia A Johnson, Macalester College
1:25 PM	Bayes for Undergraduates: A Prior Is Modified Presentation Jeffrey A Witmer, Oberlin College
1:45 PM	Challenges of Teaching Bayesian Statistics to Undergraduates Brian Reich, North Carolina State University
2:05 PM	Computation Infrastructure for Teaching Bayesian Modeling Colin Witter Rundel, University of Edinburgh
2:25 PM	A Probability Plus Bayes Course Jim H Albert, Bowling Green State University
2:45 PM	Floor Discussion

253 * !	Tue, 8/4/2020, 1:00 PM - 2:50 PM	Virtual
Innovations in AstroStatistics on Exploring Large Public Data — Invited Papers
Astrostatistics Special Interest Group, Section on Physical and Engineering Sciences, Section on Statistical Learning and Data Science, Quality and Productivity Section
Organizer(s): Hyungsuk Tak, Pennsylvania State University
Chair(s): Hyungsuk Tak, Pennsylvania State University
1:05 PM	Handling Model Uncertainty via Smoothed Inference Sara Algeri, University of Minnesota
1:30 PM	Improving Exoplanet Detection Power: Multivariate Gaussian Process Models for Stellar Activity David Edward Jones, Texas A&M University; David Stenning, Imperial College London; Eric B Ford, Penn State University; Robert L Wolpert, Duke University; Thomas J Loredo, Cornell University; Xavier Dumusque, Observatoire Astronomique de l'Universite de Geneve
1:55 PM	Disentangling Stellar Activity and Planetary Signals Using Bayesian High-dimensional Analysis Bo Ning, Yale University; Jessi Cisewski-Kehe, Yale University; Allen Davis, Yale University; Parker Holzer, Yale University; Debra Fischer, Yale University
2:20 PM	Floor Discussion

254 * !	Tue, 8/4/2020, 1:00 PM - 2:50 PM	Virtual
Digital Phenotyping — Invited 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): Wenna Xi, Weill Medical College, Cornell University
1:05 PM	Digital Phenotype of Patients with Major Depressive Disorder Samprit Banerjee, Weill Medical College, Cornell University; Jihui Lee, Weill Cornell Medicine
1:25 PM	Physical Activity Recognition Using Smartphone Data Jukka-Pekka Onnela, Harvard University
1:45 PM	Using Federated Learning to Address User Heterogeneity and Privacy in Mobile Health Data Ambuj Tewari, University of Michigan
2:05 PM	A Pre-Processing Pipeline of MHealth Data Jihui Lee, Weill Cornell Medicine; Hongzhe Zhang, Weill Cornell Medicine; Samprit Banerjee, Weill Medical College, Cornell University
2:25 PM	Behavioral Monitoring and Change Point Detection in Digital Phenotyping Studies Ian Barnett, University of Pennsylvania
2:45 PM	Floor Discussion

257 *	Tue, 8/4/2020, 1:00 PM - 2:50 PM	Virtual
Online Experimentation at Scale: Challenges and Solutions — Invited Panel
Section on Statistical Learning and Data Science, Business and Economic Statistics Section, Section for Statistical Programmers and Analysts, Quality and Productivity Section
Organizer(s): Martin Tingley, Netflix
Chair(s): Iavor Bojinov, Harvard Business School
1:05 PM	Online Experimentation at Scale: Challenges and Solutions
Panelists:	Martin Tingley, Netflix Somit Gupta, Microsoft Xiaolin Shi, Snap Myoungji Lee, Lyft Guillaume Saint-Jacques, LinkedIN Dennis Sun, Cal Poly and Google
2:40 PM	Floor Discussion

260 * !	Tue, 8/4/2020, 1:00 PM - 2:50 PM	Virtual
Statistics and AI in Music — Topic Contributed Papers
Royal Statistical Society, Section on Statistical Learning and Data Science, IMS
Organizer(s): Jan Beran, University of Konstanz
Chair(s): Philipp Sibbertsen, Leibniz Universitaet Hannover
1:05 PM	Understanding Audio from Music Practice Sessions Christopher Raphael, Indiana University
1:25 PM	Visualizing Music Information: Classical Composers Networks and Similarities Presentation Patrick Georges, University of Ottawa
1:45 PM	Statistics and AI in Music Ahmed Elgammal, Artrendex / Rutgers University; Mark Gotham, Universität des Saarlandes / Cornell
2:05 PM	Fusing Audio and Semantic Technologies: Applying AI, Machine Learning and Data Science to Music Production and Consumption Mark Sandler, Queen Mary University of London; Johan Pauwels, Queen Mary University of London; David De Roure, University of Oxford; Kevin Page, University of Oxford
2:25 PM	Discussant: Jan Beran, University of Konstanz
2:45 PM	Floor Discussion

268 * !	Tue, 8/4/2020, 1:00 PM - 2:50 PM	Virtual
Extreme Machine Learning Methods and Applications: Domestic and International — Topic Contributed Papers
Section on Statistical Consulting, Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Kelly Toppin, IMT Corporation
Chair(s): Lu Chen, National Institute of Statistical Sciences
1:05 PM	Hierarchical Gaussian Processes for Outlier Detection Felix Jimenez, National Institute of Standards and Technology
1:25 PM	Empirical data-fusion approaches to generate model covariates Luca Sartore, National Institute of Statistical Sciences; Jake Abernethy, NASS; Claire Boryan, USDA; Lu Chen, USDA; Kevin Hunt, USDA; CLIFFORD H SPIEGELMAN, Texas A&M University; Linda J Young, NASS
1:45 PM	Early Season Planted Acreage Estimates Using Machine Learning Jake Abernethy, NASS; Claire Boryan, USDA; Kevin Hunt, USDA; Luca Sartore, National Institute of Statistical Sciences
2:05 PM	Classifying Evolving Data Streams Kelly Toppin, IMT Corporation; Luca Sartore, National Institute of Statistical Sciences
2:25 PM	Floor Discussion

284 * !	Wed, 8/5/2020, 10:00 AM - 11:50 AM	Virtual
Statistical Learning for Dependent and Complex Data: New Directions and Innovation — Invited Papers
Section on Statistics in Marketing, Business and Economic Statistics Section, Section on Statistical Learning and Data Science
Organizer(s): Guannan Wang, College of William and Mary
Chair(s): Guannan Wang, College of William and Mary
10:05 AM	Reduced Rank Autoregressive Models for Matrix Time Series Rong Chen, Rutgers University
10:30 AM	Fast and Fair Simultaneous Confidence Bands for Functional Parameters Matthew Reimherr, Penn State University
10:55 AM	High-Dimensional Sparse Nonlinear Vector Autoregressive Models Yuefeng Han, Rutgers University; Wei Biao Wu, University of Chicago; Likai Chen, Washington University in St. Louis
11:20 AM	Spatiotemporal Dynamics, Nowcasting and Forecasting COVID-19 in the United States Guannan Wang, College of William and Mary; Lily Wang, Iowa State University; Yueying Wang, Iowa State University
11:45 AM	Floor Discussion

285 * !	Wed, 8/5/2020, 10:00 AM - 11:50 AM	Virtual
Statistical Inference for Probabilistic Graphical Models with Applications — Invited Papers
ENAR, Section on Statistical Learning and Data Science, Biometrics Section
Organizer(s): Mladen Kolar, University of Chicago
Chair(s): Mladen Kolar, University of Chicago
10:05 AM	Combinatorial Inference for Brain Imaging Data Sets Junwei Lu, Harvard
10:30 AM	Bayesian Structure Learning for Dynamic Brain Connectivity Presentation Sanmi Koyejo, Department of Computer Science, University of Illinois at Urbana-Champaign
10:55 AM	Detecting Differences in Brain-Region Correlations Between Two Groups Yuval Benjamini, Hebrew University of Jerusalem; Itamar Faran, Hebrew University of Jerusalem; Michael Peer, Hebrew University of Jerusalem ; Shahar Arzy, Hebrew University of Jerusalem
11:20 AM	Direct Inference for Sparse Differential Network Analysis Irina Gaynanova, Texas A&M University; Byol Kim, University of Chicago; Mladen Kolar, University of Chicago
11:45 AM	Floor Discussion

286	Wed, 8/5/2020, 10:00 AM - 11:50 AM	Virtual
Learning Networks from Point Processes: Neuronal Connectivity Networks and Beyond — Invited Papers
Section on Statistical Learning and Data Science, IMS, ENAR
Organizer(s): Ali Shojaie, University of Washington
Chair(s): Ali Shojaie, University of Washington
10:05 AM	Context-Dependent Self-Exciting Point Processes: Models, Methods, and Risk Bounds in High Dimensions Garvesh Raskutti, UW-Madison; Lili Zheng, University of Wisconsin-Madison; Rebecca Willett, University of Chicago
10:30 AM	A Universal Nonparametric Event Detection Framework for Neuropixels Data Shizhe Chen, University of California, Davis; Hao Chen, University of California, Davis; Xinyi Deng, Columbia University
10:55 AM	Latent Network Structure Learning from High-Dimensional Multivariate Point Processes Biao Cai, University of Miami; Emma Jingfei Zhang, University of Miami; Yongtao Guan, University of Miami
11:20 AM	Theory and Modeling for the Truncated Hawkes Process Victor Solo, UNSW, Sydney
11:45 AM	Floor Discussion

299 !	Wed, 8/5/2020, 10:00 AM - 11:50 AM	Virtual
Machine Learning in Causal Inference with Applications in Complicated Settings — Topic Contributed Papers
Biometrics Section, ENAR, Section on Statistical Learning and Data Science
Organizer(s): Rui Song, NC State University
Chair(s): Hongtu Zhu, University of North Carolina at Chapel Hill
10:05 AM	DOES the MARKOV DECISION PROCESS FIT the DATA: TESTING for the MARKOV PROPERTY in SEQUENTIAL DECISION MAKING Presentation Chengchun Shi, The London School of Economics; Runzhe Wan, NC State Univeristy; Rui Song, NC State University; Wenbin Lu, North Carolina State University; Ling Leng, Amazon
10:25 AM	The Confidence Interval Method for Selecting Valid Instrumental Variables Frank Windmeijer, University of Oxford
10:45 AM	Testing an Elaborate Theory of a Causal Hypothesis Dylan Small, University of Pennsylvania; Bikram Karmakar, University of Florida
11:05 AM	Personalized Policy Learning Using Longitudinal Mobile Health Data Min Qian, Columbia University; Xinyu Hu, Uber AI Lab; Bin Cheng, Columbia University; Ken Cheung, Columbia University
11:25 AM	Discussant: Jialiang Li, National University of Singapore
11:45 AM	Floor Discussion

301 *	Wed, 8/5/2020, 10:00 AM - 11:50 AM	Virtual
Natural Language Processing Applications in Defense and National Security — Topic Contributed Papers
Section on Statistics in Defense and National Security, Text Analysis Interest Group, Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Joseph D Warfield, Johns Hopkins University Applied Physics Laboratory
Chair(s): Joseph D Warfield, Johns Hopkins University Applied Physics Laboratory
10:05 AM	Neural Language Processing to Detect, Attribute, Characterize and Defend Against Digital Deception Svitlana Volkova, Pacific Northwest National Laboratory
10:25 AM	A Sliding Information Distance for Change Point Detection in Text or Audio Richard Field, Sandia National Laboratories; Christina Ting, Sandia National Laboratories; Travis Bauer, Sandia National Laboratories
10:45 AM	Few-Shot Learning for Text Applications: Exploring Authorship Identification with Small Data Lauren Phillips, Pacific Northwest National Laboratory; Sarah Reehl, Pacific Northwest National Laboratory; Ana Usenko, Western Washington University
11:05 AM	Classifying Documents Through the Use of Artificial Intelligence Kelly Townsend, Johns Hopkins University, Applied Physics Laboratory; Alex Firpi, Johns Hopkins University Applied Physics Lab
11:25 AM	Discussant: David Marchette, US Naval Surface Warfare Center Dahlgren Division
11:45 AM	Floor Discussion

306	Wed, 8/5/2020, 10:00 AM - 11:50 AM	Virtual
Algorithmic and Inferential Advances in Univariate and Multivariate Tuning-Parameter-Free Nonparametric Procedures — Topic Contributed Papers
Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, IMS
Organizer(s): Charles Doss, University of Minnesota
Chair(s): Guangwei Weng, University of Minnesota
10:05 AM	Multivariate Rank-Based Distribution-Free Nonparametric Testing Using Measure Transportation Bodhisattva Sen, Columbia University; Nabarun Deb, Columbia University
10:25 AM	Likelihood Ratio Tests and Confidence Intervals Based on the Shape Constraint of Concavity Charles Doss, University of Minnesota; Jon Wellner, University of Washington
10:45 AM	Multivariate Adaptation in Log-Concave Density Estimation Arlene K. H. Kim, Korea University; Richard Samworth, University of Cambridge; Oliver Feng, University of Cambridge; Adityanand Guntuboyina, University of California, Berkeley
11:05 AM	Dyadic CART Revisited Sabyasachi Chatterjee, University of Illinois at Urbana-Champai
11:25 AM	Learning Multivariate Log-Concave Densities Ilias Diakonikolas, UW Madison
11:45 AM	Floor Discussion

309 *	Wed, 8/5/2020, 10:00 AM - 11:50 AM	Virtual
Interface Between Machine Learning and Uncertainty Quantification — Topic Contributed Papers
Uncertainty Quantification in Complex Systems Interest Group, Section on Physical and Engineering Sciences, Section on Statistical Learning and Data Science, Quality and Productivity Section
Organizer(s): Ana Kupresanin, Lawrence Livermore National Laboratory
Chair(s): Kathleen Schmidt, Lawrence Livermore National Laboratory
10:05 AM	On-Site Surrogates for Large-Scale Calibration Jiangeng Huang, University of California Santa Cruz; Robert Gramacy, Virginia Tech
10:25 AM	Calibrating Uncertainties in Deep Learning Bhavya Kailkhura, Lawrence Livermore National Laboratory; Jize Zhang, Lawrence Livermore National Lab
10:45 AM	Physics-Informed Machine Learning for Uncertainty Quantification in Land Models Presentation Khachik Sargsyan, Sandia National Laboratories; Cosmin Safta, Sandia National Laboratories; Vishagan Ratnaswamy, Sandia National Laboratories
11:05 AM	Quantifying Model Transfer Uncertainties Using Post Hoc Explainability in Deep Learning Models Evangelina Brayfindley, Pacific Northwest National Laboratory; Thomas Grimes, Pacific Northwest National Lab
11:25 AM	Floor Discussion

354	Wed, 8/5/2020, 10:00 AM - 2:00 PM	Virtual
Multivariate Analysis and Graphical Models — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Han Zhang, The University of Alabama
	Fused-Lasso Regularized Cholesky Factors of Large Nonstationary Covariance Matrices of Longitudinal Data Aramayis Dallakyan, Texas A&M University; Mohsen Pourahmadi, Texas A&M University
	Sparse Generalized Correlation Analysis and Thresholded Gradient Descent Presentation Sheng Gao, University of Pennsylvania; Zongming Ma, University of Pennsylvania
	Smooth Time-Varying Gaussian Graphical Models to Study Disease Progression Erin Mcdonnell, Columbia University; Shanghong Xie, Columbia University; Karen Marder, Columbia University; Yuanjia Wang, Columbia University
	Tensor Mixture Model in High Dimensions Biao Cai, University of Miami; Emma Jingfei Zhang, University of Miami; Wei Sun, Purdue University
	Robust Estimation of High-Dimensional Heavy Tailed Vector Autoregressive Models Sagnik Halder; George Michailidis, University of Florida

355 !	Wed, 8/5/2020, 10:00 AM - 2:00 PM	Virtual
Modern Model Selection — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Tianxi Li, University of Virginia
	Controlling Costs: Feature Selection on a Budget Presentation Guo Yu, University of Washington; Daniela Witten, University of Washington; Jacob Bien, University of Southern California
	On the Robustness of LASSO-Type Estimators to Covariance Misspecification Rebecca North, North Carolina State University; Jonathan Stallrich, North Carolina State University
	Sparse Group LASSO False Discovery Rate Path Kan Chen; Zhiqi Bu, University of Pennsylvania
	Variable Selection with False Discovery Rate Control in Deep Neural Networks Zixuan Song, University of Notre Dame; Jun Li, University of Notre Dame
	The Complete Lasso Trade-Off Diagram: Above the Donoho–Tanner Phase Transition Yachong Yang, Univ of Pennsylvania, Wharton School of Business; Hua Wang, University of Pennsylvania, Statistics Department of Wharton; Weijie Su, University of Pennsylvania

356	Wed, 8/5/2020, 10:00 AM - 2:00 PM	Virtual
Statistical Learning: Methods and Applications — Contributed Papers
Section on Statistical Learning and Data Science, Text Analysis Interest Group
Chair(s): Scott Rome, Comcast
	Combination of Optical Character Recognition and Natural Language Processing to Identify Patients with Sleep Apnea in EHR Data Enshuo Hsu, University of Texas Medical Branch; Yong-Fang Kuo, University of Texas Medical Branch; Rizwana Sultana, University of Texas Medical Branch; Gulshan Sharma, University of Texas Medical Branch
	Flexible Feature Selection and Cluster Analysis for Heterogeneous Data with Application to a Diffusion Tensor Imaging Study Wanying Ma, Novartis Pharmaceuticals Company; Luo Xiao, North Carolina State University; Jaroslaw Harezlak, Indiana University
	Genetic Algorithms for Feature Selection Huanjun Zhang, Texas A&M University; Edward Jones, Texas A&M University
	Towards an Adaptive Algorithm for Online Substance Use Episode Detection Joshua Rumbut, University of Massachusetts Dartmouth, University of Massachusetts Medical School; Hua Fang, University of Massachusetts Dartmouth, University of Massachusetts Medical School
	Common and Distinctive Pattern Analysis Between High-Dimensional Data Sets Hai Shu, New York University; Zhe Qu, Tulane University
	Achieving Impact with Data Science and Machine Learning in Drug Development David Ohlssen, Novartis Pharmaceuticals
	Automatic Identification and Classification of Different Types of Otitis from Free-Text Pediatric Medical Notes: A Deep-Learning Approach Corrado Lanera, University of Padova

357	Wed, 8/5/2020, 10:00 AM - 2:00 PM	Virtual
Contemporary Multivariate Methods — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Alex Zolotovitski, T-Mobile
	Uplift Modeling for Panel Data Using Switch Doubly Robust Method Presentation Hiroaki Naito, Doshisha University; Hisayuki Hara, Doshisha University
	Stepsize Selection in Langevin Monte Carlo via Coupling Presentation Matteo Sordello, University of Pennsylvania; Weijie Su, University of Pennsylvania; James Johndrow, University of Pennsylvania
	Model Selection Criteria for Biological Networks by Using Loop-Based Multivariate Regression Adaptive Splines Model Gul Bulbul, Bowling Green State Univ; Vilda Bahar Purutçuo?lu, Middle East Technical University
	Generalized Linear Models with Partially Mismatched Data Zhenbang Wang, George Mason University; Emanuel Ben-David, US Census Bureau; Martin Slawski, George Mason Univ
	Numerical Tolerance for Spectral Decompositions of Random Matrices Zachary Lubberts, Johns Hopkins University; Avanti Athreya, Johns Hopkins University; Vince Lyzinski, University of Maryland College Park; Minh Tang, NC State University; Carey Priebe, Johns Hopkins University; Michael J Kane, Yale University School of Public Health; Youngser Park, Johns Hopkins University; Bryan Lewis, Independent Researcher
	Inference in Higher Order Spin Systems Presentation Somabha Mukherjee, University of Pennsylvania; Jaesung Son, University of Pennsylvania; Bhaswar Bhattarcharya, University of Pennsylvania

389 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM	Virtual
Unsupervised Learning with Latent Variables for Biobehavioral Research — Invited Papers
Mental Health Statistics Section, Section on Statistical Learning and Data Science, Biometrics Section
Organizer(s): Douglas David Gunzler, Case Western Reserve University
Chair(s): Xiao-Li Meng, Harvard University
1:05 PM	Constructing Latent Risk Labels Aligned with Clinical Practice Presentation Booil Jo, Stanford University
1:25 PM	Looking Inside the Black Box: New Methods to Assess Causality in Unsupervised Machine Learning Alessandro De Nadai, Texas State University; Ryan Zamora, Texas State University; Douglas David Gunzler, Case Western Reserve University
1:45 PM	Dealing with Latent Variable Confounding Using Auxiliary Variables and PushForward Presentation Bryant Chen, Brex Inc.
2:05 PM	Discussant: Mark Van der Laan, University of California, Berkeley
2:25 PM	Discussant: Maya Mathur, Stanford University
2:45 PM	Floor Discussion

392 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM	Virtual
Big Tensor Data Analysis — Invited Papers
Section on Statistical Learning and Data Science, International Chinese Statistical Association, Section on Nonparametric Statistics, Section on Statistical Computing
Organizer(s): Anru Zhang, University of Wisconsin-Madison
Chair(s): Anru Zhang, University of Wisconsin-Madison
1:05 PM	Tensor Response Regression in the Presence of Heavy Tails Xin Zhang, Florida State University
1:30 PM	Tensor Models for Large, Complex and High-Dimensional Data Shuheng Zhou, University of California, Riverside
1:55 PM	Co-Clustering Tensors Using Fusion Penalties and CP-Decompositions Eric Chi, North Carolina State University
2:20 PM	Duality of Graphical Models and Tensor Networks Elina Robeva, University of British Columbia
2:45 PM	Floor Discussion

394 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM	Virtual
Challenges and New Directions in Precision Medicine for Large-Scale and Complex Data — Invited Papers
Biometrics Section, Section on Statistical Learning and Data Science, International Chinese Statistical Association, Biopharmaceutical Section
Organizer(s): Yichuan Zhao, Georgia State University
Chair(s): Min Qian, Columbia University
1:05 PM	Building Cancer Prognostic Models Generated via Automatic Data-Driven Sequential Processes Hyokyoung (Grace) Hong, Michigan State University
1:30 PM	On Restricted Optimal Treatment Regime Estimation for Competing Risks Data Jie Zhou, University of South Carolina; Jiajia Zhang, University of South Carolina; Wenbin Lu , North Carolina State University; Xiaoming Li, University of South Carolina
1:55 PM	Depth Importance in Precision Medicine (DIPM): A Tree- and Forest-Based Method for Right-Censored Survival Outcomes Victoria Chen, Yale University; Heping Zhang, Yale University
2:20 PM	Discussant: Yichuan Zhao, Georgia State University
2:40 PM	Floor Discussion

395 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM	Virtual
The Need for Interpretable and Fair Algorithms in Health Policy — Invited Panel
Health Policy Statistics Section, Biometrics Section, Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Sherri Rose, Harvard Medical School
Chair(s): Laura Hatfield, Harvard Medical School
1:05 PM	The Need for Interpretable and Fair Algorithms in Health Care and Policy Presentation
Panelists:	Marzyeh Ghassemi, University of Toronto Melody Goodman, NYU Sherri Rose, Harvard Medical School Julius Adebayo, MIT
2:40 PM	Floor Discussion

398 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM	Virtual
Beyond Traditional Approaches: Evolving Artificial Intelligence and Machine Learning to Advance Clinical Research and Drug Development — Topic Contributed Papers
Biometrics Section, Biopharmaceutical Section, Section on Statistical Learning and Data Science, Text Analysis Interest Group
Organizer(s): Demissie Alemayehu, Pfizer Inc.
Chair(s): Birol Emir, Pfizer Inc.
1:05 PM	Adaptive Online Machine Learning for Real-Time Individualized Forecasting in Clinician-AI Team Rachael Phillips, University of California, Berkeley; Mark Van der Laan, University of California, Berkeley
1:25 PM	Recent Advances in the Application of Natural Language Processing to Unstructured and Semi-Structured Data in the Pharmaceutical Industry Presentation Peter Henstock, Pfizer Inc
1:45 PM	Learning Decision Rules with Observational Data Xinkun Nie, Stanford University; Stefan Wager, Stanford University
2:05 PM	“Data Nuggets” Tools for Analyzing Big Data Javier Cabrera, Rutgers University
2:25 AM	Floor Discussion

403 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM	Virtual
Sufficient Dimension Reduction and Variable Selection for High-Dimensional Inference — Topic Contributed Papers
Section on Nonparametric Statistics, Section on Statistical Learning and Data Science, International Chinese Statistical Association
Organizer(s): Wenbo Wu, University of Texas at San Antonio
Chair(s): Wenbo Wu, University of Texas at San Antonio
1:05 PM	Principal Asymmetric Least Squares for Sufficient Dimension Reduction Yuexiao Dong, Temple University; Abdul-Nasah Soale, Temple University
1:25 PM	On Sufficient Dimension Reduction for Functional Data via Weak Conditional Moments Jun Song, UNC Charlotte; Bing Li, Pennsylvania State University
1:45 PM	Sufficient Variable Selection via Expected Conditional Hilbert-Schmidt Independence Criterion Chenlu Ke, Virginia Commonwealth University
2:05 PM	On Sufficient Dimension Reduction with Mixture Normally Distributed Predictors Wei Luo, Zhejiang University
2:25 PM	Floor Discussion

406 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM	Virtual
Advances of Statistical Methodologies in Proteogenomics Research — Topic Contributed Papers
Section on Statistics in Genomics and Genetics, Biometrics Section, Section on Statistical Learning and Data Science
Organizer(s): Shrabanti Chowdhury, Icahn school of Medicine at Mount Sinai
Chair(s): Pei Wang, Icahn Medical School at Mount Sinai
1:05 PM	Statistical Characterization of Quantitative Changes in Global Post-Translational Modification Profiling Experiments Tsung-Heng Tsai, Kent State University; Erik Verschueren, Genentech, Inc.; Olga Vitek, Northeastern University
1:25 PM	Repulsive Mixtures for the Classification of Single Cells Francesca Petralia, Icahn School of Medicine At Mount Sinai
1:45 PM	Extracting Biological Information from Extreme Values in Proteogenomics Data Wenke Liu; Kelly Ruggles, NYU school of medicine; David Fenyo, NYU school of medicine
2:05 PM	Sparse Multiple Co-Inertia Analysis with Application to Integrative Analysis of Multi -Omics Data Eun Jeong Min, Univerisity of Pennsylvania; Qi Long, University of Pennsylvania
2:25 PM	A New Molecular Signature Method for Prediction of Driver Cancer Pathways from Transcriptional Data Boris Reva
2:45 AM	Floor Discussion

412 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM	Virtual
Incorporation of Real-World Evidence in Clinical Trial Designs and Associated Statistical Methodologies — Topic Contributed Papers
Section for Statistical Programmers and Analysts, Biopharmaceutical Section, Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Jeffrey Joseph, Covance; Swarna Reddy, Covance
Chair(s): Swarna Reddy, Covance
1:05 PM	Using Real World Data to Evaluate the Generalizability of Evidence from Randomized Clinical Trials Wei Shen, Eli Lilly and Company; Douglas Faries, Lilly Research Laboratories; Mark Belger, Eli Lilly and Company; Chen-Yen Lin, Eli Lilly and Company
1:25 PM	Real World Evidence Use at CBER: Emerging Issues from Submissions Jennifer Kirk, FDA, Center for Biologics Evaluation and Research (CBER)
1:45 PM	Data Standardization to Facilitate Comparison of Real World Data and Clinical Trial Data Aaron Galaznik, Acorn AI, a Medidata Company
2:05 PM	Analytic Considerations for Constructing Real-World Control Arms Katherine Tan, Flatiron Health; Brian Segal, Flatiron Health; Jonathan Bryan, Flatiron Health; Melissa Curtis, Flatiron Health; Nathan Nussbaum, Flatiron Health; Rebecca Miksad, Flatiron Health; Meghna Samant, Flatiron Health; Somnath Sarkar, Flatiron, Inc.; Aracelis Torres, Flatiron Health
2:25 PM	Leverage Real World Evidence in Drug Development and Regulatory Decision Making Rongmei Zhang, FDA, Center for Drug Evaluation and Research (CDER).
2:45 PM	Floor Discussion

417 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM	Virtual
Best Practices for Preparing Students for a Career in Business Analytics/Data Science — Topic Contributed Panel
Business Analytics/Statistics Education Interest Group, Section on Statistical Learning and Data Science, Business and Economic Statistics Section
Organizer(s): Amy L Phelps, Duquesne University
Chair(s): George Recck, Babson University
1:05 PM	Best Practices for Preparing Students for a Career in Business Analytics/Data Science Presentation
Panelists:	Sudipta Dasmohapatra, Duke University Michael Posner, Villanova University Roger Hoerl, Union College Kimberly Hockman, Genesis Equipping Ministries
2:45 PM	Floor Discussion

421 !	Thu, 8/6/2020, 10:00 AM - 11:50 AM	Virtual
Recent Advances in Time Series and Temporal Data Analysis — Invited Papers
Business and Economic Statistics Section, Section on Statistical Learning and Data Science, Social Statistics Section
Organizer(s): Shujie Ma, University of California, Riverside
Chair(s): Shujie Ma, University of California, Riverside
10:05 AM	Semiparametric Modeling of Structured Point Processes Using Multi-Level Log-Gaussian Cox Processes Yongtao Guan, University of Miami
10:30 AM	Nonparametric Standard Errors for High Frequency Data Per Mykland, University of Chicago; Lan Zhang, University of Illinois at Chicago
10:55 AM	Relevant Two-Sample Tests for the Eigenfunctions of Covariance Operators Presentation Alexander Aue, University of California-Davis
11:20 AM	Smoothed Log-Concave Probability Mass Functions with Application to Time-Series of Counts Thibault Vatter, Columbia University
11:45 AM	Floor Discussion

429 * !	Thu, 8/6/2020, 10:00 AM - 11:50 AM	Virtual
Strategic Statistical Partnerships Impacting Health and Education for the Public Good — Invited Papers
Stats. Partnerships Among Academe Indust. & Govt. Committee, Health Policy Statistics Section, Section on Statistical Learning and Data Science
Organizer(s): Ying Ding, University of Pittsburgh; Pamela McGovern, USDA National Agricultural Statistics Service; Fanni Natanegara, Eli Lilly and Company
Chair(s): Jungwha Julia Lee, Northwestern University
10:05 AM	Public-Private Collaborations Aiming to Improve the Reliability and Consistency of Clinical Tests to Guide Cancer Care Lisa Meier McShane, National Cancer Institute
10:25 AM	Furthering Public Health Through the Fred Hutchinson Cancer Research Center/Sanofi Pasteur Dengue Vaccine Collaboration Zoe Moodie, Fred Hutchinson Cancer Research Center; Peter Gilbert, Fred Hutchinson Cancer Research Center; Michal Juraska, Fred Hutchinson Cancer Research Center; Ying Huang, Fred Hutchinson Cancer Research Center; Youyi Fong, Fred Hutchinson Cancer Research Center; Brenda Price, University of Washington; Carlos DiazGranados, Sanofi Pasteur; Stephen Savarino, Sanofi Pasteur; Saranya Sridhar, Sanofi Pasteur; Edith Langevin, Sanofi Pasteur; Tifany Machabert, Sanofi Pasteur; Ming Zhu, Sanofi Pasteur
10:45 AM	An Academic and Industry Partnership Training the Next Generation of Data Scientists Presentation Amy Wagaman, Amherst College; Nicholas Horton, Amherst College
11:05 AM	Engaging Industry and Academia to Drive Meaningful Social Impact Christine Pfeil, MassMutual; Sears Merritt, MassMutual
11:25 AM	Discussant: Sally Morton, Virginia Tech
11:45 AM	Floor Discussion

438	Thu, 8/6/2020, 10:00 AM - 11:50 AM	Virtual
Statistical Methods for Topological Data Analysis — Topic Contributed Papers
Section on Statistical Learning and Data Science, Korean International Statistical Society, IMS
Organizer(s): Chul Moon, Southern Methodist University
Chair(s): Hengrui Luo, The Ohio State University
10:05 AM	Solution Manifold and Its Statistical Applications Yen-Chi Chen, University of Washington
10:25 AM	Persistent Topological Descriptors for Functional Brain Network Hyunnam Ryu, University of Georgia; Nicole Lazar, University of Georgia
10:45 AM	Uncovering the Holes in the Universe with Topological Data Analysis Jessi Cisewski-Kehe, Yale University
11:05 AM	Confidence Band for Persistent Homology Presentation Jisu Kim, Inria
11:25 AM	Discussant: Chul Moon, Southern Methodist University
11:45 AM	Floor Discussion

444 !	Thu, 8/6/2020, 10:00 AM - 11:50 AM	Virtual
Highlights from the Journal STAT — Topic Contributed Papers
SSC (Statistical Society of Canada), International Statistical Institute, Section on Statistical Learning and Data Science, WNAR
Organizer(s): Hao Helen Zhang, University of Arizona
Chair(s): Hao Helen Zhang, University of Arizona
10:05 AM	Baum-Welch Algorithm on Directed Acyclic Graph for Mixtures with Latent Bayesian Networks Jia Li, Penn State University; Lin Lin, The Pennsylvania State University
10:25 AM	Penalized Euclidean Distance Regression Daniel Vasiliu, College of William & Mary; Ian Dryden, University of Nottingham; Tanujit Dey, Harvard Medical School and Brigham and Women's Hospital
10:45 AM	Syncytial Clustering Ranjan Maitra, Iowa State University; Israel A. Almodovar-Rivera, University of Puerto Rico
11:05 AM	Clustering and Bi-Clustering for High-Frequency Financial Time Series Based on Mutual Information Jian Zou, Worcester Polytechnic Institute; Haitao Liu, Worcester Polytechnic Institute; Nalini Ravishanker, University of Connecticut
11:25 AM	From Causal Inference to Gene Regulation Caroline Uhler, Massachusetts Institute of Technology
11:45 AM	Floor Discussion

448 !	Thu, 8/6/2020, 10:00 AM - 11:50 AM	Virtual
The Contribution of Convex Optimization to New Statistical Concepts — Topic Contributed Papers
Section on Statistical Computing, Section on Statistical Graphics, Section on Statistical Learning and Data Science
Organizer(s): Michael G. Schimek, IMI Statistical Bioinformatics, Medical University of Graz, Austria
Chair(s): Michael G. Schimek, IMI Statistical Bioinformatics, Medical University of Graz, Austria
10:05 AM	Supervised Convex Clustering Tianyi Yao, Rice University; Minjie Wang, Rice University; Genevera Allen, Rice University
10:25 AM	A Computational Framework for Multivariate Convex Regression Rahul Mazumder, Massachusetts Institute of Technology
10:45 AM	TopKSignal: A Convex Optimization Tool for Signal Reconstruction from Multiple Ranked Lists Bastian Pfeifer, IMI Statistical Bioinformatics, Medical University of Graz, Austria; Luca Vitale, Medical University of Graz, Austria; University of Salerno, Salerno, Italy; Michael G. Schimek, IMI Statistical Bioinformatics, Medical University of Graz, Austria
11:05 AM	Novel Results on the Sorted L-One Penalized Estimator Presentation Malgorzata Bogdan, University of Wroclaw
11:25 AM	Discussant: David W. Scott, Rice University, Department of Statistics, Noah Harding Chair
11:45 AM	Floor Discussion

455 * !	Thu, 8/6/2020, 10:00 AM - 11:50 AM	Virtual
Big Data, Technology Platform and Digital Innovation with Measurable Impact — Topic Contributed Panel
Text Analysis Interest Group, Section on Statistical Learning and Data Science, Quantum Computing in Statistics and Machine Learning, Section on Statistical Computing
Organizer(s): Kelly Zou, Pfizer Inc
Chair(s): Kelly Zou, Pfizer Inc
10:05 AM	Big Data, Technology Platform and Digital Innovation with Measurable Impact
Panelists:	Siddhartha Dalal, Columbia University Mike Henderson, SAS Joseph Imperato, Pfizer Stanislav Kolenikov, Abt Associates Lourenco Miranda, Society Generale Mike Porath, The Mighty May Yamada-Lifton, SAS
11:45 AM	Floor Discussion

495 !	Thu, 8/6/2020, 10:00 AM - 2:00 PM	Virtual
Statistical Methods for Networks — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Adam Rothman, University of Minnesota
	Estimation of the Epidemic Branching Factor in Noisy Contact Networks Wenrui Li, Boston University; Eric Kolaczyk, Boston University; Daniel L Sussman, Boston University
	Matrix Factorization Methods for Community Detection in Dynamic Networks Yan Liu, University of Illinois at Urbana-Champaign; Yuguo Chen, University of Illinois at Urbana-Champaign
	A Bayesian Nonparametric Latent Space Approach for Modeling Evolving Communities in Dynamic Networks Joshua Loyal, University of Illinois at Urbana-Champaign; Yuguo Chen, University of Illinois at Urbana-Champaign
	Consistent Nonparametric Hypothesis Testing for Low Rank Random Graphs with Negative or Repeated Eigenvalues Joshua Agterberg, Johns Hopkins University; Minh Tang, NC State University; Carey Priebe, Johns Hopkins University; Mao Hong, Johns Hopkins University
	A Statistical Interpretation of Spectral Embedding: The Generalized Random Dot Product Graph Patrick Rubin-Delanchy, University of Bristol; Joshua Cape, University of Michigan; Minh Tang, NC State University; Carey Priebe, Johns Hopkins University
	Dynamic Latent Space Network Models with Attractors for Flocking and Polarization Xiaojing Zhu, Boston University; Eric Kolaczyk, Boston University; Konstantinos Spiliopoulos, Boston University; Dylan Walker, Boston University
	Spectral Filtering for Core Structure Identification in Complex Networks Ruizhong Miao, University of Virginia; Tianxi Li, University of Virginia

496 !	Thu, 8/6/2020, 10:00 AM - 2:00 PM	Virtual
Dimension Reduction — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Aramayis Dallakyan, Texas A&M University
	Bayesian Model Averaging Sufficient Dimension Reduction Michael Declan Power, Temple University; Yuexiao Dong, Temple University
	Principal Curve Approaches for Inferring 3D Chromatin Architecture Presentation Elena Tuzhilina, Stanford University, Department of Statistics ; Trevor Hastie, Stanford University, Department of Statistics ; Mark Segal, UCSF, Department of Epidemiology and Biostatistics
	On Sufficient Dimension Reduction via Principal Asymmetric Least Squares Abdul-Nasah Soale, Temple University; Yuexiao Dong, Temple University
	Learning Hierarchical Structures in Latent Attribute Models Chenchen Ma, University of Michigan; Gongjun Xu, University of Michigan
	A Supervised Framework for Linear Dimension Reduction Induced by Hypothesis Testing Kisung You, University of Notre Dame; Lizhen Lin, University of Notre Dame
	Canonical Correlation Analysis and Fusion Methods on a Large Face Database for Computer Vision Cuixian Chen, University of North Carolina, Wilmington; Jasmine Gaston, University of North Carolina Wilmington; Summerlin Thompson, University of North Carolina Wilmington; Suhaela Eledkawi, Wright State University; Caroline Werther, University of North Carolina Wilmington; Yaw Chang, University of North Carolina Wilmington; Yishi Wang, University of North Carolina Wilmington; Guodong Guo, West Virginia University

497 !	Thu, 8/6/2020, 10:00 AM - 2:00 PM	Virtual
Variable Selection — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Guo Yu, Cornell
	Controlled Group Variable Selection Using a Model-Free Knockoff Filter with a Generative Adversarial Networks (GANs) Generator Xinran Qi, Medical College of Wisconsin
	The Sylvester Graphical Lasso (SyGlasso) Yu Wang, University of Michigan; Byoungwook Jang, University of Michigan; Alfred Hero, University of Michigan
	Feature Screening for High-Dimensional Quadratic Generalized Linear Model via Point Biserial Correlation Jinzhu Jiang, Bowling Green State University; Junfeng Shang, Bowling Green State University
	Simultaneous Outlier Detection and Feature Selection Using Mixed-Integer Programming Ana Kenney, Pennsylvania State University; Luca Insolia, Scuola Normale Superiore (Pisa Italy); Francesca Chiaromonte, Pennsylvania State University and Sant’Anna School of Advanced Studies (Pisa Italy); Giovanni Felici, IIASI CNR
	Cluster Group Variable Selection Method for High-Dimensional Data Qingcong Yuan, Miami University; Zhiyuan Li, Miami University
	Structure Adaptive Lasso Presentation Sandipan Pramanik, Texas A&M University; Xianyang Zhang, Texas A&M University

498	Thu, 8/6/2020, 10:00 AM - 2:00 PM	Virtual
Modern Machine Learning — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Rebecca North, North Carolina State University
	Random Forest Kernels: Utility and Insights for Interpretable Statistical Learning Dai Feng, AbbVie; Richard Baumgartner, Merck
	Uniform Regret Bounds for Quantile Regression Tree Process in Offline and Online Settings Fei Fang, Duke University; Alexandre Belloni, Duke University
	Deep Learning with Gaussian Differential Privacy Zhiqi Bu, University of Pennsylvania
	An Optimal Statistical and Computational Framework for Generalized Tensor Estimation Rungang Han, University of Wisconsin-Madison; Rebecca Willett, University of Chicago; Anru Zhang, University of Wisconsin-Madison
	Nonparametric Individual Treatment Effect Estimation for Survival Data with Random Forests Denis Larocque, HEC Montreal; Sami Tabib, HEC Montreal
	Sequential Changepoint Detection for Classifier Label Shift Presentation Ciaran Evans, Carnegie Mellon University; Max G'Sell, Carnegie Mellon University
	Machine Learning Oracle to Guide Statistical Data Processing Lucas Koepke, National Institute of Standards and Techology; Michael Frey, National Institute of Standards and Technology

499	Thu, 8/6/2020, 10:00 AM - 2:00 PM	Virtual
New Methods for Machine Learning — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Dawei Liu, Biogen
	Transfer Learning in High-Dimensional Sparse Regression: Estimation, Prediction, and Minimax Optimality Sai Li, University of Pennsylvania; Hongzhe Li, University of Pennsylvania; Tony Cai, University of Pennsylvania
	Large-Scale Anomaly Detection Based on Ensemble Learning Xi Zhang, Huawei Technologies; Kai Chen, Huawei Technologies; Zhihua Lv, Huawei Technologies; Rongjun Xu, Huawei Technologies
	Random Forest-Classification of Area Socioeconomic Status (SES) and Mortality Risk in Pediatric Acute Lymphoblastic Leukemia (ALL) in the US Fatima Boukari, Delaware State University; Hacene Boukari, Delaware State University; Md Hossain, Nemours Biomedical Research, A.I. DuPont Children's Hospital
	Augmented Bagging as an Alternative to Random Forests Siyu Zhou, University of Pittsburgh; Lucas Mentch, University of Pittsburgh
	Machine Learning Techniques for Prediction of Retail Violation of Tobacco Products Adams Kusi Appiah, University of Nebraska Medical Center; Hongying Dai, University of Nebraska Medical Center

500	Thu, 8/6/2020, 10:00 AM - 2:00 PM	Virtual
Statistical Learning — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Linghui Li, AstraZeneca
	Effects of Stopping Criterion in the Growth of Trees in Regression Random Forests Aryana Arsham, National Cancer Institute; Philip Rosenberg, National Cancer Institute; Mark Peter Little, National Cancer Institute
	Using Machine Learning to Improve Propensity Score Matching Methods in Observational Studies Nan Zhang, Imperial College London; Daniel J. Graham, Imperial College London
	Inference for BART with Multinomial Outcomes Yizhen Xu, Brown University; Rami Kantor, Brown University; Ann Mwangi, Moi University; Michael Daniels, University of Florida; Joseph Hogan, Brown University
	Privacy-Preserving Distributed Learning from Electronic Health Records Across Multiple Heterogenous Clinical Sites Jiayi Tong, University of Pennsylvania; Chongliang Luo, University of Pennsylvania; Rui Duan, University of Pennsylvania; Mackenzie Edmondson, University of Pennsylvania; Christopher Forrest, Children's Hospital of Philadelphia; Yong Chen, University of Pennsylvania
	When Black Box Algorithms Are (Not) Appropriate: A Principled Prediction-Problem Ontology Jordan Rodu, University of Virginia; Michael Baiocchi, Stanford University
	Semi-Supervised Logistic Learning Based on Exponential Tilt Mixture Models Xinwei Zhang, Rutgers University; Zhiqiang Tan, Rutgers University

501	Thu, 8/6/2020, 10:00 AM - 2:00 PM	Virtual
Time Series Methods — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Ignacio Segovia-Dominguez, The University of Texas at Dallas
	Dynamic structure estimation of time-varying networks Presentation Ahyoung Amy Kim, The University of Arizona; Hongseok Ko, The University of Arizona; Xueying Tang; Hao Helen Zhang, University of Arizona
	Fast Functional Change Point Detection with Total Variation Denoising Trevor Harris, University of Illinois at Urbana-Champaign; Bo Li, University of Illinois at Urbana-Champaign; Derek Tucker, Sandia National Lab
	A Dynamic Neural ODE Model for Nonlinear Time Series Yijia Liu, Purdue University; Lexin Li, University of California, Berkeley; Xiao Wang, Purdue University
	Modeling Spiky Functional Data with Derivatives of Smooth Functions in Function-On-Function Regression Ruiyan Luo
	Completion of Partially Observed Curves with Application to Classification of Bovid Teeth Gregory Matthews, Loyola University Chicago; Ofer Harel, University of Connecticut; George Thiruvathukal, Loyola University Chicago; Juliet Brophy, Louisiana State University; Sebastian Kurtek, The Ohio State University; Karthik Bharath, University of Nottingham
	High-Dimensional Dynamic Pricing with Online Tuning Chi-Hua Wang; Zhanyu Wang, Purdue University; Wei Sun, Purdue University; Guang Cheng, Purdue University

524 * !	Thu, 8/6/2020, 1:00 PM - 2:50 PM	Virtual
Emerging Statistical Learning Methods in Modern Data Science — Invited Papers
Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, IMS
Organizer(s): Ping Ma, University of Georgia
Chair(s): Ping Ma, University of Georgia
1:05 PM	A Powerful AI Tool for CHD Screening Wenxuan Zhong, Department of Statistics, University of Georgia
1:30 PM	A Community Model for Partially Observed Networks from Surveys Tianxi Li, University of Virginia; Elizaveta Levina, University of Michigan; Ji Zhu, University of Michigan
1:55 PM	Reverse Engineering a Deep Network Presentation Douglas Nychka, Colorado School of Mines
2:20 PM	Statistical Methods for Some Problems in Physics Larry Wasserman, Carnegie Mellon University
2:45 PM	Floor Discussion

538 !	Thu, 8/6/2020, 1:00 PM - 2:50 PM	Virtual
Emerging Topics in Private Data Analysis — Topic Contributed Papers
IMS, Section on Statistical Learning and Data Science, Royal Statistical Society
Organizer(s): Weijie Su, University of Pennsylvania
Chair(s): Weijie Su, University of Pennsylvania
1:05 PM	Differentially Private Mean and Covariance Estimation Presentation Gautam Kamath, University of Waterloo
1:25 PM	KNG: The K-Norm Gradient Mechanism Jordan Awan, Penn State University; Matthew Reimherr, Penn State University
1:45 PM	Locally Private Learning, Estimation, Inference and Optimality Feng Ruan, University of California at Berkeley
2:05 PM	Gaussian Differential Privacy, with Applications to Deep Learning Jinshuo Dong, University of Pennsylvania; Aaron Roth, University of Pennsylvania; Weijie Su, University of Pennsylvania
2:25 PM	Discussant: Xiao-Li Meng, Harvard University
2:45 PM	Floor Discussion

546	Thu, 8/6/2020, 1:00 PM - 2:50 PM	Virtual
Foundational Issues in Machine Learning — Topic Contributed Papers
Section on Statistical Learning and Data Science, Uncertainty Quantification in Complex Systems Interest Group, Section on Risk Analysis, Section on Statistical Computing
Organizer(s): Bertrand Clarke, U. Nebraska-Lincoln
Chair(s): Tri Le, Mercer Univ - Atlanta
1:05 PM	Joint Robust Multiple Inference on Large-Scale Multivariate Regression Wen Zhou, Colorado State University; Wenxin Zhou, University of California, San Diego; Youngseok Song, Colorado State University
1:25 PM	BET on Independence Kai Zhang, UNC Chapel Hill
1:45 PM	Sparse Logistic Classification Using Multi-Type Predictors Arkaprava Roy, University of Florida; Bertrand Clarke, U. Nebraska-Lincoln; Subhashis Ghosal, NCSU; Diego Jarquin, UNL
2:05 PM	Floor Discussion

548 * !	Thu, 8/6/2020, 1:00 PM - 2:50 PM	Virtual
Using Artificial Intelligence and Advanced Statistical Methods to Improve Official Statistics — Topic Contributed Papers
Government Statistics Section, Section on Statistical Learning and Data Science, Survey Research Methods Section
Organizer(s): Barry W Johnson, Statistics of Income, Internal Revenue Service
Chair(s): Barry W Johnson, Statistics of Income, Internal Revenue Service
1:05 PM	Recommender Algorithms for Form Anomaly Detection William Roberts, Deloitte; Anne Parker, Internal Revenue Service; Danielle Gewurz, Deloitte
1:25 PM	Statistically Robust Estimation of Unprotected Identity Theft in Individual Tax Returns: A Non-Parametric Simulation Based Approach Sabyasachi Guharay, US Internal Revenue Service
1:45 PM	NAICS Code Prediction Using Supervised Methods Presentation Anne Parker, Internal Revenue Service; Evan Schulz, Internal Revenue Service; Christine Oehlert, Internal Revenue Service
2:05 PM	An Active Learning Approach for Collecting Tax Revenue Kristen Altenburger, Stanford University; Brandon Anderson, Stanford University; Ben Chugg, Stanford University; Jacob Goldin, Stanford University; Daniel En-Wenn Ho, Stanford Law School; Ahmad Qadri, Internal Revenue Service; Evelyn Smith, University of Michigan
2:25 PM	Discussant: Karl Branting, MITRE Corporation
2:45 PM	Floor Discussion

553 * !	Thu, 8/6/2020, 3:00 PM - 4:50 PM	Virtual
Improving Patient Care Through Personalized/Stratified Medicine: Modern Data Science and Perspectives from Pharmaceutical Industry Leads and Regulatory — Invited Papers
Biopharmaceutical Section, Section on Statistical Learning and Data Science, Section on Statistics and Data Science Education
Organizer(s): Wen Li, Merck
Chair(s): Ilya Lipkovich, Eli LIlly and Company
3:05 PM	Machine Learning, AI and Personalized Intervention Haoda Fu, Eli Lilly and Company
3:30 PM	Pruned Targeted-Learning for Personalized Medicine Yixin Fang, AbbVie Inc.
3:55 PM	PRISM: Patient Response Identifiers for Stratified Medicine Thomas Jemielita, Merck & Co., Inc; Devan Mehrotra, Merck
4:20 PM	Discussant: Maria Matilde S. Kam, OTS/CDER, FDA
4:45 PM	Floor Discussion

559 * !	Thu, 8/6/2020, 3:00 PM - 4:50 PM	Virtual
Foundations of Data Science: The TRIPODS Experience — Invited Papers
Section on Statistical Learning and Data Science, Committee on Funded Research, Section on Bayesian Statistical Science, Section on Physical and Engineering Sciences, Quality and Productivity Section
Organizer(s): Scott H. Holan, University of Missouri
Chair(s): Catherine Calder, University of Texas at Austin
3:05 PM	Build a Data Science Team Hao Helen Zhang, University of Arizona
3:25 PM	Transdisciplinary Research Institute for Advancing Data Science (TRIAD @ Georgia Tech) Xiaoming Huo, Georgia Institute of Technology
3:45 PM	Foundations of Data Science: Dynamical, Statistical and Economic Perspectives Presentation Michael I. Jordan, University of California, Berkeley
4:05 PM	Riemannian Embedding Models for Relational Data Abel Rodriguez, University of California, Santa Cruz
4:45 PM	Floor Discussion

580 * !	Thu, 8/6/2020, 3:00 PM - 4:50 PM	Virtual
Statistical and Computational Challenges in Nonparametric Learning — Topic Contributed Papers
Section on Nonparametric Statistics, Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Lingzhou Xue, Penn State University and NISS
Chair(s): Lingzhou Xue, Penn State University and NISS
3:05 PM	A Scale Invariant Approach for Sparse Signal Recovery Yifei Lou, University of Texas At Dallas
3:25 PM	Learning Non-Monotone Optimal Individualized Treatment Regimes Trinetri Ghosh, Pennsylvania State University; Yanyuan Ma, The Pennsylvania State University ; Wensheng Zhu, Northeast Normal University
3:45 PM	Nonparametric Screening Under Conditional Strictly Convex Loss for Ultrahigh Dimensional Sparse Data Xu Han, Temple University
4:05 PM	A Screening Algorithm for Cross-Validated Kernel Support Vector Machines Boxiang Wang, University of Iowa; Yi Yang, McGill University
4:25 PM	Floor Discussion

581 * !	Thu, 8/6/2020, 3:00 PM - 4:50 PM	Virtual
Advanced Cross-Disciplinary Statistical Methods in Statistical Genomics — Topic Contributed Papers
Section on Statistics in Genomics and Genetics, Section on Statistics in Epidemiology, Section on Statistical Learning and Data Science
Organizer(s): Suvo Chatterjee, National Institute of Child Health and Development (NICHD)/ National Institutes of Health
Chair(s): Shrabanti Chowdhury, Icahn school of Medicine at Mount Sinai
3:05 PM	Utilizing Patient Information to Identify Subtype Heterogeneity of Cancer Driver Genes Bin Zhu, NCI; Ho-Hsiang Wu, Food and Drug Administration; Xing Hua, Fred Hutchinson Cancer Research Center; Jianxin Shi, National Cancer Institute; Nilanjan Chatterjee, Johns Hopkins University
3:25 PM	Bayesian Functional Data Analysis Over Dependent Regions and Its Application for Identification of Differentially Methylated Regions Suvo Chatterjee, National Institute of Child Health and Development (NICHD)/ National Institutes of Health; Shrabanti Chowdhury, Icahn school of Medicine at Mount Sinai; Duchwan Ryu, Northern Illinois University; Fasil Tekola Ayele, NICHD/NIH
3:45 PM	A Bayesian Precision Medicine Framework for Calibrating Individualized Therapeutic Indices in Cancer ABHISEK SAHA, NICHD, NIH; Veera Baladandayuthapani, University of Michigan; Min Jin Ha, UT MD Anderson Cancer Center
4:05 PM	Multi-Resolution Clustering of Omics Data for Pattern Discovery Presentation Ali Rahnavard, George Washington University
4:25 PM	Multiomics Analysis of the Immunome, Transcriptome, Microbiome, Proteome, and Metabolome in Pregnancy Nima Aghaeepour, Stanford University - Stanford, CA
4:45 PM	Floor Discussion

583 !	Thu, 8/6/2020, 3:00 PM - 4:50 PM	Virtual
Learning Network Structure in Heterogeneous Populations — Topic Contributed Papers
Section on Statistical Learning and Data Science, Royal Statistical Society, International Indian Statistical Association
Organizer(s): Sandipan Roy, University of Bath
Chair(s): Sandipan Roy, University of Bath
3:05 PM	Modeling Network Time Series Using Generalized Network AutoRegression (GNAR) Kathryn Leeming, University of Warwick; Marina Knight, University of York; Guy Nason, Imperial College London; Matthew Nunes, University of Bath
3:25 PM	Sparse Locally-Stationary Wavelet Processes Alexander Gibberd, Lancaster Unviersity
3:45 PM	Efficient Estimation of Change Points in Regime Switching Dynamic Markov Random Fields Jing Ma, Texas A&M University
4:05 PM	Fast Algorithms for Detection of Structural Breaks in High-Dimensional Data George Michailidis, University of Florida
4:25 PM	Optimistic Binary Segmentation with an Application in Change Point Detection Methodologies for Graphical Models in the Presence of Missing Values Solt Kovács, ETH Zurich; Peter Bühlmann, ETH Zurich; Lorenz Haubner, ETH Zurich; Housen Li, University of Göttingen; Malte Londschien, ETH Zurich; Axel Munk, University of Göttingen
4:45 PM	Floor Discussion

585	Thu, 8/6/2020, 3:00 PM - 4:50 PM	Virtual
Bayesian Neural Networks — Topic Contributed Papers
International Society for Bayesian Analysis (ISBA), Section on Bayesian Statistical Science, Section on Statistical Learning and Data Science, Text Analysis Interest Group
Organizer(s): Deborshee Sen, Duke University
Chair(s): Rudradev Sengupta, Janssen Pharmaceutical Companies of Johnson and Johnson, Beerse, Belgium
3:05 PM	Bayesian Dimension Reduction Using Neural Networks Presentation Deborshee Sen, Duke University; David Dunson, Duke University; Theodore Papamarkou, Oak Ridge National Laboratory
3:25 PM	Bayesian Deep Net GLM and GLMM Robert Kohn, University of New South Wales; Minh Ngoc Tran, The University of Sydney
3:45 PM	Challenges in Bayesian Inference via Markov Chain Monte Carlo for Neural Networks Theodore Papamarkou
4:05 PM	Practical Bayesian Inference for Shallow CNNs in NLP Jacob Hinkle, Oak Ridge National Lab; Devanshu Agrawal, Oak Ridge National Lab; Theodore Papamarkou, Oak Ridge National Laboratory
4:25 PM	Floor Discussion

JSM 2020 Online Program

Activity Details

American Statistical Association