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

Online Program Home
My Program

All Times EDT

Legend:
* = 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
Statistical Inference for Probabilistic Graphical Models with Applications — Invited Papers

11:45 AM	Floor Discussion

286	Wed, 8/5/2020, 10:00 AM - 11:50 AM
Learning Networks from Point Processes: Neuronal Connectivity Networks and Beyond — Invited Papers

11:45 AM	Floor Discussion

299 !	Wed, 8/5/2020, 10:00 AM - 11:50 AM
Machine Learning in Causal Inference with Applications in Complicated Settings — Topic Contributed Papers

11:45 AM	Floor Discussion

301 *	Wed, 8/5/2020, 10:00 AM - 11:50 AM
Natural Language Processing Applications in Defense and National Security — Topic Contributed Papers

11:45 AM	Floor Discussion

306	Wed, 8/5/2020, 10:00 AM - 11:50 AM
Algorithmic and Inferential Advances in Univariate and Multivariate Tuning-Parameter-Free Nonparametric Procedures — Topic Contributed Papers

11:45 AM	Floor Discussion

309 *	Wed, 8/5/2020, 10:00 AM - 11:50 AM
Interface Between Machine Learning and Uncertainty Quantification — Topic Contributed Papers

11:25 AM	Floor Discussion

354	Wed, 8/5/2020, 10:00 AM - 2:00 PM
Multivariate Analysis and Graphical Models — Contributed Papers

355 !	Wed, 8/5/2020, 10:00 AM - 2:00 PM
Modern Model Selection — Contributed Papers

356	Wed, 8/5/2020, 10:00 AM - 2:00 PM
Statistical Learning: Methods and Applications — Contributed Papers

357	Wed, 8/5/2020, 10:00 AM - 2:00 PM
Contemporary Multivariate Methods — Contributed Papers

389 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM
Unsupervised Learning with Latent Variables for Biobehavioral Research — Invited Papers

2:45 PM	Floor Discussion

392 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM
Big Tensor Data Analysis — Invited Papers

2:45 PM	Floor Discussion

394 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM
Challenges and New Directions in Precision Medicine for Large-Scale and Complex Data — Invited Papers

2:40 PM	Floor Discussion

395 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM
The Need for Interpretable and Fair Algorithms in Health Policy — Invited Panel

2:40 PM	Floor Discussion

398 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM
Beyond Traditional Approaches: Evolving Artificial Intelligence and Machine Learning to Advance Clinical Research and Drug Development — Topic Contributed Papers

2:25 AM	Floor Discussion

403 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM
Sufficient Dimension Reduction and Variable Selection for High-Dimensional Inference — Topic Contributed Papers

2:25 PM	Floor Discussion

406 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM
Advances of Statistical Methodologies in Proteogenomics Research — Topic Contributed Papers

2:45 AM	Floor Discussion

412 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM
Incorporation of Real-World Evidence in Clinical Trial Designs and Associated Statistical Methodologies — Topic Contributed Papers

2:45 PM	Floor Discussion

417 * !	Wed, 8/5/2020, 1:00 PM - 2:50 PM
Best Practices for Preparing Students for a Career in Business Analytics/Data Science — Topic Contributed Panel

2:45 PM	Floor Discussion

421 !	Thu, 8/6/2020, 10:00 AM - 11:50 AM
Recent Advances in Time Series and Temporal Data Analysis — Invited Papers

11:45 AM	Floor Discussion

429 * !	Thu, 8/6/2020, 10:00 AM - 11:50 AM
Strategic Statistical Partnerships Impacting Health and Education for the Public Good — Invited Papers

11:45 AM	Floor Discussion

438	Thu, 8/6/2020, 10:00 AM - 11:50 AM
Statistical Methods for Topological Data Analysis — Topic Contributed Papers

11:45 AM	Floor Discussion

444 !	Thu, 8/6/2020, 10:00 AM - 11:50 AM
Highlights from the Journal STAT — Topic Contributed Papers

11:45 AM	Floor Discussion

448 !	Thu, 8/6/2020, 10:00 AM - 11:50 AM
The Contribution of Convex Optimization to New Statistical Concepts — Topic Contributed Papers

11:45 AM	Floor Discussion

455 * !	Thu, 8/6/2020, 10:00 AM - 11:50 AM
Big Data, Technology Platform and Digital Innovation with Measurable Impact — Topic Contributed Panel

11:45 AM	Floor Discussion

495 !	Thu, 8/6/2020, 10:00 AM - 2:00 PM
Statistical Methods for Networks — Contributed Papers

496 !	Thu, 8/6/2020, 10:00 AM - 2:00 PM
Dimension Reduction — Contributed Papers

497 !	Thu, 8/6/2020, 10:00 AM - 2:00 PM
Variable Selection — Contributed Papers

498	Thu, 8/6/2020, 10:00 AM - 2:00 PM
Modern Machine Learning — Contributed Papers

499	Thu, 8/6/2020, 10:00 AM - 2:00 PM
New Methods for Machine Learning — Contributed Papers

500	Thu, 8/6/2020, 10:00 AM - 2:00 PM
Statistical Learning — Contributed Papers

501	Thu, 8/6/2020, 10:00 AM - 2:00 PM
Time Series Methods — Contributed Papers

524 * !	Thu, 8/6/2020, 1:00 PM - 2:50 PM
Emerging Statistical Learning Methods in Modern Data Science — Invited Papers

2:45 PM	Floor Discussion

538 !	Thu, 8/6/2020, 1:00 PM - 2:50 PM
Emerging Topics in Private Data Analysis — Topic Contributed Papers

2:45 PM	Floor Discussion

546	Thu, 8/6/2020, 1:00 PM - 2:50 PM
Foundational Issues in Machine Learning — Topic Contributed Papers

2:05 PM	Floor Discussion

548 * !	Thu, 8/6/2020, 1:00 PM - 2:50 PM
Using Artificial Intelligence and Advanced Statistical Methods to Improve Official Statistics — Topic Contributed Papers

2:45 PM	Floor Discussion

553 * !	Thu, 8/6/2020, 3:00 PM - 4:50 PM
Improving Patient Care Through Personalized/Stratified Medicine: Modern Data Science and Perspectives from Pharmaceutical Industry Leads and Regulatory — Invited Papers

4:45 PM	Floor Discussion

559 * !	Thu, 8/6/2020, 3:00 PM - 4:50 PM
Foundations of Data Science: The TRIPODS Experience — Invited Papers

4:45 PM	Floor Discussion

580 * !	Thu, 8/6/2020, 3:00 PM - 4:50 PM
Statistical and Computational Challenges in Nonparametric Learning — Topic Contributed Papers

4:25 PM	Floor Discussion

581 * !	Thu, 8/6/2020, 3:00 PM - 4:50 PM
Advanced Cross-Disciplinary Statistical Methods in Statistical Genomics — Topic Contributed Papers

4:45 PM	Floor Discussion

583 !	Thu, 8/6/2020, 3:00 PM - 4:50 PM
Learning Network Structure in Heterogeneous Populations — Topic Contributed Papers

4:45 PM	Floor Discussion

585	Thu, 8/6/2020, 3:00 PM - 4:50 PM
Bayesian Neural Networks — Topic Contributed Papers

4:25 PM	Floor Discussion

JSM 2020 Online Program

Activity Details

American Statistical Association