Name: 2021 Joint Statistical Meetings
Start: 2021-08-08T07:00:00+00:00
End: 2021-08-12

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* = applied session ! = JSM meeting theme

Activity Details

7 * !	Sun, 8/8/2021, 1:30 PM - 3:20 PM	Virtual
Point Process Modeling and Estimation on Modern Recurrent Event Data — Invited Papers
Section on Statistical Learning and Data Science, WNAR, International Chinese Statistical Association
Organizer(s): Shizhe Chen, University of California, Davis
Chair(s): Shizhe Chen, University of California, Davis
1:35 PM	Causal Inference from Multi-Experiment Point Process Data Ali Shojaie, University of Washington
2:15 PM	Detecting Abrupt Changes in High-Dimensional Self-Exciting Poisson Processes Rebecca Willett, University of Chicago; Daren Wang, University of Notre Dame; Yi Yu, University of Warwick
2:35 PM	Marked Point Process Modeling and Estimation Problems in Neural Data Analysis Uri Eden, Boston University
2:55 PM	Discussant: Victor Solo, UNSW, Sydney
3:15 PM	Floor Discussion

20 * !	Sun, 8/8/2021, 1:30 PM - 3:20 PM	Virtual
Industrial Data Science: Transforming Analytics into Business Impact — Topic-Contributed Papers
Section on Statistical Learning and Data Science, Committee on Career Development, International Chinese Statistical Association
Organizer(s): Qiaolin Chen, Tencent
Chair(s): Liang Xie, Tencent
1:35 PM	Level-Up: Transforming the Gaming Industry with Machine Learning and Big Data Analytics Qiaolin Chen, Tencent; Zeng Zhao, Tencent; Botao Li, Tencent
1:55 PM	Data Science in Online Advertising: Differentially Private Reach and Frequency Estimation for Effectiveness Measurement Jiayu Peng, Google Inc.
2:15 PM	Understanding Online Behaviors of Users with Neural Sequence Models Shiwen Zhao, Apple Inc.
2:35 PM	Privacy Risk Evaluation for Deep Neural Networks Sen Yuan, Facebook
2:55 PM	Floor Discussion

31 !	Sun, 8/8/2021, 3:30 PM - 5:20 PM	Virtual
Recent Developments in Learning Across Environments — Invited Papers
IMS, International Indian Statistical Association, Section on Statistical Learning and Data Science, Caucus for Women in Statistics
Organizer(s): Moulinath Banerjee, University of Michigan
Chair(s): Bodhisattva Sen, Columbia University
3:35 PM	Adaptive Transfer Learning Richard J. Samworth, University of Cambridge; Henry Reeve, Bristol University; Timothy Cannings, University of Edinburgh
4:00 PM	Some Recent Insights on Transfer-Learning Samory K. Kpotufe, Columbia University
4:25 PM	There Is No Trade-Off: Enforcing Fairness Can Improve Accuracy Yuekai Sun, University of Michigan
4:50 PM	Discussant: Moulinath Banerjee, University of Michigan
5:10 PM	Floor Discussion

39 !	Sun, 8/8/2021, 3:30 PM - 5:20 PM	Virtual
Recent Advances on Causal Inference and Mediation Analysis — Invited Papers
WNAR, Section on Statistics in Imaging, Section on Statistical Learning and Data Science
Organizer(s): Lexin Li, University of California, Berkeley
Chair(s): Qingyuan Zhao, University of Cambridge
3:35 PM	Assumption-Lean Causal Inference for Direct and Indirect Effects Stijn Vansteelandt, Ghent University; Oliver Hines, LSHTM
4:00 PM	Testing Directed Acyclic Graph via Structural, Supervised, and Generative Adversarial Learning Chengchun Shi, LSE; Yunzhe Zhou, University of California, Berkeley; Lexin Li, University of California, Berkeley
4:25 PM	Causality in Cognitive Neuroscience: Leveraging Distributional Robustness Sebastian Weichwald, University of Copenhagen; Jonas Peters, University of Copenhagen
4:50 PM	Testing Mediation Effects Using Logic of Boolean Matrices Lexin Li, University of California, Berkeley; Chengchun Shi, LSE
5:15 PM	Floor Discussion

44 * !	Sun, 8/8/2021, 3:30 PM - 5:20 PM	Virtual
Creating Data for Good (D4G) Programs: Roadmap and Research Highlights — Invited Panel
Social Statistics Section, Section on Statistics and Data Science Education, Section on Statistical Learning and Data Science, Caucus for Women in Statistics
Organizer(s): Gizem Korkmaz, University of Virginia; Sarah Stone, eScience Institute & UW Data Science for Social Good (DSSG)
Chair(s): Gizem Korkmaz, University of Virginia
3:35 PM	Creating Data for Good (D4G) Programs: Roadmap and Research Highlights
Panelists:	Sarah Stone, eScience Institute & UW Data Science for Social Good (DSSG) Dan Richard, University of North Florida Chiara Sabatti, Stanford University Cassandra Dorius, Iowa State University Kit Rodolfa, Carnegie Mellon University Dharma Dailey, University of Washington
5:10 PM	Floor Discussion

47 !	Sun, 8/8/2021, 3:30 PM - 5:20 PM	Virtual
Geometric and Topological Information in Data Analysis — Topic-Contributed Papers
IMS, Section on Statistical Learning and Data Science, Section on Statistics in Imaging
Organizer(s): Hengrui Luo, Lawrence Berkeley National Laboratory
Chair(s): Chul Moon, Southern Methodist University
3:35 PM	Characterizing Heterogenous Information in Persistent Homology with Applications to Molecular Structure Modeling Zixuan Cang, University of California, Irvine; Guowei Wei, Michigan State Univesity
3:55 PM	Gromov-Wasserstein Learning in a Riemannian Framework Samir Chowdhury, Stanford University
4:15 PM	Density Estimation and Modeling on Symmetric Spaces Didong Li, Princeton University; Yulong Lu, University of Massachusetts Amherst; Emmanuel Chevallier, Aix Marseille University; David Dunson, Duke University
4:35 PM	Convergence of Persistence Diagram in the Subcritical Regime Takashi Owada, Purdue University, Department of Statistics
4:55 PM	Combining Geometric and Topological Information for Boundary Estimation Justin Strait, University of Georgia; Hengrui Luo, Lawrence Berkeley National Laboratory
	Discussant: Hengrui Luo, Lawrence Berkeley National Laboratory
5:15 PM	Floor Discussion

49 * !	Sun, 8/8/2021, 3:30 PM - 5:20 PM	Virtual
Recent Advances in Statistical Inference on Graphs — Topic-Contributed Papers
Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Lyndsay Shand, Sandia National Laboratories
Chair(s): Danny Ries, Sandia National Laboratories
3:35 PM	Bayesian Regression with Undirected Network Predictors with an Application to Brain Connectome Data Sharmistha Guha, Duke University; Abel Rodriguez, University of Washington
3:55 PM	Inferring Influence Networks from Longitudinal Bipartite Relational Data Presentation Frank Marrs, Los Alamos National Laboratory; Bailey K Fosdick, Colorado State University; Benjamin W Campbell, Cover My Meds; Skyler J Cranmer, The Ohio State University; Tobias Böhmelt, University of Essex
4:15 PM	A Deterministic Hitting-Time Moment Approach to Seed-Set Expansion Over a Graph Alexander Foss, Sandia National Laboratories
4:35 PM	Bayesian Graphical Modeling of Microbial Community Composition Kurtis Shuler, UCSC; Juhee Lee, University of California, Santa Cruz; Irene Chen, UCLA; Samuel Verbanic, UCLA
4:55 PM	A Latent Space Approach for Interdependent Ego-Networks with Application to Criminal Networks Isabella Gollini, University College Dublin; Paolo Campana, University of Cambridge; Alberto Caimo, Technological University Dublin
5:15 PM	Floor Discussion

76 * !	Mon, 8/9/2021, 10:00 AM - 11:50 AM	Virtual
Recent Advances in Multivariate Analysis for Modern Scientific Studies and Application — Topic-Contributed Papers
Korean International Statistical Society, WNAR, Section on Statistical Learning and Data Science
Organizer(s): Yeonhee Park, University of Wisconsin
Chair(s): Min Jin Ha, University of Texas MD Anderson Cancer Center
10:05 AM	Pseudo Estimation and Variable Selection in Regression Wenbo Wu, University of Texas at San Antonio
10:25 AM	Response Variable Selection in Multivariate Linear Regression Zhihua Su, University of Florida
10:45 AM	Envelope-Based Partial Least Squares with Application to Cytokine-Based Biomarker Analysis for COVID-19 Yeonhee Park, University of Wisconsin; Zhihua Su, University of Florida; Dongjun Chung, Ohio State University
11:05 AM	Tensor Envelope Mixture Model for Simultaneous Clustering and Multiway Dimension Reduction Kai Deng, Florida State University; Xin Zhang, Florida State University
11:25 AM	A Comprehensive Bayesian Framework for Envelope Models Saptarshi Chakraborty, University at Buffalo; Zhihua Su, University of Florida
11:45 AM	Floor Discussion

78 *	Mon, 8/9/2021, 10:00 AM - 11:50 AM	Virtual
Key Steps in Deriving Real-World Evidence from Analysis of Real-World Data — Topic-Contributed Papers
Biopharmaceutical Section, Biometrics Section, Section on Statistical Learning and Data Science
Organizer(s): Yixin Fang, AbbVie
Chair(s): Weili He, AbbVie
10:05 AM	Estimands: From Concepts to Applications in Real-World Setting (RWS) Jie Chen, On behalf of ASA Biop RWE SWG Estimand Team
10:25 AM	Principles and Approaches for the Use and Evaluation of Fit-for-Purpose, Real-World Data Sources Mark Levenson, FDA; Weili He, AbbVie
10:45 AM	Examples of Applying Causal-Inference Roadmap to RWE Clinical Studies Presentation Martin Ho, Google; Yixin Fang, AbbVie
11:05 AM	From Sensitivity Analysis to Evidence-Value: Ways to Interpret Real-World Evidence Yixin Fang, AbbVie ; Weili He, AbbVie
11:25 AM	Discussant: Hana Lee, FDA
11:45 AM	Floor Discussion

79 !	Mon, 8/9/2021, 10:00 AM - 11:50 AM	Virtual
Measure Transportation-Based Statistical Inference — Topic-Contributed Papers
IMS, Section on Nonparametric Statistics, Section on Statistical Learning and Data Science
Organizer(s): Fang Han, University of Washington
Chair(s): Fang Han, University of Washington
10:05 AM	Fully Distribution-Free Center-Outward Rank Tests for Multiple-Output Regression and MANOVA Presentation Marc Hallin, Université libre de Bruxelles
10:25 AM	Distribution-Free Consistent Independence Tests via Center-Outward Ranks and Signs Mathias Drton, Technical University of Munich; Marc Hallin, Université libre de Bruxelles; Fang Han, University of Washington; Hongjian Shi, University of Washington
10:45 AM	From Smooth Wasserstein Distance to Dual Sobolev Norm: Empirical Approximation and Statistical Applications Kengo Kato, Cornell University; Ziv Goldfeld, Cornell University; Sloan Nietert, Cornell University
11:05 AM	Optimal Transport for Fairness in Machine Learning jean michel loubes, University of Toulouse
11:25 AM	Consistent Estimation of Optimal Transport Plans Johan Segers, UCLouvain
11:45 AM	Floor Discussion

80 * !	Mon, 8/9/2021, 10:00 AM - 11:50 AM	Virtual
Sufficient Dimension Reduction and Applications — Topic-Contributed Papers
Section on Statistical Learning and Data Science, Section on Statistical Computing, Biometrics Section
Organizer(s): Ruth Pfeiffer, NCI-DCEG
Chair(s): Bin Zhu, National Cancer Institute
10:05 AM	Real-Time Sufficient Dimension Reduction Through Principal Least-Squares Support-Vector Machines Yuexiao Dong, Temple University; Andreas Artemiou, Cardiff University; Seung Jun Shin, Korea University
10:25 AM	Dimension Reduction for Multimodal Data Integration Xin Zhang, Florida State University
10:45 AM	Least Squares and Maximum Likelihood Estimation of Sufficient Reductions in Regressions with Matrix Valued Predictors Efstathia Bura, TU Wien; Daniel Kapla, TU Wien; Ruth Pfeiffer, NCI-DCEG
11:05 AM	PLS Regression Algorithms in the Presence of Nonlinearity Liliana Forzani, Universidad Nacional del Litoral; Dennis Cook, University of Minnesota
11:25 AM	Floor Discussion

81 * !	Mon, 8/9/2021, 10:00 AM - 11:50 AM	Virtual
Statistical and Machine Learning Efforts on Solar Flare Predictions I — Topic-Contributed Papers
Section on Physical and Engineering Sciences, Astrostatistics Special Interest Group, Section on Statistical Learning and Data Science
Organizer(s): Yang Chen, University of Michigan
Chair(s): Yang Chen, University of Michigan
10:05 AM	The Statistical Challenges of Solar Flare Forecasting (and) the Discriminant Analysis Flare Forecasting System at NWRA KD Leka, NorthWest Research Associates; Graham Barnes, NorthWest Research Associates
10:25 AM	Multivariate Time Series Data Set for Space Weather Data Analytics Rafal Angryk, Georgia State University
10:45 AM	Improving and Interpreting Flare Prediction with Spatial Statistics Analysis of the Magnet Field Data Hu Sun, University of Michigan, Ann Arbor; Ward Manchester, University of Michigan, Ann Arbor; Yang Chen, University of Michigan
11:05 AM	Floor Discussion

85 *	Mon, 8/9/2021, 10:00 AM - 11:50 AM	Virtual
Challenging Collaborations and Lessons Learned — Topic-Contributed Panel
Section on Statistical Consulting, Section on Statistics and Data Science Education, Section on Statistical Learning and Data Science, Caucus for Women in Statistics
Organizer(s): Harry Dean Johnson, Washington State University
Chair(s): Steve Simon, P. Mean Consulting
10:05 AM	Challenging Collaborations and Lessons Learned
Panelists:	Manisha Desai, Departments of Medicine and Biomedical Data Science Stanford University Elaine Eisenbeisz, OMEGA STATISTICS Kim Love, K. R. Love QCC Clark Kogan, Washington State University NAYAK L POLISSAR, The Mountain-Whisper-Ligtht: Statistics & Data Science
11:40 AM	Floor Discussion

101	Mon, 8/9/2021, 12:00 PM - 1:20 PM	Virtual
Section on Statistical Learning and Data Science P.M. Roundtable Discussion (Added Fee) — Roundtables PM Roundtable Discussion
Section on Statistical Learning and Data Science
ML12:	Statistical Learning on Large Population Studies: The Curse of Large P, Large N Prateek Sasan, The Ohio State University

106 * !	Mon, 8/9/2021, 1:30 PM - 3:20 PM	Virtual
New Frontiers and Developments in Causal Inference — Invited Papers
Section on Statistical Learning and Data Science
Organizer(s): Ronghui Xu, University of California at San Diego
Chair(s): Ronghui Xu, University of California at San Diego
1:35 PM	Multiply Robust Estimation of Causal Effects Under Principal Ignorability Peng Ding, UC Berkeley
2:00 PM	Robust Inference for Individualized Treatment Qingyuan Zhao, University of Cambridge
2:25 PM	Small Weights for Big Data: Computational Aspects and Empirical Performance Jose Zubizarreta, Harvard University; Kwangho Kim, Harvard University
2:50 PM	Multiple-Bias Sensitivity Analysis Using Bounds Presentation Louisa H. Smith, Harvard T.H. Chan School of Public Health; Maya Mathur, Stanford University; Tyler J VanderWeele, Harvard University
3:15 PM	Floor Discussion

120 !	Mon, 8/9/2021, 1:30 PM - 3:20 PM	Virtual
Challenges and Recent Advances in Private Data Analysis — Topic-Contributed Papers
Section on Nonparametric Statistics, IMS, CHANCE, Section on Statistical Learning and Data Science
Organizer(s): Linjun Zhang, Rutgers University
Chair(s): Linjun Zhang, Rutgers University
1:35 PM	The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower Bounds Yichen Wang, University of Pennsylvania; Tony Cai, University of Pennsylvania; Linjun Zhang, Rutgers University
1:55 PM	Differentially Private Statistics for Collaborative Neuroinformatics Anand Sarwate, Rutgers University
2:15 PM	Interactive Versus Non-Interactive Locally Differentially Private Estimation: Two Elbows for the Quadratic Functional Lukas Steinberger, University of Vienna
2:35 PM	A Central Limit Theorem and Uncertainty Principle for Differentially Private Query Answering Presentation Jinshuo Dong, Northwestern University
2:55 PM	High-Dimensional, Differentially-Private EM Algorithm: Methods and Near Optimal Statistical Guarantees Zhe Zhang, Rutgers University, New Brunswick; Linjun Zhang, Rutgers University
3:15 PM	Floor Discussion

129 * !	Mon, 8/9/2021, 1:30 PM - 3:20 PM	Virtual
Engaging Minority and Underserved Populations in Health Science: Education and Training for Careers in Statistics and Data Science — Topic-Contributed Panel
Section on Teaching of Statistics in the Health Sciences, Section on Statistical Learning and Data Science, Section on Statistics and Data Science Education
Organizer(s): Madhu Mazumdar, Icahn School of Medicine at Mount Sinai
Chair(s): Madhu Mazumdar, Icahn School of Medicine at Mount Sinai
1:35 PM	Engaging Minority and Underserved Populations in Health Science: Education and Training for Careers in Statistics and Data Science
Panelists:	Jeffrey Leek, Johns Hopkins Bloomberg School of Public Health Melanie Besculides , Icahn School of Medicine at Mount Sinai Veyom Bahl, Robin Hood Foundation Vivian Zhang, NYC Data Science Academy Doreen Thomann-Howe, New York City Department of Family Services
3:15 PM	Floor Discussion

132	Mon, 8/9/2021, 1:30 PM - 3:20 PM	Virtual
SLDS CSpeed 1 — Contributed Speed
Section on Statistical Learning and Data Science, Text Analysis Interest Group
Chair(s): Jaime Lynn Speiser, Wake Forest School of Medicine
1:35 PM	Applications of Machine Learning Methods to Identify Pediatric Patients with De Novo Acute Myeloid Leukemia from a Real-World Data Set Yimei Li, University of Pennsylvania
1:40 PM	Estimation of a Distribution Function with Increasing Failure Rate Average Ganesh B. Malla, University of Cincinnati-Clermont College
1:45 PM	A Semiparametric Complementary Log-Log Model with Applications in Rare Event Mining Cheng Peng, West Chester University of Pennsylvania; Kai Peng, Ningbo University of Technology
1:50 PM	A Deep Convolutional Neural Network Approach for Predicting Cumulative Incidence Based on Pseudo-Observations Pablo Gonzalez Gonzalez Ginestet, Karolinska Institutet; Philippe Weitz, Karolinska Institutet; Erin Gabriel, Karolinska Institutet; Mattias Rantalainen, Karolinska Institutet
1:55 PM	Subgroup Identification of Elderly Bladder Cancer Patients Based on Mental and Physical Scores: A Clustering Approach Mojgan Golzy, University of Missouri-Columbia; Katie Murray, University of Missouri-Columbia
2:00 PM	Impact of Tweets Pre-Processing Techniques on a Dictionary for Environment Camilla Salvatore, University of Bergamo; Daniele Toninelli, University of Bergamo; Michela Cameletti, University of Bergamo; Stephan Schlosser, Georg-August-Universität Göttingen
2:05 PM	Can Machine Learning Improve Correspondence Audit Case Selection? Considerations for Algorithm Selection, Validation, and Experimentation Lucia Lykke, The MITRE Corporation; Ben Howard, The MITRE Corporation; David Pinski, The MITRE Corporation; Alan Plumley, Internal Revenue Services
2:10 PM	Applying Machine Learning Methods for Insight into Textile Recycling Behavior Presentation Brandon King, North Carolina State University; Lori Rothenberg, North Carolina State University; Jeffrey Joines, North Carolina State University
2:15 PM	Empirical Comparison of Multiplicative and Tree-Based Interaction Predictive Models Chinedu Jude Nzekwe, North Carolina A&T State University; Sayed Mostafa, North Carolina A&T State University; Seong-Tae Kim, North Carolina A&T State University
2:20 PM	On Kernel-Target Alignment and Relevant Dimensions in Kernel Feature Spaces Ensuing from the Decision and Regression Tree Ensembles Dai Feng, AbbVie Inc.; Richard Baumgartner, Merck Research Laboratories
2:30 PM	Efficient Semi-Supervised Deep Learning and Machine Learning NLP System to Extract Clinical Measurements from Polysomnogram Laboratory Reports Ioannis Malagaris, University of Texas Medical Branch; David En Shuo Hsu, University of Texas Medical Branch; Yong-fang Kuo, University of Texas Medical Branch
2:35 PM	A Novel Regularized Neuro-Fuzzy Model for Chronic Diseases Outcome Prediction in Longitudinal Dietary Studies Venkata Sukumar Gurugubelli, University of Massachusetts Dartmouth; Hua Fang, University of Massachusetts Dartmouth
2:40 PM	Predicting Nursing Graduates Using Machine Learning Models Xiaoyue Cheng, University of Nebraska at Omaha; Li Hannaford, Creighton University; Mary Kunes-Connell, Creighton University
2:45 PM	Classification of Distinct Trajectories in Longitudinal Data with Irregularly Spaced Intervals: A Large Data Application of Post-Hoc Mixture Modeling of BLUPs from Mixed Models Md Jobayer Hossain, Nemours Children's Health System; Benjamin E Leiby, Thomas Jefferson University
2:50 PM	Worldwide Statistics Without Borders and Client to Consultant Bridge Collaboration: Statistical Storytelling in the Time of COVID Michal Czapski, Statistics Without Borders; Joshua Derenski, Statistics Without Borders; Stephen Godfrey, Statistics Without Borders; Michelle Vanchu-Orosco, Greater Victoria Coalition to End Homelessness; SWB
2:55 PM	Multiomics-Based Tensor Decomposition for Breast Cancer Subtyping Qian Liu, University of Manitoba; Bowen Cheng, University of Toronto; Pingzhao Hu, University of Manitoba
3:00 PM	A Prediction Model Method for Optimizing Appointment Overbooking in Health Care Clinics Using Electronic Health Care Record Data Nathaniel O'Connell, Wake Forest School of Medicine; Joseph Skelton, Wake Forest School of Medicine
3:05 PM	What Can Public Data Tell Us About the Quality of Life in Rural Small Towns? Denise Bradford, University of Nebraska- Lincoln; Susan VanderPlas, University of Nebraska - Lincoln
3:10 PM	What Makes You Unique? Ben Seiler, Stanford University; Art Owen, Stanford University; Masayoshi Mase, Hitachi, Ltd

139 * !	Tue, 8/10/2021, 10:00 AM - 11:50 AM	Virtual
Recent Advances of Semi-Supervised Learning: Techniques and Applications — Invited Papers
Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, ENAR
Organizer(s): Jiwei Zhao, University of Wisconsin-Madison
Chair(s): Jiwei Zhao, University of Wisconsin-Madison
10:05 AM	Optimal Semi-Supervised Estimation and Inference for High-Dimensional Linear Regression Yang Ning, Cornell University ; Jiwei Zhao, University of Wisconsin-Madison; Heping Zhang, Yale University
10:25 AM	Inference for Maximin Effects: A Sampling Approach to Aggregating Heterogenous High-Dimensional Regression Models Zijian Guo, Rutgers University
10:45 AM	Semi-Supervised Off Policy Reinforcement Learning Aaron Sonabend, Harvard University; Tianxi Cai, Harvard T.H. Chan School of Public Health
11:05 AM	Doubly Robust Semi-Supervised Inference for Means Under MAR-Type Labeling Mechanisms with Selection Bias and Decaying Propensity Scores Abhishek Chakrabortty, Texas A&M University
11:25 AM	Semi-Supervised Learning with Network Information Linda Zhao, University of Pennsylvania; Junhui Cai, University of Pennsylvania; Haipeng Shen, University of Hong Kong; Dan Yang, University of Hong Kong; Wu Zhu, University of Pennsylvania
11:45 AM	Floor Discussion

140 * !	Tue, 8/10/2021, 10:00 AM - 11:50 AM	Virtual
Change-Points in Multivariate and High-Dimensional Data — Invited Papers
Section on Nonparametric Statistics, IMS, Section on Statistical Learning and Data Science
Organizer(s): Piotr Fryzlewicz, London School of Economics
Chair(s): Qiwei Yao, London School of Economics and Political Science
10:05 AM	Inference for a Change Point in High-Dimensional Data via Self-Normalization Runmin Wang, Southern Methodist University; Changbo Zhu, University of California, Davis; Stanislav Volgushev, University of Toronto; Xiaofeng Shao, University of Illinois at Urbana-Champaign
10:25 AM	Jump or Kink: Super-Efficiency in Segmented Linear Regression Break-Point Estimation Yining Chen, London School of Economics
10:45 AM	Change-Point Detection for Multivariate and Non-Euclidean Data with Local Dependency Hao Chen, University of California, Davis
11:05 AM	Detection and Estimation of Signals in Space-Time Fields David Siegmund, Stanford University
11:25 AM	Discussant: Piotr Fryzlewicz, London School of Economics
11:40 AM	Floor Discussion

141 * !	Tue, 8/10/2021, 10:00 AM - 11:50 AM	Virtual
Bayesian Meets Basketball — Invited Papers
Section on Statistics in Sports, Section on Bayesian Statistical Science, Section on Statistical Learning and Data Science
Organizer(s): Guanyu Hu, University of Missouri - Columbia
Chair(s): Hal Stern, University of California Irvine
10:05 AM	Analysis of Professional Basketball Field Goal Attempts via a Bayesian Matrix Clustering Approach Guanyu Hu, University of Missouri - Columbia; Weining Shen, University of California Irvine ; Fan Ying, Microsoft
10:30 AM	It's Fun Getting into (Foul) Trouble Presentation Dani Chu, Seattle Kraken; Tim Swartz, Simon Fraser University
10:55 AM	Guard Me If You Can: Revealing the Hidden Dynamics of Sports Interactions Using the Intrinsic Dimension Edgar Santos-Fernandez, Queensland University of Technology; Francesco Denti, University of California, Irvine; Kerrie Mengersen, Queensland University of Technology; Antonietta Mira, Università della Svizzera italiana and University of Insubria
11:20 AM	Discussant: Shane Jensen, University of Pennsylvania
11:40 AM	Floor Discussion

143 * !	Tue, 8/10/2021, 10:00 AM - 11:50 AM	Virtual
New Machine Learning Tools for Mobile Health Data and Individual Intervention — Invited Papers
Lifetime Data Science Section, Section on Statistical Learning and Data Science, IMS, ENAR
Organizer(s): Annie Qu, Unviersity of California Irvine
Chair(s): Bin Nan, University of California Irvine
10:05 AM	Inference of Causal Relations with Interventions Chunlin Li, University of Minnesota; Xiaotong T Shen, University of Minnesota; Wei Pan, University of Minnesota
10:30 AM	Tracking Weight Loss Before and During Pregnancy Heping Zhang, Yale University
10:55 AM	Efficient Learning of Optimal Individualized Treatment Rules Weibin Mo, University of North Carolina; Yufeng Liu, University of North Carolina at Chapel Hill
11:20 AM	Supervised Learning of Health-Related Secret Codes from Wearable Devices Data Peter X.K. Song, University of Michigan
11:45 AM	Floor Discussion

146 !	Tue, 8/10/2021, 10:00 AM - 11:50 AM	Virtual
Statistical Reinforcement Learning — Invited Papers
Section on Statistical Computing, Section on Statistical Learning and Data Science, International Chinese Statistical Association
Organizer(s): Will Wei Sun, Purdue University
Chair(s): Emma Jingfei Zhang, University of Miami
10:05 AM	Bootstrapping Statistical Inference for Off-Policy Evaluation Mengdi Wang, Princeton University
10:30 AM	Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settings Rui Song, North Carolina State University
10:55 AM	Distribution-Free Contextual Dynamic Pricing Yiyun Luo, University of North Carolina at Chapel Hill; Will Wei Sun, Purdue University; Yufeng Liu, University of North Carolina at Chapel Hill
11:20 AM	On Efficiency in Hierarchical Reinforcement Learning Zheng Wen, DeepMind
11:45 AM	Floor Discussion

163 * !	Tue, 8/10/2021, 10:00 AM - 11:50 AM	Virtual
Understanding a Data-Rich Universe with Data-Driven Approaches — Topic-Contributed Panel
Section on Physical and Engineering Sciences, Section on Statistical Learning and Data Science, Astrostatistics Special Interest Group
Organizer(s): Joshua Shen Speagle, University of Toronto
Chair(s): Joshua Shen Speagle, University of Toronto
10:05 AM	Understanding a Data-Rich Universe with Data-Driven Approaches
Panelists:	Boris Leistedt, Imperial College London Aarya Patil, University of Toronto Stephen Portillo, University of Washington Miles Cranmer, Princeton University Kaisey Mandel, University of Cambridge
11:40 AM	Floor Discussion

165	Tue, 8/10/2021, 10:00 AM - 11:50 AM	Virtual
SLDS CSpeed 2 — Contributed Speed
Section on Statistical Learning and Data Science
Chair(s): Peter W. MacDonald, University of Michigan
10:05 AM	Causal Mediation Analysis Based on Partial Linear Models Xizhen Cai, Williams College; Yeying Zhu, University of Waterloo; Yuan Huang, Yale University
10:10 AM	Model Based, Imputed, and Assisted Approaches for Randomized Experiments Tianyi Qu, University of Illinois at Urbana-Champaign; Xinran Li, University of Illinois
10:15 AM	Adapting Random Forests for Use with Sample Survey Data Kevin Fenton, Ipsos Public Affairs; Robert Petrin, Ipsos Public Affairs; Peter Szczesny, Ipsos Public Affairs
10:20 AM	Risk Estimation in the Normal Means Problem via Auxiliary Randomization Natalia Lombardi de Oliveira, Carnegie Mellon University; Ryan Tibshirani, Carnegie Mellon University; Jing Lei, Carnegie Mellon University
10:25 AM	Two-Stage Procedure for Efficient and Robust Inference on Heterogeneous Treatment Effect in Randomized Clinical Trials Presentation Heng Chen, Fred Hutchinson Cancer Research Center; Michael LeBlanc, Fred Hutchinson Cancer Research Center; James Dai, Fred Hutchinson Cancer Research Center
10:30 AM	Adapting the Difference-in-Difference Technique to Study Change in Opioid Overdoses During the COVID-19 Pandemic Alexander Preiss, RTI International; Dalia Khoury, RTI International
10:35 AM	Missing Value Imputation of Network Node Attributes Presentation Simon Fontaine, University of Michigan; Ji Zhu, University of Michigan
10:40 AM	Multi-Level Biosensor-Based Epidemic Forecasting in Small Areas Salvador Balkus, University of Massachusetts Dartmouth; Hua Fang, University of Massachusetts Dartmouth; Honggang Wang, University of Massachusetts Dartmouth
10:45 AM	Factor Analysis of Data with Incomplete Records Fan Dai, Michigan Technological University; Somak Dutta, Iowa State University; Ranjan Maitra, Iowa State University
10:50 AM	Characterizing Smooth Trends and Irregular Spikes in Longitudinal Data Huy Dang, Penn State University; Marzia Cremona, Universite Laval; Francesca Chiaromonte, Penn State University
11:00 AM	Validity Check of Peer Assessment Schemes for Massive Open Online Courses Fangda SONG, The Chinese University of Hong Kong; Wai Yin Isabella Poon, The Chinese University of Hong Kong; Xinyuan Song, The Chinese University of Hong Kong; Yingying Wei, The Chinese University of Hong Kong
11:05 AM	A Time-to-Event Framework for Multi-Touch Attribution Dinah Shender, Google; Ali Nasiri Amini, Google; Xinlong Bao, Google; Mert Dikmen, Google; Amy Richardson, n/a; Jing Wang, Google
11:10 AM	Nonresponse Bias Study Presentation Forest Krueger, U.S. Census Bureau; Dhanapati Khatiwoda, U.S. Census Bureau; Kyle Jeong, U.S. Census Bureau
11:15 AM	Trustworthy and Powerful Online Marketplace Experimentation with Budget-Split Design Min Liu, LinkedIn; Vangelis Dimopoulos, LinkedIn; Jialiang Mao, LinkedIn; Kang Kang, LinkedIn
11:20 AM	A Tree-Based Federated Learning Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources Xiaoqing Ellen Tan, University of Pittsburgh; Chung-Chou H. Chang, University of Pittsburgh; Lu Tang, University of Pittsburgh
11:25 AM	Identifying Invariant Factors Across Multiple Environments with Kullback-Leibler Regression Jaime Roquero Gimenez, Stanford University; James Zou, Stanford University
11:30 AM	Assessing Time-Varying Causal Effect Moderation in the Presence of Cluster-Level Treatment Effect Heterogeneity Jieru Hera Shi, University of Michigan, Ann Arbor; Zhenke Wu, University of Michigan, Ann Arbor; Walter Dempsey, University of Michigan
11:35 AM	A semiparametric model for multivariate long-range dependent time series in the frequency domain Alexander Greaves-Tunnell, University of Washington
11:40 AM	Ensemble Learning for Ensuring Cross-Study Replicability of Boosting Cathy Wang, Harvard T.H. Chan School of Public Health; Pragya Sur, Harvard University; Prasad Patil, Boston University School of Public Health; Giovanni Parmigiani, Harvard University
11:45 AM	Detecting Multiple Replicating Signals Using Adaptive Filtering Procedures Lin Gui, The University of Chicago; Jingshu Wang, The University of Chicago; Weijie Su, University of Pennsylvania; Chiara Sabatti, Stanford University; Art Owen, Stanford University

184 !	Tue, 8/10/2021, 1:30 PM - 3:20 PM	Virtual
Recent Advances in Statistical Machine Learning — Invited Papers
IMS, Section on Statistical Learning and Data Science, Section on Nonparametric Statistics
Organizer(s): Jianqing Fan , Princeton University
Chair(s): Jianqing Fan , Princeton University
1:35 PM	Inhomogeneous-Word PCA for Estimating the Weights in a Topic Model Tracy Ke, Harvard University; Minzhe Wang, University of Chicago
2:00 PM	Provable Boolean Interaction Recovery from Tree Structures Obtained via Random Forests Bin Yu, University of California, Berkeley; Merle Behr, UC Berkeley; Yu Wang, University of California, Berkeley; Xiao Li, University of California, Berkeley
2:25 PM	High-Dimensional Principle Component Analysis with Heterogeneous Missingness Ziwei Zhu , University of Michigan, Ann Arbor; Tengyao Wang, University College London; Richard J. Samworth, University of Cambridge
2:50 PM	Fast Network Community Detection with Profile-Pseudo Likelihood Methods Ji Zhu, University of Michigan
3:15 PM	Floor Discussion

198 * !	Tue, 8/10/2021, 1:30 PM - 3:20 PM	Virtual
Highlights from STAT — Topic-Contributed Papers
International Statistical Institute, WNAR, Section on Statistical Learning and Data Science
Organizer(s): Hao Helen Zhang, University of Arizona
Chair(s): Hao Helen Zhang, University of Arizona
1:35 PM	Doubly Robust Estimation in Observational Studies with Partial Interference Lan Liu, University of Minnesota; Michael Hudgens, University of North Carolina; Bradley Saul, NoviSci; John Clemens, ICDDR; M Ali, Johns Hopkins; Mike Emch, University of North Carolina
1:55 PM	Set-Based Differential Covariance Testing for Genomics Yi-Hui Zhou, North Carolina State University
2:15 PM	Signal Dimension Estimation Using Principal Component Analysis Klaus Nordhausen, University of Jyväskylä; Joni Virta, University of Turku; Sara Taskinen, University of Jyväskylä
2:35 PM	Integrative Analysis of Longitudinal High-Dimensional Data with Time-Lagged Associations Yuping Zhang, University of Connecticut
2:55 PM	Flexible Clustering of High-Dimensional Data via Mixtures of Joint Generalized Hyperbolic Distributions Paul D McNicholas, McMaster University
3:15 PM	Floor Discussion

202	Tue, 8/10/2021, 1:30 PM - 3:20 PM	Virtual
SLDS Student Paper Awards — Topic-Contributed Papers
Section on Statistical Learning and Data Science
Organizer(s): Irina Gaynanova, Texas A&M University
Chair(s): Irina Gaynanova, Texas A&M University
1:35 PM	Exact Clustering in Tensor Block Model: Statistical Optimality and Computational Limit Rungang Han, University of Wisconsin-Madison; Yuetian Luo, University of Wisconsin-Madison; Miaoyan Wang, University of Wisconsin-Madison; Anru Zhang, University of Wisconsin-Madison
1:55 PM	Estimating the Number of Components in Finite Mixture Models via the Group-Sort-Fuse Procedure Presentation Tudor Manole, Carnegie Mellon University; Abbas Khalili, McGill University
2:15 PM	Distributed Community Detection in Large Networks Sheng Zhang, North Carolina State University; Rui Song, North Carolina State University; Wenbin Lu, North Carolina State University; Ji Zhu, University of Michigan
2:35 PM	Latent Space Models for Multiplex Networks with Shared Structure Peter W. MacDonald, University of Michigan; Liza Levina, University of Michigan; Ji Zhu, University of Michigan
2:55 PM	Population-Level Balance in Signed Networks: A Latent Space Approach Weijing Tang, University of Michigan; Ji Zhu, University of Michigan
3:15 PM	Floor Discussion

205	Tue, 8/10/2021, 1:30 PM - 3:20 PM	Virtual
Digital Phenotyping — Topic-Contributed Papers
Mental Health Statistics Section, Biometrics Section, Section on Statistical Learning and Data Science
Organizer(s): Samprit Banerjee, Weill Medical College of Cornell University
Chair(s): Jihui Lee, Weill Medical College of Cornell University
1:35 PM	Quantifying Environmental Exposures from Smartphone Data Jukka-Pekka Onnela, Harvard University; Aarti Sathyanarayana, Harvard University
1:55 PM	Digital Phenotyping for Predicting Depression with Weakly Labeled Data Samprit Banerjee, Weill Medical College of Cornell University; Hongzhe Zhang, Weill Medical College, Cornell University; Jihui Lee, Weill Medical College of Cornell University
2:15 PM	Within-Cluster Resampling with Informative Correlation Ian Barnett, University of Pennsylvania
2:35 PM	Next Steps in Federated Learning and Privacy Protection for Mobile and Digital Health Presentation Alexander Shen, University of Michigan; Ambuj Tewari, University of Michigan
2:55 PM	Distributional Data Analysis via Quantile Functions and Its Application to Modeling Digital Biomarkers of Gait in Alzheimer’s Disease Rahul Ghosal, Johns Hopkins Bloomberg School of Public Health; Vijay R. Varma, National Institute on Aging (NIA), National Institutes of Health (NIH); Dmitri Volfson, Neuroscience Analytics, Computational Biology, Takeda, Cambridge, MA, USA ; Inbar Hillel, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Jacek Urbanek, Johns Hopkins University School of Medicine; Jeffrey M. Hausdorff, Tel Aviv University, Tel Aviv, Israel; Amber Watts, Department of Psychology, University of Kansas; Vadim Zipunnikov, Johns Hopkins Bloomberg School of Public Health
3:15 PM	Floor Discussion

210	Tue, 8/10/2021, 1:30 PM - 3:20 PM	Virtual
SLDS CSpeed 3 — Contributed Speed
Section on Statistical Learning and Data Science
Chair(s): Trinetri Ghosh, Pennsylvania State University
1:35 PM	Facial Homophily in Human Social Networks Eric Justin Liu, Yale University; Marcus Alexander, Yale University; Derek Feng, Yale University; Nicholas A Christakis, Yale University
1:40 PM	Phase Transitions in Network Global Testing Louis Vincent Cammarata, Harvard University Statistics Department; Tracy Ke, Harvard University
1:45 PM	Semiparametric Estimation for Dynamic Network Models with Shifted Connecting Intensities Zitong Zhang, University of California, Davis; Shizhe Chen, University of California, Davis
1:50 PM	On Modularity Asymptotics in Large Structured Networks Anirban Mitra, University of Pittsburgh; Joshua Cape, University of Pittsburgh; Satish Iyengar, University of Pittsburgh
1:55 PM	Community Formation and Detection on OSS Collaboration Networks Behnaz Moradi-Jamei, University of Virginia; Brandon L. Kramer, University of Virginia; J. Bayoán Santiago Calderón, University of Virginia; Gizem Korkmaz, University of Virginia
2:00 PM	On the Asymptotics of Temporal Motif Estimation via Sampling Xiaojing Zhu, Boston University; Eric Kolaczyk, Boston University
2:05 PM	Identifying the Neurocognitive Difference Between Two Groups Using Supervised Learning Ramchandra Rimal, Middle Tennessee State University
2:10 PM	A Sparse Beta Model with Covariates for Networks Stefan Stein, University of Warwick; Chenlei Leng, University of Warwick
2:15 PM	Graph Matching Between Bipartite and Unipartite Networks: To Collapse or Not to Collapse, That Is the Question Jesus Arroyo Relión, Texas A&M University, Department of Statitics; Carey E Priebe, Johns Hopkins University; Vince Lyzinski, University of Maryland
2:20 PM	A Deterministic Filtering for Convolution with Multiple Resolution Modeling Presentation Haiyan Yu, Chongqing University of Posts and Telecommunications; Ching-chi Yang, University of Memphis
2:30 PM	Feature Selection Using a Metaheuristic Method Presentation Myung Soon Song, Kuztown University of Pennsylvania; Francis J Vasko, Kuztown University of Pennsylvania; Yun Lu, Kuztown University of Pennsylvania; Kyle Callaghan, Kuztown University of Pennsylvania
2:35 PM	Defining Opioid Prescribing Behaviors Christopher Teixeira, The MITRE Corporation; Jennifer Burrowes, The MITRE Corporation
2:45 PM	Extension of Rough Set Based on Positive Transitive Relation Min Shu, University of Wisconsin Stout; Wei Zhu, Stony Brook University
2:50 PM	Conditional Inference: Toward a Hierarchy of Statistical Evidence Ying Jin, Stanford University; Dominik Rothenhausler, Stanford University
2:55 PM	Statistical Learning for AI-Assisted Diagnosis Henry Horng-Shing Lu, National Yang Ming Chiao Tung University, Taiwan
3:00 PM	Performance of Parametric Versus Machine Learning Methods for Estimating Propensity Score with Multilevel Data: A Monte Carlo Study Tianyang Zhang, Teachers College, Columbia University; Bryan Keller, Teachers College, Columbia University

218 * !	Wed, 8/11/2021, 10:00 AM - 11:50 AM	Virtual
Leveraging and Advancing Deep Learning Techniques in Biomedical Related Fields — Invited Papers
International Chinese Statistical Association, Section on Statistical Learning and Data Science, Biometrics Section
Organizer(s): Jian Kang, University of Michigan; Tianyu Zhan, Data and Statistical Sciences, AbbVie Inc.
Chair(s): Tianyu Zhan, Data and Statistical Sciences, AbbVie Inc.
10:05 AM	Deep Learning in Pharma Industry Haoda Fu, Eli Lilly and Company
10:30 AM	Statistics Inferences on Neuroimaging Data via Deep Neural Networks Jian Kang, University of Michigan
10:55 AM	Efficient Multi-Modal Sampling via Tempered Distribution Flow Xiao Wang, Purdue University; Yixuan Qiu, Shanghai University of Finance and Economics
11:20 AM	A Representational Model of Grid Cells' Path Integration Based on Matrix Lie Algebras Presentation Ying Nian Wu, UCLA
11:45 AM	Floor Discussion

221	Wed, 8/11/2021, 10:00 AM - 11:50 AM	Virtual
Topics on Deep Learning — Invited Papers
IMS, Section on Statistical Learning and Data Science
Organizer(s): Xiaotong T Shen, University of Minnesota
Chair(s): Fei Xue, University of Pennsylvania
10:05 AM	Overparametrization in Linear Models, and the Uniform Consistency of Cross-Validation for Ridge Regression Pratik Patil, Carnegie Mellon University; Ryan Tibshirani, Carnegie Mellon University; Yuting Wei, Carnegie Mellon University; Alessandro Rinaldo, Carnegie Mellon University
10:35 AM	Significance Tests for Feature Relevance of a Black-Box Learner Ben Dai, University of Minnesota; Xiaotong T Shen, University of Minnesota; Wei Pan, University of Minnesota
11:05 AM	What Causes the Test Error? Going Beyond Bias-Variance via ANOVA Edgar Dobriban, University of Pennsylvania
11:35 AM	Floor Discussion

223 * !	Wed, 8/11/2021, 10:00 AM - 11:50 AM	Virtual
Recent Developments in Differential Privacy — Invited Papers
Section on Statistical Learning and Data Science, IMS, Section on Nonparametric Statistics
Organizer(s): Xuan Bi, University of Minnesota
Chair(s): Xuan Bi, University of Minnesota
10:05 AM	Differential Private Data Release Using Latent Factor Model Transform Annie Qu, Unviersity of California Irvine; Yanqing Zhang, Yunnan University; Niansheng Tang, Yunnan U
10:30 AM	Private Posterior Inference Consistent with Public Information Aleksandra Slavkovic, Penn State University
10:55 AM	Congenial Differential Privacy Under Mandated Disclosure Ruobin Gong, Rutgers University; Xiao-Li Meng, Harvard University
11:20 AM	Near Instance-Optimality in Differential Privacy John C Duchi, Stanford University; Hilal Asi, Stanford University
11:45 AM	Floor Discussion

230	Wed, 8/11/2021, 10:00 AM - 11:50 AM	Virtual
Biased Data, Biased Models? Bridging Advances in Survey Research and Computer Science for Improving Fairness in Algorithmic Decision-Making — Invited Panel
Survey Research Methods Section, Section on Statistical Learning and Data Science
Organizer(s): Ruben Bach, University of Mannheim; Christoph Kern, University of Mannheim
Chair(s): Frauke Kreuter, University of Maryland and University of Mannheim
10:05 AM	Biased Data, Biased Models? Bridging Advances in Survey Research and Computer Science for Improving Fairness in Algorithmic Decision-Making
Panelists:	Trent Buskirk, Bowling Green State University Rayid Ghani, Carnegie Mellon University Daniel Oberski, Utrecht University Omer Reingold, Stanford University
11:45 AM	Floor Discussion

245	Wed, 8/11/2021, 10:00 AM - 11:50 AM	Virtual
SLDS CSpeed 4 — Contributed Speed
Section on Statistical Learning and Data Science
Chair(s): Nathaniel O'Connell, Wake Forest School of Medicine
10:05 AM	A Generalization of the Lomax Distribution and Applications to the Cancer Patient Data Sher B Chhetri, University of South Carolina Sumter; Denzyl Lastimoso, University of South Carolina Sumter; Cory Ball, Oak Ridge National Lab, TN
10:10 AM	Statistical Convergence Rates for Knothe-Rosenblatt Coupling Estimators Nicholas Irons, University of Washington; Zaid Harchaoui, University of Washington; Soumik Pal, University of Washington; Meyer Scetbon, CREST, ENSAE
10:15 AM	Tree Boosting for Learning Probability Measures Naoki Awaya, Duke University; Li Ma, Duke University
10:20 AM	Stein’s Paradox for Sample Eigenvectors Alexander Shkolnik, University of California, Santa Barbara
10:25 AM	A model-agnostic hypothesis test for community structure and homophily in networks Eric Yanchenko, North Carolina State University; Srijan Sengupta, North Carolina State University
10:30 AM	Mutually Exciting Point Process Graphs for Modeling Dynamic Networks Francesco Sanna Passino, Imperial College London; Nick Heard, Imperial College London
10:35 AM	Latent Factor Model for Multivariate Functional Data Ruonan Li, North Carolina State University; Luo Xiao, Department of Statistics, North Carolina State University
10:40 AM	Parameter Estimation for Stochastic McKean-Vlasov Equations Louis Sharrock, Imperial College London; Nikolas Kantas, Imperial College London; Grigorios Pavliotis, Imperial College London; Panos Parpas, Imperial College London
10:45 AM	The Bethe Hessian and Information Theoretic Approaches for Online Change-Point Detection in Network Data Jiarui Xu, Oregon State University; Neil Hwang, City University of New York - Bronx Community College; Sharmodeep Bhattacharyya, Oregon State University; Dr. Shirshendu Chatterjee, City University of New York
10:50 AM	Spectral Goodness-of-Fit Tests for Complete and Partial Network Data Bolun Liu, Department of Statistics, University of Washington; Shane Lubold, Department of Statistics, University of Washington; Tyler McCormick , University of Washington
11:00 AM	Testing the constant curvature assumption of networks, with applications in efficient network seeding Shane Lubold, Department of Statistics, University of Washington
11:05 AM	Robust Persistence Diagrams Using Reproducing Kernels Siddharth Vishwanath, Penn State University; Kenji Fukumizu, Institute of Statistical Mathematics; Satoshi Kuriki, Institute of Statistical Mathematics; Bharath Sriperumbudur, Penn State University
11:10 AM	Robust Multiple Regression Zhipeng Wang, Rice University; David Warren Scott, Rice University
11:15 AM	A Random Forest Method with Variable Selection for Developing Prediction Models for Binary Outcomes with Clustered and Longitudinal Data Jaime Lynn Speiser, Wake Forest School of Medicine
11:20 AM	A Robust Birnbaum-Saunders Regression Model Based on Asymmetric Heavy-Tailed Distributions Rocío Maehara, Universidad del Pacífico; Filidor Vilca, Universidade Estadual de Campinas; Heleno Bolfarine, Universidade de Sao Paulo; Narayanaswamy Balakrishnan, McMaster University
11:25 AM	Robust Criterion for Multicollinear Factor Selection Kimon Ntotsis, University of the Aegean ; Alex Karagrigoriou, University of the Aegean ; Andreas Artemiou, Cardiff University
11:30 AM	An Exact Solution to the Univariate Behrens-Fisher Problem and Its Extension Jiajuan Liang, BNU-HKBU United International College; Guoliang Tian, Southern University of Science and Technology; Man-Lai Tang, The Hang Seng University of Hong Kong ; Jing Yang, Tianjin Medical University
11:35 AM	Distribution-Free, Risk-Controlling Prediction Sets Stephen Bates, UC Berkeley

255	Wed, 8/11/2021, 12:00 PM - 1:20 PM	Virtual
Section on Statistical Learning and Data Science P.M. Roundtable Discussion (Added Fee) — Roundtables PM Roundtable Discussion
Section on Statistical Learning and Data Science
WL09:	An Easy First Step in Causal Inference Harsh Parikh, Duke University
WL10:	Establishing Statisticians as Leaders in Data Science: Increasing Career Opportunities by Using Leadership Research Alessandro De Nadai, Texas State University

262 * !	Wed, 8/11/2021, 1:30 PM - 3:20 PM	Virtual
Emerging Statistical Theory and Methods in Deep Learning — Invited Papers
Section for Statistical Programmers and Analysts, Section on Statistical Learning and Data Science, Health Policy Statistics Section
Organizer(s): Ping Ma, University of Georgia
Chair(s): Ping Ma, University of Georgia
1:35 PM	Probabilistic Connection Importance Inference and Lossless Compression of Deep Neural Networks Xin (Shayne) Xing, Virginia Tech
2:05 PM	Causal Inference via Artificial Neural Networks: From Prediction to Causation Shujie Ma, University of California, Riverside; Xiaohong Chen, Yale University; Ying Liu, University of California, Riverside; Zheng Zhang, Renmin University of China
2:35 PM	Sufficient Dimension Reduction for Classification Using Principal Optimal Transport Direction Cheng Meng, Institute of Statistics and Big Data, Renmin University of China; Jun Yu, School of Mathematics and Statistics, Beijing Institute of Technology; Jingyi Zhang, Center for Statistical Science, Tsinghua University; Ping Ma, University of Georgia; Wenxuan Zhong, University of Georgia
3:15 PM	Floor Discussion

265 !	Wed, 8/11/2021, 1:30 PM - 3:20 PM	Virtual
Recent Advances in Statistical Network Analysis — Invited Papers
International Indian Statistical Association, IMS, Section on Statistical Learning and Data Science
Organizer(s): Sumit Mukherjee, Columbia University
Chair(s): Sumit Mukherjee, Columbia University
1:35 PM	Accounting for Network Noise: Counting, Experimenting, and Epidemic Control Eric Kolaczyk, Boston University
2:05 PM	Long-Range Dependence in Evolving Network Models Shankar Bhamidi, University of North Carolina
2:35 PM	Motif Estimation via Subgraph Sampling: The Fourth-Moment Phenomenon Bhaswar Bikram Bhattacharya, Department of Statistics, University of Pennsylvania; Sayan Das, Departments of Mathematics, Columbia University; Sumit Mukherjee, Columbia University
3:05 PM	Floor Discussion

266 * !	Wed, 8/11/2021, 1:30 PM - 3:20 PM	Virtual
Recent Advances in Statistical Network Analysis with Applications — Invited Papers
Section on Statistical Graphics, Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Ji Zhu, University of Michigan
Chair(s): Ji Zhu, University of Michigan
1:35 PM	On Scalable Estimation for Overlapping Clustering Models Purnamrita Sarkar, University of Texas, Austin; Deepayan Chakrabarti, University of Texas at Austin
2:00 PM	Mixed-Effect Time-Varying Network Model Emma Jingfei Zhang, University of Miami; Will Wei Sun, Purdue University; Lexin Li, University of California, Berkeley
2:25 PM	Linear Regression and Its Inference on Noisy Network-Linked Data Can M Le, University of California, Davis; Tianxi Li, University of Virginia
2:50 PM	Causal Inference for Contagious Processes on Networks Forrest W. Crawford, Yale University
3:15 PM	Floor Discussion

276	Wed, 8/11/2021, 1:30 PM - 3:20 PM	Virtual
Statistical Foundations of Reinforcement Learning — Topic-Contributed Papers
IMS, Section on Statistical Learning and Data Science, International Chinese Statistical Association
Organizer(s): Yuxin Chen, Princeton University
Chair(s): Yuxin Chen, Princeton University
1:35 PM	Breaking the sample size barrier in model-based reinforcement learning Yuting Wei, Carnegie Mellon University
1:55 PM	Distributional Robust Batch Contextual Bandits Zhengyuan Zhou, New York University
2:15 PM	Learning Good State and Action Representations via Tensor Decomposition Anru Zhang, University of Wisconsin-Madison; Chengzhuo Ni, Princeton University; Yaqi Duan, Princeton University; Mengdi Wang, Princeton University
2:35 PM	Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits Zhimei Ren, Stanford University; Zhengyuan Zhou, New York University
2:55 PM	Is Q-Learning Minimax Optimal? Yuejie Chi, Carnegie Mellon University
3:15 PM	Floor Discussion

280 *	Wed, 8/11/2021, 1:30 PM - 3:20 PM	Virtual
Application of Machine Learning in Clinical Development — Topic-Contributed Papers
Biopharmaceutical Section, Section on Statistical Learning and Data Science, International Chinese Statistical Association, Text Analysis Interest Group
Organizer(s): Dooti Roy, Boehringer Ingelheim Pharmaceuticals, Inc.; Nan Shao, Boehringer Ingelheim Pharmaceuticals, Inc.
Chair(s): Zheng Zhu, Boehringer Ingelheim Pharmaceuticals, Inc.
1:35 PM	Application of Digital Medicine in Drug Development Sandeep M Menon, Pfizer; Tim McCarthy, Pfizer; F. Isik Karahanoglu, Pfizer Global Research and Development
1:55 PM	Predicting Patient Adherence in a Changing World Dooti Roy, Boehringer Ingelheim Pharmaceuticals, Inc.
2:15 PM	Automatic Disease Screening of Borderline Personality Disorder Using Electronic Health Records (EHR) Nan Shao, Boehringer Ingelheim Pharmaceuticals, Inc.; Marianne Goodman, Icahn School of Medicine at Mount Sinai; James J Peters VA Medical Center; Chengxi Zang, Weill Cornell Medicine, Cornell University; Zheng Zhu, Boehringer Ingelheim Pharmaceuticals, Inc.; Zsuzsanna Tamas, Boehringer Ingelheim; Rachel Ovens, Boehringer Ingelheim; Agnes Koczon-Jaremko , Boehringer Ingelheim; Vikas_Mohan Sharma, Boehringer Ingelheim
2:35 PM	Application of Natural Language Processing in Drug Development Hua Xu, The University of Texas Health Science Center at Houston
2:55 PM	Floor Discussion

288	Wed, 8/11/2021, 1:30 PM - 3:20 PM	Virtual
SLDS CSpeed 5 — Contributed Speed
Section on Statistical Learning and Data Science
Chair(s): Jinming Li, University of Michigan
1:35 PM	Skeleton Clustering: Dimension-Free Density-Based Clustering Zeyu Wei, University of Washington, Department of Statistics; Yen-Chi Chen, University of Washington
1:40 PM	A New Algorithm for Convex Biclustering and Its Extension to the Compositional Data Presentation Binhuan Wang, NYU School of Medicine; Lanqiu Yao, New York University; Jiyuan Hu, New York University Grossman School of Medicine; Huilin Li, New York University Grossman School of Medicine
1:45 PM	Optimal Imperfect Classification for Functional Data Shuoyang Wang, Auburn university; Zuofeng Shang, New Jersey Institute of Technology; GUANQUN CAO, Auburn university
1:50 PM	A Penalized Model-Based Coclustering Algorithm Chenchen Ma, University of Michigan; Gongjun Xu, University of Michigan; Ji Zhu, University of Michigan
1:55 PM	Unsupervised Clustering of Aging Individuals Using Multi-Region Brain Transcriptomes Annie J Lee, Columbia University; Yiyi Ma, Columbia University; Lei Yu, Rush Alzheimer Disease Center, Rush University Medical Center; Robert J. Dawe, Rush Alzheimer Disease Center, Rush University Medical Center; Konstantinos Arfanakis, Rush Alzheimer Disease Center, Rush University Medical Center; Richard Mayeux, Columbia University; Bennett David, Rush Alzheimer Disease Center, Rush University Medical Center; Hans-Ulrich Klein, Columbia University; Philip L. De Jager, Columbia University
2:00 PM	Clustering and Directional Outlier Detection with Missing Information Hung Tong, San Jose State University; Cristina Tortora, San Jose State University
2:05 PM	How Many Clusters Are Best? Investigating Model Selection in Robust Clustering Presentation Louis Tran, San Jose State University; Cristina Tortora, San Jose State University
2:10 PM	Consistency of Privacy-Preserving Spectral Clustering of Block Models Jonathan Hehir, Penn State University; Aleksandra Slavkovic, Penn State University; Xiaoyue Niu, Pennsylvania State University
2:15 PM	Unsupervised Feature Decorrelation for Variable Selection Ana Maria Kenney, Pennsylvania State University; Francesca Chiaromonte, Penn State University
2:20 PM	PCAN: Principal Component Analysis for Networks Jihui Lee, Weill Medical College of Cornell University; James D. Wilson, University of San Francisco
2:30 PM	Longitudinal Cluster Analysis Using Segmented-LSTM with Applications of Weight Loss Trajectories Following RYGB Surgery Yirui Hu, Geisinger; Simo Wu, Facebook; Kunpeng Liu , University of Central Florida; Michelle R. Lent, Philadelphia College of Osteopathic Medicine; Peter N. Benotti, Geisinger ; Anthony T. Petrick, Geisinger; G. Craig Wood, Geisinger; Christopher D. Still, Geisinger; H. Lester Kirchner, Geisinger
2:35 PM	WITHDRAWN Assessing Classification Uncertainty on Astronomical Objects with Measurement Error Sarah Shy, Pennsylvania State University; Hyungsuk Tak, Pennsylvania State University; Eric Feigelson, Pennsylvania State University; John Timlin, Pennsylvania State University; Jogesh Babu, Pennsylvania State University
2:40 PM	Semi-Supervised Classification and Visualization of Multi-View Data Theodoulos Rodosthenous, Imperial College London; Vahid Shahrezaei, Imperial College London; Marina Evangelou, Imperial College London
2:45 PM	Comparing the Accuracy Classification of the Machine Learning Algorithms Using Anxiety Data Hojjatollah Farahani, Tarbiat Modares University; Parviz Azadfallah, Tarbiat Modares University; Peter Watson, University of Cambridge; Arezoo Esfandiary, Azad University of Karaj; Kazhal Rashidi, Azad University of Rudehen
2:50 PM	Spatial Autoregressive Mode Parameter Estimation and Inference Using Stochastic Gradient Descent and Bootstrap Perturbation Ji Meng Loh, New Jersey Institute of Technology ; Gan Luan, New Jersey Institute of Technology
2:55 PM	Precision Learner in Classification: A Subject-Based Approach for Classification Using Item Response Theory for Ensemble Machine Learning Di Xiong, University of California, Los Angeles; Honghu Liu, University of California, Los Angeles
3:00 PM	Classification for Imbalanced Data Renxiong Liu, Ohio State University; Yunzhang Zhu, Ohio State University
3:05 PM	WITHDRAWN: A Virtual Multi-Label Approach to Imbalanced Data Classification Elizabeth Chou, Dept. of Statistics, National Chengchi University
3:10 PM	Classification Accuracy Evaluation for Five Machine-Learning Classification Methods in Identifying Rare Cases in Education Assessment Chi Chang, Michigan State University; Harlan McCaffery, University of Michigan
3:15 PM	A Novel Application of Finite Gaussian Mixture Model (GMM) Using Real and Simulated Biomarkers of Cardiovascular Disease to Distinguish Adolescents with and Without Obesity Jobayer Hossain, Nemours Children's Health System; Babu Balagopal, Nemours Children's Health System

292 !	Wed, 8/11/2021, 3:30 PM - 5:20 PM	Virtual
Inferential Thinking in a Machine Learning World — Invited Papers
Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, Section on Statistical Computing
Organizer(s): Lucas Mentch, University of Pittsburgh
Chair(s): Lucas Mentch, University of Pittsburgh
3:35 PM	Why Random Forests Work and Why That’s a Problem Siyu Zhou, University of Pittsburgh; Lucas Mentch, University of Pittsburgh
4:00 PM	A Semiparametric Approach to Variable Importance with Multiple Testing Corrections Aaditya K Ramdas, Carnegie Mellon University
4:25 PM	Recent Advances in Applying Floodgate to High-Dimensional Inference Presentation Lucas Janson, Harvard University; Lu Zhang, Harvard University
4:50 PM	Variable Importance, Cohort Shapley Value, and Redlining Art Owen, Stanford University; Masayoshi Mase, Hitachi, Ltd; Ben Seiler, Stanford University
5:15 PM	Floor Discussion

312 * !	Wed, 8/11/2021, 3:30 PM - 5:20 PM	Virtual
Macroeconomic Forecasting in Theory and Practice — Topic-Contributed Papers
Business and Economic Statistics Section, Government Statistics Section, Section on Statistical Learning and Data Science
Organizer(s): Andrew B Martinez, US Department of the Treasury
Chair(s): Jennifer Castle, Magdalen College
3:35 PM	Selecting a Model for Forecasting Jennifer Castle, Magdalen College; Jurgen A Doornik, Climate Econometrics, University of Oxford; David F. Hendry, University of Oxford
3:55 PM	Smooth Robust Multi-Horizon Forecasts Andrew B Martinez, US Department of the Treasury; Jennifer Castle, Magdalen College; David F. Hendry, University of Oxford
4:15 PM	Forecasting US Inflation in Real Time Kirstin Hubrich, Federal Reserve Board; Chad Fulton, Federal Reserve Board
4:35 PM	Forecasting FOMC Forecasts Presentation Jaime Marquez, Johns Hopkins University; S. Yanki Kalfa, Rady School of Managment University of California @ San Diego
4:55 PM	Evaluating the Federal Reserve’s Tealbook Forecasts Neil R Ericsson, Federal Reserve Board
5:15 PM	Floor Discussion

313 * !	Wed, 8/11/2021, 3:30 PM - 5:20 PM	Virtual
Recent Advances in Symbolic Data Analysis — Topic-Contributed Papers
Section on Statistical Learning and Data Science, Section on Statistical Computing, IMS
Organizer(s): S. Yaser Samadi, Southern Illinois University Carbondale
Chair(s): Jenifer Le-Rademacher , Department of Health Sciences Research, Mayo Clinic
3:35 PM	Partitioning Interval-Valued Data Using Regression Lynne Billard, University of Georgia; Fei Liu, Bank of America
3:55 PM	Facial Recognition Development Using Principal Component Analysis for Interval-Valued Face Data Set Anuradha Roy, The University of Texas at San Antonio
4:15 PM	Symbolic Interval-Valued Time Series: Theory and Applications S. Yaser Samadi, Southern Illinois University Carbondale; Lynne Billard, University of Georgia
4:35 PM	It’s Natural to Think About Cluster Randomized Trials (CRTs) Within the Symbolic Data Framework: The Symbolic Two-Step Method in the Design and Analysis of CRTs David Zahrieh, Mayo Clinic
4:55 PM	Floor Discussion

314 * !	Wed, 8/11/2021, 3:30 PM - 5:20 PM	Virtual
Statistical Challenges in Cosmology — Topic-Contributed Papers
Section on Nonparametric Statistics, Astrostatistics Special Interest Group, Section on Statistical Learning and Data Science, Section on Physical and Engineering Sciences
Organizer(s): Collin A Politsch, Carnegie Mellon University
Chair(s): Jessi J. Cisewski-Kehe, University of Wisconsin-Madison
3:35 PM	Three-Dimensional Cosmography of the High Redshift Universe Using Intergalactic Absorption Collin A Politsch, Carnegie Mellon University; Jessi J. Cisewski-Kehe, University of Wisconsin-Madison; Rupert Croft, Carnegie Mellon University; Larry Wasserman, Carnegie Mellon University
3:55 PM	Stratified Learning: a general-purpose statistical method for improved learning under Covariate Shift Maximilian Autenrieth, Imperial College London; David A van Dyk, Imperial College London; Roberto Trotta, Imperial College London, International School for Advanced Studies (Trieste); David C Stenning, Simon Fraser University
4:15 PM	Cosmological Parameter Inference in Photometric Surveys: An Inverse Problem Markus Michael Rau, Carnegie-Mellon-University
4:35 PM	Learning to Predict Structure Formation in the Universe Presentation Yin Li, Flatiron Institute
4:55 PM	Discussant: Chad Schafer, Carnegie Mellon University
5:15 PM	Floor Discussion

319	Wed, 8/11/2021, 3:30 PM - 5:20 PM	Virtual
SLDS CSpeed 6 — Contributed Speed
Section on Statistical Learning and Data Science, Text Analysis Interest Group
Chair(s): Weijing Tang, University of Michigan
3:35 PM	Estimation of the Mean Function of Functional Data via Deep Neural Networks GUANQUN CAO, Auburn university; Shuoyang Wang, Auburn university; Zuofeng Shang, New Jersey Institute of Technology
3:40 PM	Nonlinear Functional Modeling Using Neural Networks Aniruddha Rajendra Rao, Pennsylvania State University; Matthew Reimherr, Penn State University
3:45 PM	Hyperparameter Optimization of Deep Neural Networks with Applications to Medical Device Manufacturing Gautham Sunder, Carlson School of Management; Christopher Nachtsheim, Carlson School of Management; Thomas Albrecht, Boston Scientific
3:50 PM	Deep Upper Confidence Bound Algorithm for Contextual Bandit Ranking of Information Selection Michael Rawson, Department of Mathematics, University of Maryland at College Park; Jade Freeman, CCDC Army Research Laboratory
3:55 PM	Exploring Neural Networks' Ability to Generate Music NOAH Daniel SOLOMON, Bridgewater State University; Wanchunzi Yu, Bridgewater State University
4:00 PM	Efficient Path Following Algorithms and Its Applications to Case Influence Assessment Qiuyu Gu, The Ohio State University; Renxiong Liu, Ohio State University; Yunzhang Zhu, Ohio State University
4:05 PM	TensorFlow Versus H2O, Round 2: Predicting Currency Prices Kenneth Davis, Statistical Significance
4:10 PM	Low-Rank Matrix/Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-order Convergence Wen Huang, Xiamen University; Xudong Li, Fudan University; Anru Zhang, University of Wisconsin-Madison; Yuetian Luo, University of Wisconsin-Madison
4:15 PM	Using Machine Learning Techniques to Model Factors That Influence the Intent of a Person to Take a Coronavirus Test Sheila Rutto, The University of Texas Rio Grande Valley
4:20 PM	On the Algorithmic Stability of Adversarial Training Yue Xing, Purdue University; Qifan Song, Purdue University; Guang Cheng, Purdue University
4:30 PM	A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks Presentation Zhiqi Bu, University of Pennsylvania; Shiyun Xu, University of Pennsylvania; Kan Chen, University of Pennsylvania
4:35 PM	WITHDRAWN: Fair Influence Maximization on Social Networks with Community Structure Octavio César Mesner, University of Michigan; Ji Zhu, University of Michigan; Liza Levina, University of Michigan
4:40 PM	Controlled Group Variable Selection Using Variational Autoencoder-Generated Knockoffs and Reproducibility Evaluation Xinran Qi, Medical College of Wisconsin
4:45 PM	Stability of Text Analytics and Topic Analysis: A Deeper Look at Popular Methods Presentation Mary Milam Whiteside, The University of Texas at Arlington; Mark E Eakin, The University of Texas at Arlington
4:50 PM	Testing Hypotheses in Agent-Based Models Georgiy Bobashev, RTI International; Hang Xiong, Huazhong Agricultural University, China
4:55 PM	WITHDRAWN: Stacked Models and the Explainability Tradeoff in Recommender Systems shaudi mahdavi hosseini, m.i.t.
5:00 PM	An Eigenmodel for Dynamic Multilayer Networks Joshua Daniel Loyal, University of Illinois at Urbana-Champaign; Yuguo Chen, University of Illinois at Urbana-Champaign

332 *	Thu, 8/12/2021, 10:00 AM - 11:50 AM	Virtual
Synthetic Clinical Trials Design to Accelerate FDA Approvals — Invited Papers
Biopharmaceutical Section, Section on Statistics in Epidemiology, Section on Statistical Learning and Data Science
Organizer(s): Choudur Lakshminarayan, Teradata Labs/The University of Texas at Austin
Chair(s): Peter Mueller, University of Texas Austin
10:05 AM	Machine Learning-Enabled Real-World Evidence in Synthetic Arms Trials Prater Edmund, The University of Texas at Arlington; Kay-Yut Chen, The University of Texas at Arlington; Sridhar Nerur, The University of Texas at Arlington
10:30 AM	Incorporating External Data into the Analysis of Clinical Trials via Bayesian Additive Regression Trees Tianjian Zhou, Colorado State University; Yuan Ji, The University of Chicago
10:55 AM	A Novel Bayesian Nonparametric Method to Use Real-World Data in Clinical Trials Presentation Noirrit Kiran Chandra, University of Texas at Austin; Peter Mueller, University of Texas Austin
11:20 AM	Discussant: Choudur Lakshminarayan, Teradata Labs/The University of Texas at Austin
11:40 AM	Floor Discussion

333 * !	Thu, 8/12/2021, 10:00 AM - 11:50 AM	Virtual
Recent Developments in Network Inference Methods — Invited Papers
IMS, International Indian Statistical Association, Section on Statistical Learning and Data Science
Organizer(s): Dr. Shirshendu Chatterjee, City University of New York
Chair(s): Dr. Shirshendu Chatterjee, City University of New York
10:05 AM	Bootstrap for Networks: From Parametric to Nonparametric Approaches Liza Levina, University of Michigan
10:30 AM	Identifying Heterogeneous Temporal Structure from Multiple Network Time Series Carey E Priebe, Johns Hopkins University; Guodong Chen, Johns Hopkins University; Jonathan Larson, Microsoft Research; Weiwei Yang, Mocrosoft Research; Christopher White, Microsoft Research; Joshua Vogelstein, Johns Hopkins University; Youngser Park, Johns Hopkins University
10:55 AM	Statistical Inference for Networks with Dependent Edges Sharmodeep Bhattacharyya, Oregon State University; Dr. Shirshendu Chatterjee, City University of New York; Soumendu Sundar Mukherjee, Indian Statistical Institute
11:20 AM	Hierarchical stochastic block model for multiplex networks Arash A. Amini, UCLA
11:45 AM	Floor Discussion

347 !	Thu, 8/12/2021, 10:00 AM - 11:50 AM	Virtual
Recent Advances in Clustering and Mixture Models Analysis — Topic-Contributed Papers
Section for Statistical Programmers and Analysts, IMS, Section on Statistical Learning and Data Science, International Chinese Statistical Association
Organizer(s): Anderson Ye Zhang, University of Pennsylvania
Chair(s): Anderson Ye Zhang, University of Pennsylvania
10:05 AM	Structures of Local Minima in K-Means and the Likelihood of Mixture Models Yudong Chen, School of ORIE, Cornell University; Xumei Xi, School of ORIE, Cornell University
10:25 AM	Causal Inference for Randomized Experiments in Social Networks David Choi, Carnegie Mellon University
10:45 AM	Learning Mixtures of Permutations: Groups of Pairwise Comparisons and Combinatorial Method of Moments Presentation Cheng Mao, Georgia Institute of Technology; Yihong Wu, Yale
11:05 AM	Efficient Clustering for Stretched Mixtures: Landscape and Optimality Kaizheng Wang, Columbia University; Yuling Yan, Princeton University; Mateo Diaz, Cornell University
11:25 AM	Sparse Topic Modeling: Computational Efficiency and Near-Optimal Algorithms Ruijia Wu, Department of Statistics, University of Pennsylvania; Linjun Zhang, Rutgers University; Tony Cai, University of Pennsylvania
11:45 AM	Floor Discussion

351 * !	Thu, 8/12/2021, 10:00 AM - 11:50 AM	Virtual
Statistical Modeling in Pre-Clinical Drug Proarrhythmic Assessment — Topic-Contributed Panel
Biopharmaceutical Section, Section on Statistical Learning and Data Science, Biometrics Section
Organizer(s): Dalong Huang, FDA
Chair(s): Dalong Huang, FDA
10:05 AM	Statistical Modeling in Pre-Clinical Drug Proarrhythmic Assessment
Panelists:	Yu-yi Hsu, FDA/CDER Nan Xi, UCLA
11:40 AM	Floor Discussion

362 * !	Thu, 8/12/2021, 12:00 PM - 1:50 PM	Virtual
What Can Statistical Graphics Speak to Us About Deep Learning and Complex Models? — Invited Papers
Section on Statistical Graphics, Section on Statistical Computing, Section on Statistical Learning and Data Science
Organizer(s): Dianne Cook, Monash University
Chair(s): Denise Bradford, University of Nebraska- Lincoln
12:05 PM	An Introduction to Deep Learning for Computer Vision Kevin R Moon, Utah State University
12:30 PM	How Do You Define a Circle? Perception and Computer Vision Diagnostics Susan VanderPlas, University of Nebraska - Lincoln
12:55 PM	Casting Multiple Shadows: High-Dimensional Interactive Data Visualization with Tours and Embeddings Stuart Lee, Monash University
1:20 PM	Discussant: Dianne Cook, Monash University
1:40 PM	Floor Discussion

389 * !	Thu, 8/12/2021, 2:00 PM - 3:50 PM	Virtual
Words and Insights via Text Analysis — Invited Papers
Text Analysis Interest Group, Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Kelly H. Zou, Viatris
Chair(s): Kelly H. Zou, Viatris
2:05 PM	Topic-Adjusted Visibility Metric for Scientific Articles Tian Zheng, Columbia University; Linda S. L. Tan, National University of Singapore
2:30 PM	Measuring the Impact of Behavior Change Interventions Using Free-Text Michael Baiocchi, Stanford University; Jordan Rodu, University of Virginia
2:55 PM	Transfer Learning for Latent Dirichlet Allocation Presentation Tommy W Jones, In-Q-Tel
3:20 PM	Text Analysis for Topic Discovery with Applications for Cross-Reference, Summarization, and Optimized Recommended Reading List Michael Henderson; Jesse Behrens, SAS
3:45 PM	Floor Discussion

392 !	Thu, 8/12/2021, 2:00 PM - 3:50 PM	Virtual
Recent Advances in Tensor Learning — Invited Papers
IMS, International Chinese Statistical Association, Section on Statistical Learning and Data Science
Organizer(s): Will Wei Sun, Purdue University
Chair(s): Will Wei Sun, Purdue University
2:05 PM	Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness Presentation Xuan Bi, University of Minnesota; Gediminas Adomavicius, University of Minnesota; William Li, Shanghai Jiao Tong University; Annie Qu, Unviersity of California Irvine
2:30 PM	Community Detection on Mixture Multi-Layer Networks via Regularized Tensor Decomposition Dong XIA, Hong Kong University of Science and Technology
2:55 PM	Frequentist Predictions with Incorrect Tensor Models Peter Hoff, Duke University
3:20 PM	Dynamic Tensor Factor Model Based on CP Decomposition Yuefeng Han, Rutgers University; Rong Chen, Rutgers University; Cun-Hui Zhang, Rutgers University
3:45 PM	Floor Discussion

400 !	Thu, 8/12/2021, 2:00 PM - 3:50 PM	Virtual
Breiman Award Lectures — Invited Papers
Section on Statistical Learning and Data Science, IMS, International Chinese Statistical Association
Organizer(s): Hao Helen Zhang, University of Arizona
Chair(s): Jelena Bradic, University of California, San Diego
2:05 PM	Hypothesis Testing After Hypothesis Generation Daniela Witten, University of Washington; Jacob Bien, University of Southern California; Lucy Gao, University of Waterloo; Anna Neufeld, University of Washington
2:35 PM	Some Comments on CV Trevor JOHN Hastie, STANFORD UNIVERSITY; Stephen Bates, UC Berkeley; Robert Tibshirani, Stanford Univ
3:45 PM	Floor Discussion

408 * !	Thu, 8/12/2021, 2:00 PM - 3:50 PM	Virtual
Modeling Visitation of Public Lands and Analyzing Visitors’ Sentiments Using Social Media — Topic-Contributed Papers
Section on Statistics and the Environment, WNAR, Section on Statistical Learning and Data Science
Organizer(s): Kimihiro Noguchi, Department of Mathematics, Western Washington University
Chair(s): Ramadha Piyadi Gamage, Western Washington University - Mathematics
2:05 PM	Modeling Recreation on Public Lands Using Social Media and Crowd-Sourced Data Samantha Winder, University of Washington; Emmi Lia, University of Washington; Lesley Miller, University of Washington; Spencer Wood, eScience Institute, University of Washington
2:25 PM	Modeling and Forecasting Percent Changes in National Park Visitation Using Social Media Russell J Goebel, Department of Mathematics & Statistics, Boston University
2:45 PM	Natural Environments and Sentiment Expressed on Twitter Yian Lin, University of Washington; Joshua Lawler, University of Washington
3:05 PM	Discussant: Spencer Wood, eScience Institute, University of Washington
3:25 PM	Discussant: Kimihiro Noguchi, Department of Mathematics, Western Washington University
3:45 PM	Floor Discussion

416	Thu, 8/12/2021, 2:00 PM - 3:50 PM	Virtual
SLDS CSpeed 7 — Contributed Speed
Section on Statistical Learning and Data Science
Chair(s): Danielle C. Tucker, University of Illinois at Chicago
2:05 PM	Efficient Designs of SLOPE Penalty Sequences in Finite Dimension Yiliang Zhang, University of Pennsylvania; Zhiqi Bu, University of Pennsylvania
2:10 PM	DebiNet: Debiasing Linear Models with Nonlinear Overparameterized Neural Networks Presentation Shiyun Xu, University of Pennsylvania; Zhiqi Bu, University of Pennsylvania
2:15 PM	Testing Joint Independence in High Dimensions Faith Zhang, University of Massachusetts Amherst; Maryclare Griffin, University of Massachusetts Amherst
2:20 PM	Doubly Robust Feature Selection with Mean and Variance Outlier Detection and Oracle Properties Presentation Luca Insolia, Sant’Anna School of Advanced Studies; Runze Li, Pennsylvania State University; Francesca Chiaromonte, Penn State University; Marco Riani, University of Parma
2:25 PM	Distribution-Free Bootstrap Prediction Intervals After Variable Selection Hasthika Rupasinghe, Appalachian State University; Lasanthi Watagoda, Appalachian State University
2:30 PM	Comparing Six Shrinkage Estimators with Large Sample Theory and Asymptotically Optimal Prediction Intervals Lasanthi Watagoda, Appalachian State University; David Olive, Southern Illinois University
2:35 PM	Inference Post-Selection of Group-Sparse Regression Models Daniel Allan Kessler, University of Michigan; Peter W. MacDonald, University of Michigan; Snigdha Panigrahi, University of Michigan
2:40 PM	Group Selection and Shrinkage with Application to Sparse Semiparametric Modeling Ryan Thompson, Monash University; Farshid Vahid, Monash University
2:45 PM	EAS Methodology for Grouped Variable Selection in Multivariate Linear Model Salil Koner, North Carolina State University; Jonathan P Williams, North Carolina State University
2:50 PM	Automatically Extracting Differential Equations from Data with Sparse Regression Techniques Presentation Kevin Egan, Durham University; Rui Carvalho, Durham University
3:00 PM	Canonical Correlation Analysis in High Dimensions with Structured Regularization Elena Tuzhilina, Stanford University; Leonardo Tozzi, Stanford University; Trevor JOHN Hastie, STANFORD UNIVERSITY
3:05 PM	Covariate-Assisted Sparse Tensor Completion and Inference Hilda Somnooma Ibriga, Purdue University; Will Wei Sun, Purdue University
3:10 PM	Computationally Sufficient Reductions for Some Sparse Multi-Way and Matrix-Variate Estimators Prateek Sasan, The Ohio State University; Akshay Prasadan, Carnegie Mellon University; Vincent Q Vu, The Ohio State University
3:15 PM	High-Dimensional Factor Analysis for Network-Linked Data Jinming Li, University of Michigan; Gongjun Xu, University of Michigan; Ji Zhu, University of Michigan
3:20 PM	Sparse Envelope Quantile Regression Presentation Lawrence Segbehoe, South Dakota State University; Gemechis Djira, South Dakota State Unversity; Hossein Moradi Rekabdarkolaee, South Dakota State University
3:25 PM	An Investigation of the False Discovery Rate Under Weak Dependency Andrew Bartlett, Southern Connecticut State University
3:30 PM	A Univariate Approach to High-Dimensional Linear Regression via a Quasi-EM Algorithm Alexander McLain, University of South Carolina; Anja Zgodic, University of South Carolina; Joshua Habiger, Oklahoma State University
3:35 PM	A Sampling-Based Principal Component Analysis Procedure for Interpretable Representations of a Network Sample James D. Wilson, University of San Francisco; Jihui Lee, Weill Medical College of Cornell University
3:40 PM	Comparison of Change Point Detection Methods for Independent Data: Testing & Estimation Casey Christiansen, Western Washington University

422 !	Thu, 8/12/2021, 4:00 PM - 5:50 PM	Virtual
Recent Development of Machine Learning Methods in Causal Inference — Invited Papers
Section on Statistical Learning and Data Science, ENAR, American Association for the Advancement of Science
Organizer(s): Rui Song, North Carolina State University
Chair(s): Hongtu Zhu, University of North Carolina
4:05 PM	Nonparametric Inverse Probability Weighted Estimators Based on the Highly Adaptive Lasso Ashkan Ertefaie, University of Rochester; Nima Hejazi, University of California, Berkeley; Mark Van Der Laan, University of California
4:25 PM	Calibrated Optimal Decision-Making with Multiple Data Sources and Limited Outcome Hengrui Cai, North Carolina State University
4:45 PM	Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis Max Goplerud, University of Pittsburgh; Kosuke Imai, Harvard University; Nicole E Pashley, Rutgers University
5:05 PM	Almost Matching Exactly Cynthia Rudin, Duke University; Alexander Volfovsky, Duke University; Sudeepa Roy, Duke University
5:25 PM	Discussant: Jelena Bradic, University of California, San Diego
5:45 PM	Floor Discussion

425 * !	Thu, 8/12/2021, 4:00 PM - 5:50 PM	Virtual
Modern Statistical Learning of Complex Data — Invited Papers
Section on Nonparametric Statistics, IMS, Section on Statistical Learning and Data Science
Organizer(s): Lily Wang, Iowa State University
Chair(s): Lily Wang, Iowa State University
4:05 PM	Sparse Modeling of Functional Linear Regression via Fused Lasso with Application to Genotype-by-Environment Interaction Studies Shan Yu, University of Virginia; Lily Wang, Iowa State University; Dan Nettleton, Iowa State University; Aaron Kusmec, Iowa State University
4:25 PM	Group Testing with Missing Values Aurore Delaigle, University of Melbourne; Ruoxu Tan, University of Melbourne
4:45 PM	Kernel-Based Learning for Informative Selection in Complex Surveys Jay Breidt, Colorado State University; Teng Liu, Colorado State University
5:05 PM	Robust Estimation of Additive Boundaries with Quantile Regression and Shape Constraints Lan Xue, Oregon State University; Yan Fang, Shanghai University of International Business and Economics; Carlos Martins-Filho, University of Colorado; Lijian Yang, Tsinghua University
5:25 PM	On Function-On-Scalar Quantile Regression Yusha Liu , University of Chicago; Meng Li, Rice University; Jeffrey S. Morris, University of Pennsylvania Perelman School of Medicine
5:45 PM	Floor Discussion

427 * !	Thu, 8/12/2021, 4:00 PM - 5:50 PM	Virtual
Toward Objective, Reproducible, and Scalable Digital Phenotyping Using Smartphones — Invited Papers
Section on Statistical Computing, Section on Statistical Learning and Data Science, Section on Nonparametric Statistics
Organizer(s): Marcin Straczkiewicz, Harvard T.H. Chan School of Public Health
Chair(s): Jukka-Pekka Onnela, Harvard University
4:05 PM	Methods for Digital Phenotyping: Handling the Challenges of Smartphone Data for Health Care Research Anna Beukenhorst, Harvard T.H. School of Public Health
4:30 PM	Application of Advanced Signal Processing Methods for Estimation of Walking Periods Using Smartphones Marcin Straczkiewicz, Harvard T.H. Chan School of Public Health
4:55 PM	Combining Accelerometer and Gyroscope Data in Smartphone-Based Activity Recognition using Movelets Emily Huang, Wake Forest University
5:20 PM	Digital Phenotyping for Post-Operative Surgical Recovery Assessment Patrick Emedom-Nnamdi, Harvard T.H. School of Public Health
5:45 PM	Floor Discussion

430 * !	Thu, 8/12/2021, 4:00 PM - 5:50 PM	Virtual
Infusing Data Ethics into the Development of Data Users — Invited Panel
Section on Statistics and Data Science Education, Section on Statistical Learning and Data Science, Committee on Professional Ethics
Organizer(s): Tyler Kloefkorn, National Academies of Sciences, Engineering, and Medicine
Chair(s): Nicholas Horton, Amherst College
4:05 PM	Infusing Data Ethics into the Development of Data Users
Panelists:	Susan Winter, University of Maryland Michael Kalichman, University of California San Diego Ben Baumer, Smith College Anne Washington, New York University Steinhardt School Natalie Evans Harris, Harris Data Consulting
5:45 PM	Floor Discussion

434	Thu, 8/12/2021, 4:00 PM - 5:50 PM	Virtual
Recent Advances in Unlinked and Permuted Regression — Topic-Contributed Papers
IMS, Government Statistics Section, Section on Statistical Learning and Data Science
Organizer(s): Charles Raouf Doss, University of Minnesota
Chair(s): Guangwei Weng, University of Minnesota
4:05 PM	Multivariate Regression with Unknown Permutation Martin Slawski, George Mason University; Bodhisattva Sen, Columbia University
4:25 PM	Isotonic Regression with Unknown Permutations: Statistics, Computation, and Adaptation Ashwin Pananjady, Georgia Tech; Richard J. Samworth, University of Cambridge
4:45 PM	Optimal Permutation Recovery in Permuted Monotone Matrix Model Rong Ma, University of Pennsylvania; Tony Cai, University of Pennsylvania; Hongzhe Li, University of Pennsylvania
5:05 PM	Discussant: Charles Raouf Doss, University of Minnesota
5:25 PM	Floor Discussion

436 * !	Thu, 8/12/2021, 4:00 PM - 5:50 PM	Virtual
Network Inference for Omics and Imaging Data — Topic-Contributed Papers
Section on Statistical Learning and Data Science, WNAR, Biometrics Section
Organizer(s): Li-Xuan Qin, Memorial Sloan Kettering Cancer Center
Chair(s): Jianhua Hu, Columbia University
4:05 PM	Modern Machine Learning Techniques in Biology Johannes Lederer, Ruhr-University Bochum
4:25 PM	Generalized Tensor Canonical Correlation Analysis for Network Inference Using Multi-Omics Data Katerina Kechris, University of Colorado Anschutz Medical Campus; Weixuan Liu, University of Colorado Anschutz Medical Campus; Farnoush Banaei-Kashani, University of Colorado Denver
4:45 PM	Prediction with Network Valued Covariates Suprateek Kundu, Emory University
5:05 PM	Principal Regression for High-Dimensional Covariance Matrices Yi Zhao, Indiana University; Brian Caffo, Johns Hopkins University; Xi Luo, The University of Texas Health Science Center at Houston
5:25 PM	Data Harmonization Using Polycistronic Clusters for MicroRNA Sequencing Data Li-Xuan Qin, Memorial Sloan Kettering Cancer Center; Yannick Düren, Ruhr-University Bochum; Johannes Lederer, Ruhr-University Bochum
5:45 PM	Floor Discussion

438 !	Thu, 8/12/2021, 4:00 PM - 5:50 PM	Virtual
Modern Statistical Learning Methods for High-Dimensional Biomedical Data: Treatment Heterogeneity and Data Integration — Topic-Contributed Papers
Biometrics Section, Section on Statistical Learning and Data Science, International Chinese Statistical Association
Organizer(s): Lu Xia, University of Washington, Seattle
Chair(s): Lu Xia, University of Washington, Seattle
4:05 PM	Cancer Prediction by Detecting and Integrating Connectomic Networks and Marginally Weak Signals Yanming Li, University of Kansas Medical Center; Fengwei Yang, University of Kansas Medical Center; Xueping Zhou, University of Pittsburgh; Devin C Koestler, University of Kansas Medical Center; Wei Chen, University of Pittsburgh
4:25 PM	Sharp Inference on Selected Subgroups in Observational Studies Xinzhou Guo, Harvard University; Waverly Wei, University of California, Berkeley; Chong Wu, Florida State University; Jingshen Wang, UC Berkeley
4:45 PM	Efficient Debiased Estimation of Heterogeneous Treatment Effect in Observational Studies Waverly Wei, University of California, Berkeley; Chong Wu, Florida State University; Jingshen Wang, UC Berkeley
5:05 PM	Floor Discussion

440	Thu, 8/12/2021, 4:00 PM - 5:50 PM	Virtual
SLDS CSpeed 8 — Contributed Speed
Section on Statistical Learning and Data Science
Chair(s): Adi Andrei, Northwestern University
4:05 PM	Multiclass Regularized Regression Integrating Prior Information Jingxuan He, University of Southern California; Chubing Zeng, University of Southern California; Juan Pablo Lewinger, University of Southern California; David Conti, University of Southern California
4:10 PM	Structural Breaks and Time Series Data: Comparison and Real Data Analysis Arick Grootveld, Western Washington University
4:15 PM	Performance of GANs and VAEs in Image Generation and Its Implication for Medical Applications: A Simulation Study Dawei Liu, Biogen
4:20 PM	Regularization for Shuffled Data Problem via Exponential Family Prior on the Permutation Group Zhenbang Wang, George Mason University; Emanuel Ben-David, US Census Bureau; Martin Slawski, George Mason University
4:25 PM	Penalized Intrinsic Quadratic Spline on the Sphere Jae-Kyung Shin, Department of Statistics, Korea University, Seoul 02841, Korea; Kwan-Young Bak, Department of Statistics, Korea University, Seoul 02841, Korea; Ja-Yong Koo, Department of Statistics, Korea University, Seoul 02841, Korea
4:30 PM	Change-Point Detection in Time Series of Weighted Stochastic Block Model Graphs Carolyn Liu, Ward Melville High School; Heng Wang, AppLovin; Youngser Park, Johns Hopkins University; Carey E Priebe, Johns Hopkins University
4:35 PM	Optimal Financial Portfolio Using Graphical Lasso Under Unstable Environment Ekaterina Seregina, University of California, Riverside; Tae-Hwy Lee, University of California, Riverside
4:40 PM	A Modified Bayesian Information Criterion for Improving the Performance of Tree-Based Learning Algorithms Without the Use of Cross-Validation Nikola Surjanovic, Simon Fraser University; Andrew Henrey, Finning; Thomas Loughin, Simon Fraser University
4:45 PM	Novel Entropy-Based Criterion in the Selection of Clusters of a Biological Network Structure Gul Bahar Bulbul, BGSU
4:50 PM	Estimating Uncertainty of Machine Learning Predictions Using Bayesian Additive Regression Trees Jeong Hwan Kook, Merck & Co., Inc.; Andy Liaw, Merck & Co., Inc.; Yuting Xu, Merck & Co., Inc.; Himel Mallick, Merck Research Laboratories; Vladimir Svetnik, Merck & Co.
5:00 PM	Learning Bayesian Networks Through Birkhoff Polytop Aramayis Dallakyan, Texas A&M University; Mohsen Pourahmadi, Texas A&M University
5:05 PM	Quasi-Monte Carlo, Quasi-Newton for Variational Bayes Sifan A. Liu, Stanford University; Art Owen, Stanford University
5:10 PM	GAMVT: A Generative Algorithm for MultiVariate Timeseries Data Jamie Thorpe, Sandia National Laboratories; Srideep Musuvathy, Sandia National Laboratories; Stephen Verzi, Sandia National Laboratories; Eric Vugrin, Sandia National Laboratories; Matthew Dykstra, Sandia National Laboratories; Meghan Sahakian, Sandia National Laboratories
5:15 PM	A Generative Approach to Conditional Sampling Xingyu Zhou, Department of statistics and actuarial Science, The university of Iowa; Jian Huang, Department of statistics and actuarial science, The university of Iowa; Yuling Jiao, School of Mathematics and Statistics, Wuhan University; Jin Liu, Duke-NUS Medical School, Health Service & Systems Research
5:20 PM	Posterior Sampling Algorithms for Sequential Decision-Making Based on Partially Observed Data Hongju Park, University of Georgia; Mohamad Kazem Shirani Faradonbeh, University of Georgia
5:25 PM	Statistics in 30 Minutes Terrie Vasilopoulos, University of Florida, College of Medicine; Cynthia Garvan, University of Florida, College of Medicine
5:30 PM	Statistical Issues in Principal Component Score Estimation for Exponential Family PCA Ruochen Huang, Ohio State University; Yoonkyung Lee, Ohio State University
5:35 PM	Aggregated Functional Data Model Applied on Clustering and Disaggregation of Electrical Load Profiles Camila P. E. de Souza, The University of Western Ontario; Gabriel Franco de Souza, University of Campinas; Nancy L. Garcia, University of Campinas
5:40 PM	Distributed Learning of Finite Gaussian Mixtures Qiong Zhang, University of British Columbia
5:45 PM	Adaptive Smoothing Dimension Reduction Methods for Neural Firing Rate Data Angel Garcia de la Garza, Columbia University ; Britton Sauerbrei, Janelia Research Campus HHMI; Jeff Goldsmith, Columbia University, Department of Biostatistics

JSM 2021 Online Program

Activity Details

American Statistical Association