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Legend:
CC = Walter E. Washington Convention Center   M = Marriott Marquis Washington, DC
* = applied session       ! = JSM meeting theme

Activity Details


CE_08C
Sun, 8/7/2022, 8:30 AM - 5:00 PM CC-146B
Machine Learning and Deep Learning — Professional Development Continuing Education Course
ASA, Section on Statistical Learning and Data Science
Instructor(s): Annie Qu, UC Irvine; Xiao Wang, Purdue University; Edgar Dobriban, University of Pennsylvania
This short course is for those who are new to data science and interested in understanding the cutting-edge machine learning and deep learning models. It is for those who want to become familiar with the core concepts behind these learning algorithms and their successful applications. It is for those who want to start thinking about how machine learning and deep learning might be useful in their research, business or career development. This one-day short course will provide a comprehensive overview of statistical machine learning and deep learning methods. Topics include classical methods as well as modern techniques including basic machine learning tools, supervised and unsupervised learning, deep neural network, computational algorithms and software of deep learning, and various applications in deep learning.
 
 

2 * !
Sun, 8/7/2022, 2:00 PM - 3:50 PM CC-202A
Emerging Methods in Quantum Computing, Quantum Information, and Quantum Statistical Learning — Invited Papers
Section on Statistical Computing, Section on Statistical Learning and Data Science, Section on Statistics and Data Science Education
Organizer(s): Ping Ma, University of Georgia
Chair(s): Ping Ma, University of Georgia
2:05 PM Wavelet Matrix Operations and Quantum Transforms
Zhiguo Zhang, University of Electronic Science and Technology of China; Mark Kon, Boston University
2:35 PM Statistical Computing Meets Quantum Computing
Wenxuan Zhong, University of Georgia; Yuan Ke, University of Georgia; Ping Ma, University of Georgia
3:05 PM The Role of Statistics in Quantum Computation and Quantum Information
Yazhen Wang, University of Wisconsin-Madison
3:25 PM Floor Discussion
 
 

3 * !
Sun, 8/7/2022, 2:00 PM - 3:50 PM CC-152A
New Developments and Challenges for Dynamic Individualized Treatments — Invited Papers
Section on Statistical Learning and Data Science, IMS, Biometrics Section
Organizer(s): Annie Qu, UC Irvine
Chair(s): Annie Qu, UC Irvine
2:05 PM Optimal Treatment Regime Estimation for a Target Population with Summary Statistics Only
Wenbin Lu, North Carolina State University; Shu Yang, North Carolina State University; Jianing Chu, North Carolina State University
2:30 PM Constructing Stabilized Dynamic Surveillance Rules for Optimal Monitoring Schedule
Xinyuan Dong, Amazon Inc; Yingye Zheng, Fred Hutchinson Cancer Research Center; Yingqi Zhao, Fred Hutchinson Cancer Research Center
2:55 PM A Proximal Temporal Consistency Approach for Infinite Horizon Dynamic Treatment Regime
Ruoqing Zhu, University of Illinois at Urbana-Champaign
3:20 PM Data adaptive estimation of individualized treatment strategies
Ashkan Ertefaie, University of Rochester
3:45 PM Floor Discussion
 
 

8 * !
Sun, 8/7/2022, 2:00 PM - 3:50 PM CC-150B
The Best of AOAS — Invited Papers
IMS, Section on Statistical Consulting, Section on Statistical Learning and Data Science, Text Analysis Interest Group
Organizer(s): Karen Kafadar, University of Virginia
Chair(s): Karen Kafadar, University of Virginia
2:05 PM Crime Linkage Detection by Spatial-Temporal-Textual Point Processes
Yao Xie, Georgia Institute of Technology; Shixiang Zhu, Georgia Institute of Technology
2:35 PM Integrating Geostatistical Maps and Infectious Disease Ttransmission Models Using Adaptive Multiple Importance Sampling
Renata Retkute, University of Cambridge; Panayiota Touloupou, University of Birmingham; Maria-Gloria Basanez, Imperial College London; Simon E.F. Spencer, University of Warwick; Christopher A Gilligan, University of Cambridge
3:05 PM Monitoring Vaccine Safety by Studying Temporal Variation of Adverse Events Using Vaccine Adverse Event Reporting System
Jing Huang, University of Pennsylvania; Yi Cai, AT&T Services, Inc.; Jingcheng Du, Melax Tech; Ruosha Li, The University of Texas Health Science Center at Houston; Susan Ellenberg, University of Pennsylvania; Sean Hennessy, University of Pennsylvania; Cui Tao, University of Texas Health Science Center at Houston; Yong Chen, University of Pennsylvania
3:35 PM Floor Discussion
 
 

11 * !
Sun, 8/7/2022, 2:00 PM - 3:50 PM CC-150A
Modern Machine Learning Tools for Social Science — Invited Papers
Social Statistics Section, IMS, Section on Statistical Learning and Data Science, Text Analysis Interest Group
Organizer(s): Jiashun Jin, Carnegie Mellon University
Chair(s): Rui Song, North Carolina State University
2:05 PM Evidence-Based Elections Presentation
Philip B Stark, UC Berkeley
2:35 PM Community Detection in Networks with Covariates
Wanjie Wang, National University of Singapore
3:05 PM The Citation Behavior of Statisticians
Jiashun Jin, Carnegie Mellon University; Tracy Ke, Harvard University; Pengsheng Ji, University of Georgia; Wanshan Li, Carnegie Mellon University
3:35 PM Floor Discussion
 
 

15 !
Sun, 8/7/2022, 2:00 PM - 3:50 PM CC-102A
Subsampling: Basic Tool That Facilitates the Identification of Statistical Relationships in Big Data — Topic Contributed Papers
Section on Statistical Learning and Data Science, International Indian Statistical Association, Section on Physical and Engineering Sciences
Organizer(s): Rakhi Singh, UNC Greensboro
Chair(s): Rakhi Singh, UNC Greensboro
2:05 PM Unweighted Estimation Based on Optimal Sample Under Measurement Constraints
Jing Wang, University of Connecticut
2:25 PM Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data
HaiYing Wang, Uninversity of Connecticut; Aonan Zhang, ByteDance Inc.; Chong Wang, ByteDance Inc.
2:45 PM Using Subsampling to Speed up Training in Attention-Based NNs: A Case Study at Doing Statistics at Scale in the Amazon Supply Chain
Dean p foster, Amazon; Kenny Shirley, Amazon
3:05 PM Supervised Compression of Big Data
Roshan V Joseph, Georgia Institute of Technology; Simon Mak, Duke University
3:25 PM Subdata Selection Methods
John Stufken, UNC Greensboro
3:45 PM Floor Discussion
 
 

22 * !
Sun, 8/7/2022, 2:00 PM - 3:50 PM CC-143B
Statistical Learning in Cancer Medicine: From Early-Phase Trials to Preclinical Systems — Topic Contributed Papers
International Chinese Statistical Association, Biometrics Section, Section on Statistical Learning and Data Science
Organizer(s): Li-Xuan Qin, Memorial Sloan Kettering Cancer Center
Chair(s): Li-Xuan Qin, Memorial Sloan Kettering Cancer Center
2:05 PM Hybrid Designs Using Concurrent Randomized Controls and Historical Evidence
Mithat Gonen, Memorial Sloan Kettering Cancer Center
2:25 PM A Review of Bayesian Early-Phase Clinical Trials in Cancer Research
Hao Liu, Rutgers University
2:45 PM Treatment Response Data for Patient-Derived Xenografts
Michael Lloyd, The Jackson Laboratory; Soner Koc, Seven Bridges Genomics; Anuj Srivastava, The Jackson Laboratory; Vivek Philip, The Jackson Laboratory; Tim Stearns, The Jackson Laboratory; Carol Bult, The Jackson Laboratory; Yvonne Evrard, Leidos Biomedical Research, Frederick National Laboratory for Cancer Research; Jeffrey Moscow, Investigational Drug Branch, National Cancer Institute; Funda Meric-Bernstam, The University of Texas MD Anderson Cancer Center; Dennis Dean, Seven Bridges Genomics; Jeffrey H. Chuang, The Jackson Laboratory for Genomic Medicine
3:05 PM Probabilistic Learning of Treatment Trees in Cancer
Veera Baladandayuthapani, University of Michigan; Tsung-Hung Yao, University of Michigan; Zhenke Wu, University of Michigan, Ann Arbor; Karthik Bharath, University of Nottingham
3:25 PM Discussant: Lisa McShane, National Cancer Institute
3:45 PM Floor Discussion
 
 

24 * !
Sun, 8/7/2022, 2:00 PM - 3:50 PM CC-154A
High-Performance Statistical Computing: Current Trends and Future Prospects — Topic Contributed Panel
Section on Statistical Computing, Section on Statistical Learning and Data Science, Section on Physical and Engineering Sciences
Organizer(s): Sameh Abdulah, KAUST
Chair(s): Sameh Abdulah, KAUST
2:05 PM High-Performance Statistical Computing: Current Trends and Future Prospects
Panelists: Marc Genton, KAUST
Dorit Hammerling, Colorado School of Mines
Zhengqing Ouyang, University of Massachusetts, Amherst
George Ostrouchov, ORNL
Hatem Ltaief, KAUST
3:40 PM Floor Discussion
 
 

27
Sun, 8/7/2022, 2:00 PM - 3:50 PM CC-140A
SPEED: Statistical Learning and Data Challenge Part 1 — Contributed Speed
Section on Statistical Learning and Data Science, Section on Bayesian Statistical Science, Section on Statistics and Data Science Education
Chair(s): Dena M Asta, The Ohio State University
2:05 PM Understanding Changes of Racial and Ethnic Representation in Homeowners and US Post-Secondary Institutions
Jhonatan Jorge Medri Cobos, University of South Florida; Tejasvi Channagiri, University of South Florida
2:10 PM Examining Relationships Between Household Conditions and Educational Outcomes at the District Level
Erin Walker Post, The University of Iowa
2:15 PM Public Transit Policies to Promote Equitable Urban Mobility
Jenny Y Huang, Duke University; Gaurav Rajesh Parikh, Duke Kunshan University; Albert Sun, Duke University
2:20 PM LinCDE: Conditional Density Estimation via Lindsey's Method
Zijun Gao, Stanford University; Trevor Hastie, Stanford University
2:25 PM Quantifying Estimation Error of Nonlinear Kalman-Type Filters
Shihong Wei, The Johns Hopkins University; James Spall, The Johns Hopkins University
2:30 PM Random Forest for Individualized Treatment Regimes in Observational Student Success Studies
Juanjuan Fan, San Diego State University; Luo Li, San Diego State University; Richard A Levine, San Diego State University
2:35 PM Stochastic Gradient Descent for Estimation and Inference in Spatial Quantile Models
Gan Luan, New Jersey Institute of Technology; Jimeng Loh, NJIT
2:40 PM Popularity Adjusted Block Models Are Generalized Random Dot Product Graphs
John Koo, Indiana University; Minh Tang, North Carolina State University; Michael Trosset, Indiana University
2:50 PM Extrapolation Control Using K-Nearest Neighbors
Kasia Dobrzycka, North Carolina State University; Jonathan Stallrich, North Carolina State University; Christopher M. Gotwalt, SAS Institute
2:55 PM A Continual Learning Framework for Adaptive Defect Classification and Inspection
Wenbo Sun, University of Michigan Transportation Research Institute; Raed Al Kontar, University of Michigan; Judy Jin, University of Michigan; Tzyy-Shuh Chang, OG Technology
3:00 PM HODOR: A Two-Stage Hold-Out Design for Online Controlled Experimentation on Networks
Nicholas Alfredo Larsen, North Carolina State University; Jonathan Stallrich, North Carolina State University; Srijan Sengupta, NCSU
3:05 PM Utilizing Open Source Resources to Teach Introductory Data Science
Tyler George, Cornell College
3:10 PM Fast Bayesian Estimation for Ranking Models
Michael Pearce, University of Washington
3:15 PM Diversity in Project-Based Learning Strategy in Undergraduate Statistics Education
Shurong Fang, John Carroll University; Lisa Dierker, Wesleyan University
3:20 PM Incorporating Cultural Context in Statistics Courses Through Presentation of Music Videos Before Class
Thomas R. Belin, UCLA Department of Biostatistics
3:25 PM Boosting Students' Programming Interest Using an R Shiny Web App Rstats in Introductory Statistics Courses
Xuemao Zhang, East Stroudsburg University
3:30 PM Student Perceptions on Reproducible Research in Introductory and Advanced Statistics Courses
Nicholas W Bussberg, Elon University
3:35 PM Changes in Undergraduate Attitudes Towards Statistics After Working as Statistical Consultants
Tracy Morris, University of Central Oklahoma; Tyler Cook, University of Central Oklahoma; Cynthia Murray, University of Central Oklahoma
3:40 PM Floor Discussion
 
 

34
Sun, 8/7/2022, 2:00 PM - 3:50 PM CC-144A
Advanced Methods in Statistical Learning — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Jason Brinkley, Abt Associates
2:05 PM Bayesian Algorithms Learn to Stabilize Unknown Stochastic Differential Equations
Mohamad Kazem Shirani Faradonbeh, University of Georgia
2:20 PM An Augmented Multivariate Hidden Markov Model to Capture Dynamics in Freely Diffusing SmFRET Experiments
Axel Cortes, University of Puerto Rico-Mayaguez; Roberto Rivera, University of Puerto Rico-Mayaguez; Sebastian Alzate, University of Puerto Rico-Mayaguez
2:35 PM The Third Moment Tensor Method with Principal Components and Basis Expansion Presentation
W. D. Brinda, Quantitations LLC; Ruchira Ray, Columbia University
2:50 PM Finding Higher Order Interactions Using Local Corex
Thomas J Kerby, Utah State University; Dr. Kevin Moon, Utah State University; Greg Steeg, Information Sciences Institute
3:05 PM Data Integration via Analysis of Subspaces (DIVAS)
Jack B. Prothero, National Institute of Standards and Technology; Jan Hannig, University of Noerth Carolina at Chapel Hill; J. S. (Steve) Marron, UNC; Quoc Tran-Dinh, University of North Carolina Chapel Hill; Meilei Jiang, Meta
3:20 PM Floor Discussion
 
 

38 !
Sun, 8/7/2022, 4:00 PM - 5:50 PM CC-144B
Recent Advances in Adaptive Treatment Strategy Estimation — Invited Papers
Section on Statistics in Epidemiology, International Chinese Statistical Association, Section on Statistical Learning and Data Science
Organizer(s): Ai Ni, The Ohio State University
Chair(s): Yuan Chen, Memorial Sloan Kettering Cancer Center
4:05 PM Adaptive Respondent-Driven Sampling
Eric Laber, Duke University; Justin Weltz, Duke University; Alex Volfovsky, Duke University
4:30 PM Ranking Tailoring Variables for Constructing Individualized Treatment Rules
Jiacheng Wu, University of Washington; Nina Galanter, University of Washington; Susan M Shortreed, Kaiser Permanente Washington Health Research Institute; Erica EM Moodie, McGill University
4:55 PM Contrast-Based Optimal Treatment Rule Estimation with Composite Survival Data
Ai Ni, The Ohio State University; Xiaohan Guo, The Ohio State University
5:20 PM Discussant: Yuanjia Wang, Columbia University
5:40 PM Floor Discussion
 
 

42 * !
Sun, 8/7/2022, 4:00 PM - 5:50 PM CC-150A
Recent Developments of Statistical Methods for Microbiome Research — Invited Papers
Biometrics Section, ENAR, Section on Statistical Learning and Data Science
Organizer(s): Gen Li, University of Michigan
Chair(s): Stephanie Garcia, Florida International University
4:05 PM Deep Learning to Predict the Biosynthetic Gene Clusters in Bacterial Genomes
Hongzhe Li, University of Pennsylvania
4:35 PM A Bayesian Joint Model for Mediation Effect Selection in Compositional Microbiome Data
Marina Vannucci, Rice University
5:05 PM Phylogenetically Informed Bayesian Truncated Copula Graphical Models for Microbial Association Networks Presentation
Irina Gaynanova, Texas A&M University; Hee Cheol Chung, Texas A&M University; Yang Ni, Texas A&M University
5:35 PM Floor Discussion
 
 

47 * !
Sun, 8/7/2022, 4:00 PM - 5:50 PM CC-152B
Causal Inference in the Presence of Nuisance Parameters: Latest Developments — Invited Papers
IMS, ENAR, Section on Statistical Learning and Data Science
Organizer(s): Judith Jacqueline Lok, Boston University
Chair(s): Qingyan Xiang, Boston University
4:05 PM Automatic Debiased Machine Learning via Neural Nets for Generalized Linear Regression Presentation
Whitney Newey, MIT Economics; Victor Chernozhukov, MIT Economics; Vasilis Syrgkanis, Microsoft Research; Victor Quintas-Martinez, MIT Economics
4:30 PM Sequentially Debiased Estimation of Identified Total Effects in Causal Graphical Models with Hidden Variables
Andrea ROTNITZKY, Universidad Torcuato Di Tella; Ezequiel Smucler, Glovo; James M Robins, Harvard University
4:55 PM How Estimating Nuisance Parameters Often Reduces the Variance (With Variance Correction)
Judith Jacqueline Lok, Boston University
5:20 PM Discussant: Oliver Dukes, University of Pennsylvania
5:40 PM Floor Discussion
 
 

50 * !
Sun, 8/7/2022, 4:00 PM - 5:50 PM CC-158AB
Industry Applications for Environmental Statistics — Topic Contributed Papers
Section on Statistics and the Environment, Section on Statistical Learning and Data Science, Committee on Applied Statisticians
Organizer(s): Maria A Terres, Waymo
Chair(s): Maria A Terres, Waymo
4:05 PM Autonomously Driven Vehicles and the Weather Around Them
Jiabin Liu, Waymo
4:25 PM Environmental Statistics in Computational Agriculture
David Clifford, X, the moonshot factory
4:45 PM Data Science and Applied Statistics in Climate Risk Analysis
Alexis Hoffman, Jupiter Intelligence
5:05 PM Using Physics-Informed Machine Learning to Generate Global Medium-Range Probabilistic Weather Forecasts
Steven Joel Brey, Tomorrow.io; Ashley Payne, Tomorrow.io; Stelios flampouris, Tomorrow.io
5:25 PM Floor Discussion
 
 

52 * !
Sun, 8/7/2022, 4:00 PM - 5:50 PM CC-204B
Contrastive Dimension Reduction: Exploring Differential Patterns in High-Dimensional Data — Topic Contributed Papers
Section on Statistical Learning and Data Science, Section on Statistics in Genomics and Genetics, International Society for Bayesian Analysis (ISBA)
Organizer(s): Andrew Jones, Princeton University
Chair(s): Didong Li, Princeton University
4:05 PM Exploring Patterns Enriched in a Data Set with Contrastive Principal Component Analysis
Abubakar Abid, Stanford; James Zou, Stanford University
4:25 PM Exploring High-Dimensional Biological Data with Sparse Contrastive Principal Component Analysis
Philippe Boileau, University of California, Berkeley; Nima S Hejazi, Weill Cornell Medicine; Sandrine Dudoit, University of California, Berkeley
4:45 PM Probabilistic Models for Contrastive Dimension Reduction with Applications to Sequencing Data
Andrew Jones, Princeton University; Barbara E. Engelhardt, Princeton University
5:05 PM Floor Discussion
 
 

67 * !
Sun, 8/7/2022, 4:00 PM - 5:50 PM CC-141
Advances in Variable Selection — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Seema Sangari, Kennesaw State University
4:05 PM Properties and Applications of Feature Whitening
Ana Maria Kenney, UC Berkeley; Francesca Chiaromonte, Pennsylvania State University and Sant’Anna School of Advanced Studies
4:20 PM Are Latent Factor Regression and Sparse Regression Adequate?
Mengxin Yu, Princeton University; Jianqing Fan, Princeton University; Zhipeng Lou, Princeton University
4:35 PM A Feature Selection Technique for Binary Classification Problem
Ayman Alzaatreh, American University of Sharjah
4:50 PM Sample Size Calibration by FDR-Power Tradeoff for Logistic Regression in High Dimensions
Gerda Claeskens, KU Leuven; Jing Zhou, KU Leuven
5:05 PM Reluctant Interaction Modeling in Generalized Linear Models
Guo Yu, University of California Santa Barbara
5:20 PM Robust Estimation in High-Dimensional Sparse Heteroscedastic Linear Models
Duzhe Wang, Eli Lilly and Company
5:35 PM Flexible Structural Varying-Coefficient Regression to Better Predict Outcomes in Complex Neurogenerative Diseases
Farzaneh Boroumand, Macquarie University ; Samuel Muller, Macquarie University; Tanya P. Garcia, University of North Carolina at Chapel Hill; Rakheon Kim, Baylor University
 
 

72
Sun, 8/7/2022, 4:00 PM - 4:45 PM CC-Hall D
SPEED: Statistical Learning and Data Challenge Part 2 — Contributed Poster Presentations
Section on Statistical Learning and Data Science, Section on Bayesian Statistical Science, Section on Statistics and Data Science Education
Chair(s): Dena M Asta, The Ohio State University
01: Understanding Changes of Racial and Ethnic Representation in Homeowners and US Post-Secondary Institutions
Jhonatan Jorge Medri Cobos, University of South Florida; Tejasvi Channagiri, University of South Florida
02: Examining Relationships Between Household Conditions and Educational Outcomes at the District Level
Erin Walker Post, The University of Iowa
03: Public Transit Policies to Promote Equitable Urban Mobility
Jenny Y Huang, Duke University; Gaurav Rajesh Parikh, Duke Kunshan University; Albert Sun, Duke University
04: Are There Home Affordability Hot Spots in the United States?
Dane Korver, NC State University; Maegan Frederick, NC State University; Fang Wu, NC State University
05: LinCDE: Conditional Density Estimation via Lindsey's Method
Zijun Gao, Stanford University; Trevor Hastie, Stanford University
06: Quantifying Estimation Error of Nonlinear Kalman-Type Filters
Shihong Wei, The Johns Hopkins University; James Spall, The Johns Hopkins University
07: Random Forest for Individualized Treatment Regimes in Observational Student Success Studies
Juanjuan Fan, San Diego State University; Luo Li, San Diego State University; Richard A Levine, San Diego State University
08: Stochastic Gradient Descent for Estimation and Inference in Spatial Quantile Models
Gan Luan, New Jersey Institute of Technology; Jimeng Loh, NJIT
09: Popularity Adjusted Block Models Are Generalized Random Dot Product Graphs
John Koo, Indiana University; Minh Tang, North Carolina State University; Michael Trosset, Indiana University
10: Extrapolation Control Using K-Nearest Neighbors
Kasia Dobrzycka, North Carolina State University; Jonathan Stallrich, North Carolina State University; Christopher M. Gotwalt, SAS Institute
11: Changes in Undergraduate Attitudes Towards Statistics After Working as Statistical Consultants
Tracy Morris, University of Central Oklahoma; Tyler Cook, University of Central Oklahoma; Cynthia Murray, University of Central Oklahoma
12: A Continual Learning Framework for Adaptive Defect Classification and Inspection
Wenbo Sun, University of Michigan Transportation Research Institute; Raed Al Kontar, University of Michigan; Judy Jin, University of Michigan; Tzyy-Shuh Chang, OG Technology
13: HODOR: A Two-Stage Hold-Out Design for Online Controlled Experimentation on Networks
Nicholas Alfredo Larsen, North Carolina State University; Jonathan Stallrich, North Carolina State University; Srijan Sengupta, NCSU
14: Utilizing Open Source Resources to Teach Introductory Data Science
Tyler George, Cornell College
15: Fast Bayesian Estimation for Ranking Models
Michael Pearce, University of Washington
16: Diversity in Project-Based Learning Strategy in Undergraduate Statistics Education
Shurong Fang, John Carroll University; Lisa Dierker, Wesleyan University
17: Incorporating Cultural Context in Statistics Courses Through Presentation of Music Videos Before Class
Thomas R. Belin, UCLA Department of Biostatistics
18: Boosting Students' Programming Interest Using an R Shiny Web App Rstats in Introductory Statistics Courses
Xuemao Zhang, East Stroudsburg University
19: Developing Data Science Skills Using Call of Duty Data
Matt Slifko, High Point University
20: Student Perceptions on Reproducible Research in Introductory and Advanced Statistics Courses
Nicholas W Bussberg, Elon University
 
 

87 !
Mon, 8/8/2022, 8:30 AM - 10:20 AM CC-206
Reduced-Rank Methods: Seventy Years of History and State-of-the-Art Developments — Invited Papers
General Methodology, Section on Statistical Learning and Data Science, Business and Economic Statistics Section
Organizer(s): Kun Chen, University of Connecticut
Chair(s): Kun Chen, University of Connecticut
8:35 AM History of Reduced-Rank Regression: Past and Present
Raja Velu, Syracuse University
9:05 AM Envelopes and Related Paradigms for Dimension and Rank Reduction in Regression
Dennis Cook, University of Minnesota
9:35 AM Spectral Learning for High-Dimensional Tensors
Ming Yuan, Columbia University
10:05 AM Floor Discussion
 
 

114
Mon, 8/8/2022, 8:30 AM - 10:20 AM CC-155
Time Series Methods and Applications — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Mohamad K S Faradonbeh, University of Georgia
8:35 AM K-ARs: Fast Large-Scale Time Series Clustering
Victor Solo, UNSW Sydney
8:50 AM Recursive Partitioning for Interpretable Prescriptive Policy Making
Maria Lentini, Rowan University; Umashanger Thayasivam, Rowan University
9:05 AM A Novel Neural Network Estimator for the AR(1) Model
Angela Folz, National Institute of Standards and Technology/University of Colorado Boulder; Mary Gregg, National Institute of Standards and Technology; Lucas Koepke, National Institute of Standards and Technology; Michael Frey, National Institute of Standards and Technology
9:20 AM A Computationally Efficient Model for Large Scale Crop Type Forecasting Presentation
Jonathon Abernethy, USDA NASS; Luca Sartore , USDA NASS; Kevin Hunt, USDA NASS; Claire Boryan, USDA NASS
9:35 AM Variational Objectives for Sequential Vartional AutoEncoders by Sequential Bayes Filtering
Tsuyoshi Ishizone, Meiji University; Tomoyuki Higuchi, Chuo University; Kazuyuki Nakamura, Meiji University
9:50 AM Near-Optimal Inference in Adaptive Linear Regression
Koulik Khamaru, University of California, Berkeley; Lester Mackey, Microsoft Research New England; Martin J. Wainwright, University of California, Berkeley; Yash Deshpande, The Voleon group
10:05 AM Time-per-Stop Forecasting in Last-Mile Deliveries
Chuchu Cheng, Amazon
 
 

115
Mon, 8/8/2022, 8:30 AM - 10:20 AM CC-156
Advances in Clustering and Classification — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Tianxi Li, University of Virginia
8:35 AM Label Shift and Generalizable Classifiers
Ciaran Evans, Wake Forest University; Max G'Sell, Carnegie Mellon University
8:50 AM Selective Inference for K-Means Clustering
Yiqun Chen, University of Washington, Seattle; Daniela Witten, University of Washington
9:05 AM Instance Selection with Threshold Clustering for Support Vector Machines
Michael Higgins, Kansas State University; Tahany Basir, Kansas State University
9:20 AM Multiway Spherical Clustering via Degree-Corrected Tensor Block Models
Jiaxin Hu, University of Wisconsin-Madison; Miaoyan Wang, University of Wisconsin-Madison
9:35 AM Application of Machine Learning for Predicting Outcomes in a Random Effect Clustered Bivariate Model
Edmund Essah Ameyaw, Howard University; Seth Akonor Adjei, Northern Kentucky University; John Kwagyan, Howard University
9:50 AM Domain-Dependent Classification with Geometric Digraphs
Antony Pearson, Auburn University; Elvan Ceyhan, Auburn University
10:05 AM Floor Discussion
 
 

117 * !
Mon, 8/8/2022, 10:30 AM - 12:20 PM CC-143C
Statistica Sinica Special Invited Papers on Causal Inference — Invited Papers
International Chinese Statistical Association, Section on Statistical Learning and Data Science
Organizer(s): Xiaotong Shen, University of Minnesota
Chair(s): Rong Chen, Rutgers University
10:35 AM Robust Inference of Conditional Average Treatment Effects Using Dimension Reduction
Ming-Yueh Huang, Academia Sinica
11:15 AM A Generalized Heteroscedastic Gaussian Process Model
Jun Liu, Harvard University
11:55 AM Floor Discussion
 
 

118
Mon, 8/8/2022, 10:30 AM - 12:20 PM CC-143B
Recent Advances in Change-Point Analysis — Invited Papers
Section on Nonparametric Statistics, Section on Statistical Learning and Data Science, IMS
Organizer(s): Xiaofeng Shao, University of Illinois at Urbana-Champaign
Chair(s): Zifeng Zhao, University of Notre Dame
10:35 AM Nonparametric Online Change-Point Detection in High Dimensions
Ali Shojaie, University of Washington
11:00 AM Are Deviations in a Gradually Varying Mean Relevant? a Testing Approach Based on Sup-Norm Estimators
Holger Dette, Ruhr University Bochum; Axel Bücher, Heinrich Heine University Düsseldorf; Florian Heinrichs, Ruhr-Universität Bochum
11:25 AM Graph-Based Multiple Change-Point Detection
Yuxuan Zhang, University of California, Davis; Hao Chen, University of California, Davis
11:50 AM Toward Automatic Change-Point Testing and Detection in Time Series via Deep Learning Presentation
Piotr Fryzlewicz, London School of Economics; Jie Li, London School of Economics; Paul Fearnhead, Lancaster University; Tengyao Wang, London School of Economics
12:15 PM Floor Discussion
 
 

125 * !
Mon, 8/8/2022, 10:30 AM - 12:20 PM CC-150B
Practical Recommendations for Prediction Modeling That Advance Innovation — Invited Papers
Section on Statistical Learning and Data Science, Biometrics Section, Health Policy Statistics Section
Organizer(s): Jaime Lynn Speiser, Wake Forest School of Medicine
Chair(s): Heather Shappell, Wake Forest School of Medicine
10:35 AM Variable Selection with Random Forest: Choosing the Best Method for Different Types of Data
Jaime Lynn Speiser, Wake Forest School of Medicine
10:55 AM Current Methods for Evaluating Prediction Model Performance
Michael W. Kattan, Cleveland Clinic
11:15 AM Cross Validation for Small and Imbalanced Data
Nathaniel O'Connell, Wake Forest School of Medicine
11:35 AM Disseminating Prediction Methods: Avoiding Computational Bottlenecks and Developing User-Friendly APIs
Byron Casey Jaeger, Wake Forest School of Medicine
11:55 AM Discussant: Joseph Rigdon, Wake Forest School of Medicine
12:15 PM Floor Discussion
 
 

126 * !
Mon, 8/8/2022, 10:30 AM - 12:20 PM CC-101
Topics at the Frontier of Statistical Computing and Machine Learning — Invited Papers
Section on Bayesian Statistical Science, Section on Statistical Computing, Section on Statistical Learning and Data Science
Organizer(s): Robert Kohn, University of New South Wales
Chair(s): Hung Dao, University of New South Wales
10:35 AM Flexible Variational Bayes Based on a Copula of a Mixture of Normals
Robert Kohn, University of New South Wales; David Gunawan, School of Mathematics and Applied Statistics; David Nott, University of Singapore
11:05 AM Variational Bayes on Manifolds
Minh-Ngoc Tran, University of Sydney; Dang Nguyen, University of Alabama; Duy Nguyen, Marist College
11:35 AM Sparse Hamiltonian Flows (Or Bayesian Coresets Without All the Fuss)
Trevor Campbell, UBC; Naitong Chen, University of British Columbia; Zuheng Xu, University of British Columbia - Vancouver, BC
12:05 PM Floor Discussion
 
 

133 * !
Mon, 8/8/2022, 10:30 AM - 12:20 PM CC-207B
Equity in Innovation: Should Race and Ethnicity Be Included in Clinical Prediction Models and Algorithms? — Topic Contributed Papers
Biometrics Section, Justice Equity Diversity and Inclusion Outreach Group, Section on Statistical Learning and Data Science, Caucus for Women in Statistics
Organizer(s): Yates Coley, Kaiser Permanente Washington Health Research Institute
Chair(s): Trang Nguyen, Johns Hopkins Bloomberg School of Public Health
10:35 AM Implications of Race-Based Estimates of Kidney Function
Leila Zelnick, University of Washington
10:55 AM Incorporating Ethical Judgements into Measures and Causal Decompositions of Health Disparities
John W Jackson, Johns Hopkins University; Trang Nguyen, Johns Hopkins Bloomberg School of Public Health; Ting-Hsuan Chang, Johns Hopkins Bloomberg School of Public Health
11:15 AM FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data
Zhun Deng, Harvard University
11:35 AM Evaluating the Clinical Impact of Racial/Ethnic Disparities in the Performance of Risk Prediction Models
Aasthaa Bansal, University of Washington; Sara Khor, University of Washington
11:55 AM Discussant: Stephanie Cook, New York University
12:15 PM Floor Discussion
 
 

140 * !
Mon, 8/8/2022, 10:30 AM - 12:20 PM CC-158AB
Disease Outbreak and Modeling Applications in Defense and National Security — Topic Contributed Papers
Section on Statistics in Defense and National Security, Section on Statistical Learning and Data Science
Organizer(s): Joseph D Warfield, John Hopkin University Applied Physics Lab
Chair(s): Joseph D Warfield, John Hopkin University Applied Physics Lab
10:35 AM A Holistic Approach to Comparing Infectious Disease Forecasting Methods Presentation
Karl Pazdernik, Pacific Northwest National Laboratory; Samuel Dixon, Pacific Northwest National Laboratory; Ravikiran Keshava Murthy, Pacific Northwest National Laboratory; Brent Daniel, Pacific Northwest National Laboratory; Andrew Stevens, Pacific Northwest National Laboratory; Lauren Charles, Pacific Northwest National Laboratory
10:55 AM A Statistical Model for the Spread of SARS-CoV-2 in New Mexico
Lyndsay Shand, Sandia National Laboratories; Adah Zhang, Sandia National Laboratories; Alexander Foss, Sandia National Laboratories; James Derek Tucker, Sandia National Laboratories; Gabriel Huerta, Sandia National Laboratories; Audrey McCombs, Sandia National Laboratories
11:15 AM Agent-Based Modeling for Evaluation of a Wearable-Sensor-Based Disease Surveillance Network
Ivan Stanish, Johns Hopkins University Applied Physics Laboratory; Joseph D Warfield, John Hopkin University Applied Physics Lab; Jane E. Valentine, Johns Hopkins University Applied Physics Laboratory; Damon C Duquaine, Johns Hopkins University Applied Physics Laboratory; Ariel M. Greenberg, Johns Hopkins University Applied Physics Laboratory; James P. Howard, Johns Hopkins University Applied Physics Laboratory
11:35 AM Evaluation of the United States COVID-19 Vaccine Allocation Strategy
Audrey McCombs, Sandia National Laboratories; Md Rafiul Islam, Iowa State University; Tamer Oraby, University of Texas Rio Grand Valley; Mohammad Mihrab Chowdhury, Texas Tech University; Mohammad Al-Mamun, West Virginia University; Michael Tyshenko, University of Ottowa; Claus Kadelka, Iowa State University
11:55 AM Discussant: Howard Burkom, Johns Hopkins University Applied Physics Lab
12:15 PM Floor Discussion
 
 

149
Mon, 8/8/2022, 10:30 AM - 12:20 PM CC-144A
Statistical Learning for Decision Support — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): David Kline, Wake Forest University
10:35 AM Generalized V-Learning Framework for Estimating Dynamic Treatment Regimes
Duyeol Lee, Wells Fargo; Michael Kosorok, University of North Carolina at Chapel Hill
10:50 AM Assign Experiment Variants at Scale in Online Controlled Experiments
Qike Li, Wish; Samir Jamkhande, Wish
11:05 AM A Scalable and Flexible Cox Proportional Hazard Model for High-Dimensional Survival Prediction and Functional Selection
Boyi Guo, University of Alabama at Birmingham; Nengjun Yi, University of Alabama at Birmingham
11:20 AM On Kernel Fusion and Learning for Kernels Ensuing from the Decision and Regression Tree Ensembles
Dai Feng, AbbVie; David Windridge, Middlesex University London; University of Surrey; Santosh Tirunagari, Middlesex University London; Richard Baumgartner, Merck Research Laboratories
11:35 AM A Workflow Time Study to Estimate Savings from the Implementation of a Machine Learning Algorithm to Automate Fall Risk Assessment
Gregg M Gascon, OhioHealth; Erika Braun, OhioHealth; Victoria Zigmont, OhioHealth; Amy Tracy, OhioHealth; Michelle Murray, OhioHealth; Shalunda Tyler, OhioHealth; Jason Kaufman, OhioHealth; Stephanie Risteff, OhioHealth (retired); Eleanora Vassileva, OhioHealth; Bradley Waller, The Ohio State University; J. Michael Kramer, OhioHealth
11:50 AM Stochastic Ordered Empirical Risk Minimization
Ronak Mehta, University of Washington; Krishna Pillutla, University of Washington; Vincent Roulet, University of Washington; Zaid Harchaoui, University of Washington
12:05 PM How Real Is Synthetic Missing Data? Impact of Missing Pattern Modeling on Imputer Evaluation
Rohan Chakraborty, pymetrics; Ambar Kleinbort, pymetrics; Janelle Szary, pymetrics; Anne Thissen-Roe, pymetrics
 
 

Register 165
Mon, 8/8/2022, 12:30 PM - 1:50 PM CC-Ballroom Level South Prefunction
Section on Statistical Learning and Data Science P.M. Roundtable Discussion (Added Fee) — Roundtables PM Roundtable Discussion
Section on Statistical Learning and Data Science
ML13: Reproducible Research in Data Science
Elizabeth C Chase, University of Michigan
 
 

176 * !
Mon, 8/8/2022, 2:00 PM - 3:50 PM CC-207B
New England Statistical Society Invited Papers on Novel Developments and Future Directions of Statistics in Data Science — Invited Papers
New England Statistical Society, General Methodology, Section on Statistical Learning and Data Science
Organizer(s): Colin O. Wu, National Heart, Lung and Blood Institute, NIH
Chair(s): Minge Xie, Rutgers University
2:05 PM Analyzing Survival Data with Graphical Proportional Hazards Measurement Error Models
Grace Yi, University of Western Ontario
2:30 PM Innovative Applications of Hidden Markov Models in Cancer Data Science
Paul S. Albert, National Cancer Institute
2:55 PM A Composite Endpoint for Treatment Benefit According to Patient Preference
Ying Lu, Stanford University School of Medicine; Ruben van Eijk, University Medical Center Utrecht (UMCU), Utrecht, the Netherlands; Lu Tian, Stanford University; Lori Nelson, Stanford University
3:20 PM Discussant: Colin O. Wu, National Heart, Lung and Blood Institute, NIH
3:40 PM Floor Discussion
 
 

188 !
Mon, 8/8/2022, 2:00 PM - 3:50 PM CC-150B
SLDS Student Paper Awards — Topic Contributed Papers
Section on Statistical Learning and Data Science
Organizer(s): Jacob Bien, University of Southern California; Xingye Qiao, Binghamton University
Chair(s): Jacob Bien, University of Southern California
2:05 PM Inference for Heteroskedastic PCA with Missing Data
Yuling Yan, Princeton University; Yuxin Chen, University of Pennsylvania; Jianqing Fan, Princeton University
2:25 PM Floodgate: Inference for Model-Free Variable Importance Presentation
Lu Zhang, Harvard University; Lucas Janson, Harvard University
2:45 PM Optimal Variable Clustering for High-Dimensional Matrix Valued Data
Inbeom Lee, Cornell University; Siyi Deng, Cornell University; Yang Ning, Cornell University
3:05 PM Multi-Source Learning via Completion of Block-Wise Overlapping Noisy Matrices
Doudou Zhou, University of California, Davis; Tianxi Cai , Harvard University ; Junwei Lu, Harvard University
3:25 PM Crowdsourcing Utilizing Subgroup Structure of Latent Factor Modeling
Qi Xu, Unviersity of California Irvine; Yubai Yuan, UC Irvine; Junhui Wang, City university of Hong Kong ; Annie Qu, UC Irvine
3:45 PM Floor Discussion
 
 

192 * !
Mon, 8/8/2022, 2:00 PM - 3:50 PM CC-143A
Using Ranking Data for Decision-Making — Topic Contributed Papers
Social Statistics Section, Section on Statistical Learning and Data Science, Government Statistics Section
Organizer(s): Elena Erosheva, University of Washington ; Michael Pearce, University of Washington
Chair(s): Michael Pearce, University of Washington
2:05 PM Mixture-of-Experts Models with Item Covariates for Ranking Data
Thomas Brendan Murphy, University College Dublin; Lucy Small, University College Dublin
2:25 PM Joint Confidence Regions for Communicating Uncertainty in Rankings Presentation
Jerzy Wieczorek, Colby College
2:45 PM The Usefulness of Both Ranking and Scoring in the Grant Review of Research Applications
Stephen Gallo, American Institute of Biological Sciences
3:05 PM Dishonest Behavior in Peer Review
Nihar B Shah, Carnegie Mellon University
3:25 PM Ratings as Comparisons with Mileposts -- a Mallows Model
Annelise Wagner, University of Washington; Marina Meila, University of Washington
3:45 PM Floor Discussion
 
 

205
Mon, 8/8/2022, 2:00 PM - 3:50 PM CC-140B
Inference on Functional Data — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Weijing Tang, University of Michigan
2:05 PM Robust Deep Neural Network Estimation for Multi-Dimensional Functional Data
Guanqun Cao, Auburn University; Shuoyang Wang, Auburn University
2:20 PM Mixture Functional Graphical Model
Han Chen, Virginia Tech; Inyoung Kim, Virginia Tech
2:35 PM Network Traffic Anomaly Detection with Continuous Time Markov Chains
Danielle Gewurz, Deloitte Consulting; Mike Greene, Deloitte Consulting ; Bill Roberts, Deloitte Consulting
2:50 PM Sequential Bayesian Registration for Functional Data
Yoonji Kim, The Ohio State University; Oksana Chkrebtii, The Ohio State University; Sebastian Kurtek, The Ohio State University
3:05 PM Robust Inference for Change Points in Piecewise Polynomials
Shakeel A O B Gavioli-Akilagun, London School of Economics; Piotr Fryzlewicz, London School of Economics
3:20 PM Inference for Change Points in High-Dimensional Mean Shift Models
Abhishek Kaul, Washington State University; George Michailidis, U Florida
3:35 PM Floor Discussion
 
 

223619
Mon, 8/8/2022, 4:00 PM - 5:30 PM M-Liberty N
Section on Statistical Learning and Data Science Mixer — Other Cmte/Business
Section on Statistical Learning and Data Science
Chair(s): Ali Shojaie, University of Washington
 
 

220 * !
Tue, 8/9/2022, 8:30 AM - 10:20 AM CC-143B
Frontiers of Spatio-Temporal Statistical Learning in Health Care and Environmental Science — Invited Papers
Section on Statistical Learning and Data Science, Biometrics Section, ENAR
Organizer(s): Xinyi Li, Clemson University; Guannan Wang, College of William and Mary
Chair(s): Shan Yu, University of Virginia
8:35 AM How Close and How Much? Linking Health Outcomes to Spatial Distributions of Built Environment Features
Veronica J Berrocal, University of California
9:00 AM Statistical Learning via Additive Partial Linear Spatial Regression
Xinyi Li, Clemson University; Guanqun Cao, Auburn University; Lily Wang, George Mason University
9:25 AM Spatial Automatic Subgroup Analysis for Areal Data with Repeated Measures
Hao Helen Zhang, University of Arizona; Xin Wang, Miami University; Zhengyuan Zhu, Iowa State University
9:50 AM A Spatiotemporal Epidemiological Prediction Model to Inform County-Level COVID-19 Risk in the United States
Yiwang Zhou, St. Jude Children's Research Hospital; Peter Song, University of Michigan
10:15 AM Floor Discussion
 
 

223 * !
Tue, 8/9/2022, 8:30 AM - 10:20 AM CC-159AB
Forecasting for Policy in an Uncertain and Rapidly Changing World — Invited Papers
Business and Economic Statistics Section, Government Statistics Section, Section on Statistical Learning and Data Science, Caucus for Women in Statistics
Organizer(s): Andrew B Martinez, US Department of the Treasury
Chair(s): Andrew B Martinez, US Department of the Treasury
8:35 AM Augmented Information Rigidity Test
Tucker McElroy, U.S. Census Bureau; Xuguang Simon Sheng, American University
9:00 AM Jointly Modeling Male and Female Labor Participation and Unemployment
David Harry Bernstein, Environmental Protection Agency; Andrew B Martinez, US Department of the Treasury
9:25 AM The Longer-Run Forecasts of the FOMC
Jaime Marquez, Johns Hopkins University
9:50 AM The Wisdom of Diversity in Committees
Neil R Ericsson, Federal Reserve Board; David Hendry, Nuffield College; Yanki Kalfa, Rady School of Management; Jaime Marquez, Johns Hopkins University
10:15 AM Floor Discussion
 
 

228 * !
Tue, 8/9/2022, 8:30 AM - 10:20 AM CC-152A
Promoting Diversity in Sports Analytics — Invited Panel
Section on Statistics in Sports, Section on Statistics and Data Science Education, Section on Statistical Learning and Data Science, Justice Equity Diversity and Inclusion Outreach Group
Organizer(s): Eric Gerber, CSU Bakersfield
Chair(s): Eric Gerber, CSU Bakersfield
8:35 AM Promoting Diversity in Sports Analytics
Panelists: Christien Wright, The Milwaukee Bucks
Sameer Deshpande, UW-Madison
Rebecca Nugent, CMU
Arielle Dror, Zelus Analytics
John Tobias, UNC Charlotte
10:10 AM Floor Discussion
 
 

229 * !
Tue, 8/9/2022, 8:30 AM - 10:20 AM CC-143A
Geostatistical Computing on Modern Parallel Architectures — Topic Contributed Papers
Section on Statistical Computing, Section on Statistical Learning and Data Science, Section on Physical and Engineering Sciences
Organizer(s): Sameh Abdulah, KAUST
Chair(s): Zhuo Qu, King Abdullah University of Science and Technology
8:35 AM Scalable Gaussian-Process Regression and Variable Selection Using Vecchia Approximations Presentation
Jian Cao, Texas A&M University; Matthias Katzfuss, Texas A&M University; Marc Genton, KAUST; Joe Guinness, Cornell University
8:55 AM Parallel Likelihood Function Optimization to Accelerate Air Pollution Prediction on Large-Scale Systems
Mary Lai Salvana, KAUST; Sameh Abdulah, KAUST; Hatem Ltaief, KAUST; Ying Sun, KAUST; Marc Genton, KAUST; David Keyes, King Abdullah University of Science and Technology
9:15 AM A Sandwich Smoother for Spatio-Temporal Functional Data
Joshua French, University of Colorado Denver; Piotr Kokoszka, Colorado State University
9:35 AM Accelerating Geostatistical Modeling with Mixed-Precision and Tile Low-Rank Algorithms on Large-Scale
Qinglei Cao, Innovative Computing Laboratory, University of Tennessee; Sameh Abdulah, KAUST; Rabab Alomairy, KAUST; Yu Pei, Innovative Computing Laboratory, University of Tennessee; Pratik Nag, King Abdullah University of Science and Technology; George Bosilca, Innovative Computing Laboratory, University of Tennessee; Jack Dongarra, Innovative Computing Laboratory, University of Tennessee; Marc Genton, KAUST; David Keyes, King Abdullah University of Science and Technology; Hatem Ltaief, KAUST; Ying Sun, KAUST
9:55 AM Distributed Inference for a Spatial Bayesian Network with Application to Natural Hazard Risk Assessment
Christopher Krapu, Oak Ridge National Laboratory; Nolan Hayes, Oak Ridge National Laboratory; Robert Stewart, Oak Ridge National Laboratory; Amy Rose, Oak Ridge National Laboratory; Alexandre Sorokine, Oak Ridge National Laboratory; Kuldeep Kurte, Oak Ridge National Laboratory
10:15 AM Floor Discussion
 
 

234 !
Tue, 8/9/2022, 8:30 AM - 10:20 AM CC-102A
New Challenges in Statistical Learning and Inference for Complex Data — Topic Contributed Papers
Section for Statistical Programmers and Analysts, Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, Section on Statistical Computing
Organizer(s): Ganggang Xu, University of Miami
Chair(s): Hou-Cheng Yang, U.S. Food and Drug Administration
8:35 AM Nonparametric Comparison of Time Series via Quantile Periodograms
Lei Jin, Texas A&M University - Corpus Christi
8:55 AM Jointly Modeling and Clustering Tensors in High Dimensions
Biao Cai, Yale University; Emma Jingfei Zhang, University of Miami; Will Wei Sun, Purdue University
9:15 AM On Deep Instrumental Variables Estimate
Ruiqi Liu, Texas Tech University; Zuofeng Shang, New Jersey Institute of Technology; Guang Cheng, Purdue University
9:35 AM Calibrating Multi-Dimensional Complex ODE from Noisy Data via Deep Neural Networks
Kexuan Li Li, Worcester Polytechnic Institute; Fangfang Wang, Worcester Polytechnic Institute; Ruiqi Liu, Texas Tech University; Fan Yang, Eli Lilly and Company; Zuofeng Shang, New Jersey Institute of Technology
9:55 AM Model-Assisted Uniformly Honest Inference for Optimal Treatment Regimes in High Dimension
Yunan Wu, University of Taxas at Dallas; Lan Wang, University of Miami; Haoda Fu, Eli Lilly and Company
10:15 AM Floor Discussion
 
 

235 !
Tue, 8/9/2022, 8:30 AM - 10:20 AM CC-144C
Recent Advancements in Nonparametric and Semiparametric Methodologies and Their Applications — Topic Contributed Papers
ENAR, Section on Nonparametric Statistics, Section on Statistical Learning and Data Science
Organizer(s): Trinetri Ghosh, University of Wisconsin-Madison
Chair(s): Shubhadeep Chakraborty, University of Washington
8:35 AM Multiply Robust Estimators in Longitudinal Studies with Missing Data Under Control-Based Imputation
Siyi Liu, North Carolina State University; Shu Yang, North Carolina State University; Yilong Zhang, Merck & Co., Inc.; Frank G. Liu, Merck & Co., Inc
8:55 AM Optimal Estimation of Average Treatment Effect on the Treated Under Endogeneous Treatment Assignment
Trinetri Ghosh, University of Wisconsin-Madison; Menggang Yu, University of Wisconsin-Madison; Jiwei Zhao, University of Wisconsin-Madison
9:15 AM Efficient Surrogate Assisted Inference for Patient-Reported Outcome with Complex Missing Mechanism
Muxuan Liang, University of Florida; Jaeyoung Park, University of Florida; Xiang Zhong, University of Florida; Yingqi Zhao, Fred Hutchinson Cancer Research Center
9:35 AM Causal Effect Estimation in Graphical Models with Unmeasured Confounders
Rohit Bhattacharya, Williams College; Razieh Nabi, Emory University; Ilya Shpitser, Johns Hopkins University
9:55 AM Estimating Longitudinal Causal Effects of Air Pollution Exposures Using Marginal Structural Models
Daniel Malinsky, Columbia University
10:15 AM Floor Discussion
 
 

252
Tue, 8/9/2022, 10:30 AM - 12:20 PM CC-203AB
Novel Methods in Curve Registration for Functional Data — Invited Papers
Biometrics Section, Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Julia Wrobel, Colorado School of Public Health
Chair(s): Jeff Goldsmith, Columbia University
10:35 AM Multimodal Bayesian Registration of Noisy Functions Using an Elastic Metric
James Derek Tucker, Sandia National Laboratories; Lyndsay Shand, Sandia National Laboratories; Kamaljit Derek Chowdhary, Sandia National Laboratories
11:00 AM Bayesian Multilevel Curve Registration
Zhenke Wu, University of Michigan, Ann Arbor; Julia Wrobel, Colorado School of Public Health; Jeff Goldsmith, Columbia University
11:25 AM Simultaneous Warping and Clustering of Functional Electrocardiogram
Wei Yang, University of Pennsylvania; Wensheng Guo, University of Pennsylvania
11:50 AM Registration for Incomplete Non-Gaussian Functional Data
Alexander Bauer, LMU Munich, Germany; Fabian Scheipl, LMU Munich, Germany; Helmut Küchenhoff, LMU Munich, Germany; Alice-Agnes Gabriel, LMU Munich, Germany
12:15 PM Floor Discussion
 
 

260 !
Tue, 8/9/2022, 10:30 AM - 12:20 PM CC-144C
Science-Integrated Statistical Learning — Invited Papers
Section on Physical and Engineering Sciences, Section on Statistical Learning and Data Science, Section on Bayesian Statistical Science
Organizer(s): Simon Mak, Duke University
Chair(s): Robert Gramacy, Virginia Tech
10:35 AM Multi-Stage, Multi-Fidelity Gaussian Process Modeling, with Applications to Emulation of Heavy-Ion Collisions
Simon Mak, Duke University
11:05 AM When Epidemic Models Meet Statistics: Understanding the Impact of Weather and Government Interventions on COVID-19 Outbreak
Chih-Li Sung, Michigan State University
11:35 AM APIK: Active Physics-Informed Kriging Model with Partial Differential Equations
C. F. Jeff Wu, Georgia Inst of Tech
12:05 PM Floor Discussion
 
 

270 * !
Tue, 8/9/2022, 10:30 AM - 12:20 PM CC-151B
Advanced Multivariate Time Series Modeling — Topic Contributed Papers
International Chinese Statistical Association, Business and Economic Statistics Section, Section on Statistical Learning and Data Science
Organizer(s): Mengyu Xu, University of Central Florida
Chair(s): Rui Xie, University of Central Florida
10:35 AM A Stratified Penalization Method for Semiparametric Variable Labeling of Multi-Output, Time-Varying Coefficient Models
Ting Zhang, University of Georgia; Weiliang Wang, Boston University; Yu Shao, Boston University
10:55 AM CP Factor Model for Dynamic Tensors
Yuefeng Han, Rutgers University; Rong Chen, Rutgers University; Cun-Hui Zhang, Rutgers University
11:15 AM Tail Adversarial Stability for Linear Processes
Shuyang Bai, University of Georgia; Ting Zhang, University of Georgia
11:35 AM Recursive Quantile Estimation: Non-Asymptotic Confidence Bounds
Likai Chen, Washington University in Saint Louis; Wei Biao Wu, University of Chicago; Georg Keilbar, University of Vienna
11:55 AM Minimax Nonparametric Multiple-Sample Test Under Smoothing
Xin Xing, Virginia Tech
12:15 PM Floor Discussion
 
 

272 *
Tue, 8/9/2022, 10:30 AM - 12:20 PM CC-204B
Approaches in Clustering for Analysis of Emerging Data Types — Topic Contributed Papers
Section on Statistical Learning and Data Science, Section on Statistics and Data Science Education, Section on Statistical Computing
Organizer(s): Tanzy Love, University of Rochester
Chair(s): Qiuyi Wu, University of Rochester
10:35 AM Transformation Mixture Modeling for Skewed Data Groups with Heavy Tails and Scatter
Xuwen Zhu, The University of Alabama; Volodymyr Melnykov, The University of Alabama; Yana Melnykov, The University of Alabama
10:55 AM Mixtures of Matrix Variate Contaminated Normal Distributions
Salvatore Daniele Tomarchio, University of Catania; Michael Gallaugher, Baylor University; Antonio Punzo, University of Catania; Paul David McNicholas, McMaster University
11:15 AM On Measuring Soft Agreement in Clustering
Jeffrey Andrews, University of British Columbia Okanagan; Ryan Browne, University of Waterloo; Chelsey Hvingelby, Concordia University
11:35 AM Uncovering Biological Heterogeneity via Clustering to Identify Gene Expression Networks and Patient Similarity Networks
Anjali Silva, University of Toronto
11:55 AM Discussant: Tanzy Love, University of Rochester
12:15 PM Floor Discussion
 
 

283
Tue, 8/9/2022, 10:30 AM - 12:20 PM CC-204A
Deep Learning Methods — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Bowei Xi, Purdue University
10:35 AM Interval Prediction with Deep Learning Models
Ghulam Qadir, Heidelberg Institute of Theoretical Studies; Tilmann Gneiting, Heidelberg Institute for Theoretical Studies
10:50 AM Classification of Longitudinal Data with Irregularly Spaced Intervals: A Comparison Between Posthoc Mixture Modeling of BLUPs from Mixed-Effects Model and Deep Clustering Methods
Md Jobayer Hossain, Nemours Children's Health; Ben Leiby, Thomas Jefferson University
11:05 AM A Framework to Explain Deep Learning Model Using Bayesian Rank Statistics and Layer-Wise Relevance Propagation
Yiqing Wang, Southern Methodist University; Xiaowei Zhan, The University of Texas Southwestern Medical Center; Guanghua Xiao, The University of Texas Southwestern Medical Center; Sen Yang, Southern Methodist University; Shidan Wang, University of Texas Southwestern Medical Center
11:20 AM How Much Data Do We Need? Predicting Deep Learning Model Performance and Training Data Sizes
Jelena Frtunikj, ArgoAI; Thomas Muehlenstaedt, ArgoAI; Rajat Mehta, ArgoAI
11:35 AM Neural Network Models for Clustered Data
Jing Wang, University of Utah
11:50 AM Manifold Alignment by Matching the Data Geometry and Label Information
Andres Felipe Duque, Utah state university; Dr. Kevin Moon, Utah State University
12:05 PM Self-Tuning, Nonlinear Parameter Estimation: A 21st-Century Approach to Statistics
Mary Gregg, National Institute of Standards and Technology; Lucas Koepke, National Institute of Standards and Technology; Angela Folz, National Institute of Standards and Technology/University of Colorado Boulder; Michael Frey, National Institute of Standards and Technology
 
 

Register 300
Tue, 8/9/2022, 12:30 PM - 1:50 PM CC-Ballroom Level South Prefunction
Section on Statistical Learning and Data Science P.M. Roundtable Discussion (Added Fee) — Roundtables PM Roundtable Discussion
Section on Statistical Learning and Data Science
TL13: Pros and Cons of Utilizing Stepped-Wedge Cluster Randomized Trial in Evaluation of Health Care Delivery Interventions
Madhu Mazumdar, Icahn School of Medicine at Mount Sinai
TL14: Statistics in Data Sonification
Jami Mulgrave, Making Music with AI Podcast
 
 

307 * !
Tue, 8/9/2022, 2:00 PM - 3:50 PM CC-102A
Deep Learning, Nonparametric Statistics, and Beyond — Invited Papers
Section on Nonparametric Statistics, Section on Statistical Learning and Data Science, Section on Statistical Computing, Caucus for Women in Statistics
Organizer(s): Yufeng Liu, University of North Carolina
Chair(s): Yufeng Liu, University of North Carolina
2:05 PM Kernel Estimation of Bivariate Time-Varying Coefficient Model for Longitudinal Data with Terminal Event
Bin Nan, University of California, Irvine; Yue Wang, University of California, Irvine; Jack Kalbfleisch, University of Michigan
2:30 PM Optimal Omni-Channel Individualized Treatment Rules Under Budget Constraints Using Deep Learning
Qi Xu, Unviersity of California Irvine; Haoda Fu, Eli Lilly and Company; Annie Qu, UC Irvine
2:55 PM Sleep Classification with Artificial Synthetic Imaging Data Using Convolutional Neural Networks
Peter Song, University of Michigan; Lan Shi, University of Michigan ; Marianthie Wank, University of Michigan; Yan Chen, University of Michigan ; Yibo Wang, University of Michigan; Emily Charlotte Hector, North Carolina State University
3:20 PM Population-Level Balance in Signed Networks
Weijing Tang, University of Michigan; Ji Zhu, University of Michigan
3:45 PM Floor Discussion
 
 

308 * !
Tue, 8/9/2022, 2:00 PM - 3:50 PM CC-150A
Highlights in Bayesian Analysis: Innovations in Bayesian Learning — Invited Papers
Section on Bayesian Statistical Science, International Society for Bayesian Analysis (ISBA), Section on Statistical Learning and Data Science
Organizer(s): Michele Guindani, University of California, Irvine
Chair(s): Veronica J Berrocal, University of California
2:05 PM Informative Bayesian Neural Network Priors for Weak Signals
Tianyu Cui, Aalto University; Aki Havulinna, Finnish Institute for Health and Welfare (THL); Pekka Marttinen, Aalto University; Samuel Kaski, Aalto University and University of Manchester
2:30 PM Fast and Accurate Estimation of Non-Nested Binomial Hierarchical Models Using Variational Inference
Max Goplerud, University of Pittsburgh
2:55 PM Bayesian Survival Tree Ensembles with Submodel Shrinkage
Antonio R. Linero, University of Texas at Austin; Piyali Basak, Merck Pharmaceuticals; Yinpu Li, Florida State University; Debajyoti Sinha, Florida State University
3:20 PM Bayesian Hierarchical Stacking: All Models Are Wrong, but Some Are Somewhat Useful
Yuling Yao, Flatiron Institute; Gregor Pirš, University of Ljubljana; Aki Vehtari, Aalto University; Andrew Gelman, Columbia University
3:45 PM Floor Discussion
 
 

309 !
Tue, 8/9/2022, 2:00 PM - 3:50 PM CC-152B
Statistical Reinforcement Learning — Invited Papers
Section on Statistical Computing, Section on Statistical Learning and Data Science, IMS
Organizer(s): Will Wei Sun, Purdue University
Chair(s): Will Wei Sun, Purdue University
2:05 PM Settling the Sample Complexity of Model-Based Offline Reinforcement Learning Presentation
Yuxin Chen, Princeton University; Yuxin Chen, University of Pennsylvania; Laixi Shi, Carnegie Mellon University; Yuejie Chi, Carnegie Mellon University; Yuting Wei , University of Pennsylvania
2:30 PM Demystifying (Deep) Reinforcement Learning with Optimism and Pessimism
Zhaoran Wang, Northwestern University
2:55 PM Doubly-Robust Estimation for an Optimal Intervention Strategy Under a Markov Decision Process
Owen Leete, Duke University; Eric Laber, Duke University
3:20 PM A Survival Reinforcement Learning Framework and Its Biomedical Applications
Hunyong Cho, University of North Carolina at Chapel Hill; Shannon T. Holloway, North Carolina State University; David J. Couper, University of North Carolina, Chapel Hill; Michael Kosorok, University of North Carolina at Chapel Hill
3:45 PM Floor Discussion
 
 

324 * !
Tue, 8/9/2022, 2:00 PM - 3:50 PM CC-101
Causal Inference and Machine Learning in Practice: Challenges Across Industry — Topic Contributed Papers
Section on Statistical Consulting, Committee on Applied Statisticians, Section on Statistical Learning and Data Science
Organizer(s): Qiaolin Chen, Tencent
Chair(s): Fei Guo, Tencent
2:05 PM CausalML: A Python Package for Uplift Modeling and Causal Inference Empowered by Machine Learning Methods
Zhenyu Zhao, Tencent; Totte Harinen, Toyota Research Institute
2:25 PM Measuring the Incremental Value of Uber for Business Products - an Instrumental Variable Approach
Zehao Hu, Uber Technologies, Inc.; Marie-Camille Achard, Uber Technologies, Inc.; John Kingsley, Uber Technologies, Inc.; Erjie Ang, Uber Technologies, Inc.
2:45 PM Empower Machine Learning Modeling and Development with Analytics
Pan Wu, Meta Platform, Inc.
3:05 PM Producer-Side Experiments for Online Recommender System
Shan Ba, LinkedIn
3:25 PM Discussant: Charles Wu, Tencent
3:45 PM Floor Discussion
 
 

334 *
Tue, 8/9/2022, 2:00 PM - 3:50 PM CC-140B
Network Data and Models — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Joshua Agterberg, Johns Hopkins University
2:05 PM Bootstrapping Network Data: Conditional and Marginal Approaches
Keith Levin, University of Wisconsin-Madison; Yichen Qin, University of Cincinnati; Youngser Park, Johns Hopkins University; Carey E Priebe, Johns Hopkins University
2:20 PM Investigating Excessive Activities in a Dynamic Network Using Time Series Models, Probabilistic Topic Modeling and Scan Statistics
Suchismita Goswami, George Mason University
2:35 PM Population-Level Balance in Signed Networks
Weijing Tang, University of Michigan; Ji Zhu, University of Michigan
2:50 PM Identifying the Latent Space Geometry of Network Models Through Analysis of Curvature
Shane Lubold, University of Washington; Arun Chandrasekhar, Stanford University; Tyler McCormick, University of Washington
3:05 PM Local, Asymptotically Normal Estimation of Network Curvature
Steven Wilkins-Reeves, University of Washington; Tyler McCormick, University of Washington
3:20 PM A Comparative Study of Machine Learning Methods on ASD Classification
Ramchandra Rimal, Middle Tennessee State University
3:35 PM Central Limit Theorems and Hypothesis Testing for Multiple Networks with Common Invariant Subspaces
Runbing Zheng, North Carolina State University; Minh Tang, North Carolina State University
 
 

341
Tue, 8/9/2022, 2:00 PM - 3:50 PM CC-Hall D
Contributed Poster Presentations: Section on Statistical Learning and Data Science — Contributed Poster Presentations
Section on Statistical Learning and Data Science
Chair(s): Gyuhyeong Goh, Kansas State University
01: Robust Two-Layer Partition Clustering of Sparse Multivariate Functional Data
Zhuo Qu, King Abdullah University of Science and Technology; Wenlin Dai, Renmin University of China; Marc Genton, KAUST
02: Exploring Efficacious FDA Approved Drugs and Their Subgroups in a Large Claims Database for Protection Against COVID-19
Joshua W Lambert, University of Cincinnati
03: Deep Learning Methods to Classify Cancer vs. Normal Samples Using TCR Data
Yujia Cai, Fred Hutchinson Cancer Research Center; Si Liu, Fred Hutchinson Cancer Research Center; Wei Sun, Fred Hutchinson Cancer Research Center
04: Applying Machine Learning to National Surveillance Data to Predict Excess Growth in Clusters of Tuberculosis Cases
Kathryn Winglee, Centers for Disease Control and Prevention; Sandy Althomsons, Centers for Disease Control and Prevention; Charles M Heilig, Centers for Disease Control and Prevention; Sarah Talarico, Centers for Disease Control and Prevention; Benjamin Silk, Centers for Disease Control and Prevention; Jonathan Wortham, Centers for Disease Control and Prevention; Andrew Hill, Centers for Disease Control and Prevention; Thomas Navin, Centers for Disease Control and Prevention
05: Sparse Bayesian Expectation-Maximization Algorithm for High-Dimensional Linear Mixed Models
Anja Zgodic, University of South Carolina; Alexander McLain, University of South Carolina
06: A Comparison of Deep Learning Methods for Identifying Anomalous Heartbeats in Electrocardiogram Data
Thomas Dunn, University of Central Oklahoma; Tyler Cook, University of Central Oklahoma; Emily Hendryx, University of Central Oklahoma
07: Machine Learning-Based Sentiment Analysis for Fuzzy Data to Predict Online Customer Satisfaction
Nicolò Biasetton, University of Padova; Luigi Salmaso, University of Padova; Marta Disegna, University of Padova; Luca Pegoraro, University of Padova; Riccardo Ceccato, University of Padova; Elena Barzizza, Università degli Studi di Padova; Rosa Arboretti, University of Padova
08: Bayesian Modeling Averaging for Dynamic Latent Space Models
Joshua Daniel Loyal, University of Illinois at Urbana-Champaign; Yuguo Chen, University of Illinois at Urbana-Champaign
09: Combining Augmented Design and Statistical Learning Approaches to Address Multicollinearity in Small Data
Min Chen, ExxonMobil ; Christine A Zielinski, ExxonMobil; Charles L Baker, ExxonMobil
10: Feature Engineering Approach for Learning and Predicting Process Units with Two Timestamps
Yoann Valero, Université de Technologie de Troyes / Livejourney; Frédéric Bertrand, Troyes Technology University; Myriam Maumy, Troyes Technology University
11: Random Forest-Based Diffusion Information Geometry for Supervised Visualization and Data Exploration
Jake Slater Rhodes, Utah State University; Dr. Kevin Moon, Utah State University; Adele Cutler, Utah State University; Guy Wolf, Université de Montréal
 
 

Register 356
Wed, 8/10/2022, 7:00 AM - 8:15 AM CC-Ballroom Level South Prefunction
Section on Statistical Learning and Data Science A.M. Roundtable Discussion (Added Fee) — Roundtables AM Roundtable Discussion
Section on Statistical Learning and Data Science
WL02: On the Trade-Offs Between Statistical and Computational Efficiencies
Aritra Guha, Data Science & AI Research, AT&T Chief Data Office; Arkaprava Roy, University of Florida
 
 

361 * !
Wed, 8/10/2022, 8:30 AM - 10:20 AM CC-202B
Using Statistical Foundations to Demonstrate Effectiveness of ML/AI Algorithms for Clinical Utility — Invited Papers
Biopharmaceutical Section, Section on Statistical Learning and Data Science, Health Policy Statistics Section
Organizer(s): Charmaine Demanuele, Pfizer; Stephen J Ruberg, Analytix Thinking, LLC
Chair(s): Sandeep M Menon, Pfizer, Inc.
8:35 AM Deep Learning for Image Analysis - Initial Development of Digital Diagnostic/Prognostic Algorithms: Analogies and Lessons Learned from Drug Development
Chong Duan, Pfizer
9:00 AM Optimizing Digital Diagnostic/Prognostics Algorithms: A New Framework and Approach Beyond Area Under the Receiver Operating Characteristic Curve Presentation
Stephen J Ruberg, Analytix Thinking, LLC
9:25 AM Clinical Trial Designs to Estimate the Effect of a Digital Diagnostic/Prognostic Algorithm in Clinical Practice: Application of Cluster-Randomized Designs
Liz Turner, Duke University
9:50 AM Discussant: John Quackenbush, Harvard T.H. Chan School of Public Health
10:15 AM Floor Discussion
 
 

364 * !
Wed, 8/10/2022, 8:30 AM - 10:20 AM CC-151A
Modern Nonparametric Methods, with Applications in Complex Biomedical Data — Invited Papers
Section on Nonparametric Statistics, International Chinese Statistical Association, Section on Statistical Learning and Data Science
Organizer(s): Tracy Ke, Harvard University
Chair(s): Tracy Ke, Harvard University
8:35 AM Distributed Nonparametric Function Estimation: Optimal Rate of Convergence and Cost of Adaptation
Tony Cai, University of Pennsylvania
9:00 AM Fast, Model-Agnostic Confidence Intervals for Feature Importance
Genevera Allen, Rice University; Lili Zheng, Rice University; Luqin Gan, Rice University
9:25 AM Transfer Learning Under High-Dimensional Generalized Linear Models
Yang Feng, New York University; Ye Tian, Columbia University
9:50 AM Statistical Inference for Linear Mediation Models with High-Dimensional Mediators
Runze Li, Penn State University; Xu Guo, Beijing Normal University; Jingyuan Liu, Xiamen University; Mudong Zeng, Penn State University
10:15 AM Floor Discussion
 
 

366 *
Wed, 8/10/2022, 8:30 AM - 10:20 AM CC-204B
New Advances in Integrative Learning for Multi-Group and Multi-View Data — Invited Papers
ENAR, Section on Statistical Learning and Data Science, International Indian Statistical Association
Organizer(s): Arkaprava Roy, University of Florida
Chair(s): Arkaprava Roy, University of Florida
8:35 AM Single-Cell Multi-Omic Integration Using Sparse Multi-View Non-Negative Matrix Factorization with Graph Regularization
Dorothy DeMore Ellis, University of Florida; Susmita Datta, University of Florida; Arkaprava Roy, University of Florida
9:00 AM Integrative Regression and Factorization of Multi-Omics Multi-Cohort Data
Eric F Lock, University of Minnesota
9:25 AM Sparse and Integrative Principal Component Analysis for Multi-View Data
Luo Xiao, North Carolina State University; Lin Xiao, North Carolina State University
9:50 AM Statistical Approaches for Integrative Learning for Neuroimaging Data
Suprateek Kundu, The University of Texas at MD Anderson Cancer Center
10:15 AM Floor Discussion
 
 

368 * !
Wed, 8/10/2022, 8:30 AM - 10:20 AM CC-150A
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
8:35 AM Nonparametric Inference Under a Birth-Death Dynamic Network Model
Soumendra Lahiri, Washington University in St Louis
9:00 AM Identifying the Latent Space Geometry of Network Models Through Analysis of Curvature
Shane Lubold, University of Washington; Arun Chandrasekhar, Stanford University; Tyler McCormick, University of Washington
9:25 AM Using Maximum Entry-Wise Deviation to Test the Goodness of Fit for Stochastic Block Models
Emma Jingfei Zhang, University of Miami
9:50 AM Informative Core Identification in Complex Networks
Ruizhong Miao, University of Virginia; Tianxi Li, University of Virginia
10:15 AM Floor Discussion
 
 

369 * !
Wed, 8/10/2022, 8:30 AM - 10:20 AM CC-150B
Analysis of Random Objects — Invited Papers
Section on Statistical Computing, Section on Nonparametric Statistics, Section on Statistical Learning and Data Science, IMS
Organizer(s): Lingzhou Xue, Penn State University and National Institute of Statistical Sciences
Chair(s): Qi Zhang, Penn State University
8:35 AM Partially Global Fréchet Regression
Danielle C. Tucker, University of Illinois at Chicago; Yichao C. Wu, University of Illinois at Chicago
8:55 AM Single Index Fréchet Regression
Hans-Georg Müller, University of California, Davis ; Satarupa Bhattacharjee, University of California, Davis
9:15 AM Dimension Reduction and Data Visualization for Fréchet Regression
Qi Zhang, Penn State University; Lingzhou Xue, Penn State University and National Institute of Statistical Sciences; Bing Li, Penn State University
9:35 AM Functional Models for Time-Varying Random Objects and Dynamic Networks
Paromita Dubey, University of Southern California; Hans-Georg Müller, University of California, Davis
9:55 AM Nonlinear Sufficient Dimension Reduction for Distributional Data
Qi Zhang, Penn State University; Lingzhou Xue, Penn State University and National Institute of Statistical Sciences; Bing Li, Penn State University
10:15 AM Floor Discussion
 
 

373 * !
Wed, 8/10/2022, 8:30 AM - 10:20 AM CC-159AB
Recent Advances in Complex and High-Dimensional Data — Topic Contributed Papers
IMS, Section on Nonparametric Statistics, Section on Statistical Learning and Data Science
Organizer(s): Shuheng Zhou, University of California, Riverside
Chair(s): Min Xu, Rutgers University
8:35 AM Differentially Private Inference via Noisy Optimization
Po-Ling Loh, University of Cambridge; Marco Avella Medina, Columbia University; Casey Bradshaw, Columbia University
8:55 AM High-Dimensional Changepoint Estimation with Heterogeneous Missingness
Tengyao Wang, London School of Economics
9:15 AM Guaranteed Functional Tensor Singular Value Decomposition
Anru Zhang, Duke University; Rungang Han, Duke University; Pixu Shi, Duke University
9:35 AM Leave-One-Out Singular Subspace Perturbation Analysis for Spectral Clustering
Harrison Zhou, Yale University
9:55 AM Concentration of Measure Bounds for Matrix-Variate Data with Missing Values
Shuheng Zhou, University of California, Riverside
10:15 AM Floor Discussion
 
 

381 !
Wed, 8/10/2022, 8:30 AM - 10:20 AM CC-144B
Recent Advances in High-Dimensional Estimation and Inference Methods — Topic Contributed Papers
Section on Statistical Learning and Data Science, Biometrics Section, ENAR
Organizer(s): Emily Charlotte Hector, North Carolina State University; Lu Xia, University of Washington
Chair(s): Emily Charlotte Hector, North Carolina State University
8:35 AM High-Dimensional Change-Point Detection Using Generalized Homogeneity Metrics
Shubhadeep Chakraborty, University of Washington
8:55 AM Theoretical Foundations of T-SNE for Visualizing High-Dimensional Clustered Data
Rong Ma, Stanford University; Tony Cai, University of Pennsylvania
9:15 AM Two-Sample Hypothesis Testing for Multiple-Network Data
Yinqiu He, Columbia University
9:35 AM Statistical Inference for Correlated Data with High-Dimensional Covariates
Lu Xia, University of Washington; Ali Shojaie, University of Washington
9:55 AM Debiased Inference on Heterogeneous Quantile Treatment Effects with Regression Rank-Scores
Alexander Giessing, University of Washington; Jingshen Wang, UC Berkeley
10:15 AM Floor Discussion
 
 

393
Wed, 8/10/2022, 8:30 AM - 10:20 AM CC-144A
NLP and Text Analysis — Contributed Papers
Section on Statistical Learning and Data Science, Text Analysis Interest Group
Chair(s): Aramayis Dallakyan, StataCorp
8:35 AM A Text Mining Approach to Determine Correlations Between the Spanish Flu and COVID-19
Billie Anderson, University of Missouri Kansas City; Majid Bani-Yaghoub, University of Missouri Kansas City; Vagmi Kantheti, University of Missouri Kansas City; Scott Curtis, University of Missouri Kansas City
8:50 AM Model Editing in Language Models Using Influence Functions
Jillian Fisher, University of Washington; Liwei Jiang, University of Washington, Allen Institute for Artificial Intelligence; Krishna Pillutla, University of Washington; Swabha Swayamdipta, Allen Institute for Artificial Intelligence; Yejin Choi, University of Washington,Allen Institute for Artificial Intelligence; Zaid Harchaoui, University of Washington
9:05 AM Words and Phrases Associated with Absconding Supervision Among Probationers in Tarrant County Texas Using Natural Language Processing
Jialiang Liu, Temple University; Sumihiro Suzuki, Rush University
9:20 AM Industry Self-Classification in the Economic Census Presentation
Brian Dumbacher, U.S. Census Bureau; Daniel Whitehead, U.S. Census Bureau
9:35 AM Exploratory Factor Analysis of Data with Incomplete Records
Fan Dai, Michigan Technological University; Karin Dorman, Iowa State University; Somak Dutta, Iowa State University; Ranjan Maitra, Iowa State University
9:50 AM Application of Medical Concept Embeddings and Evaluation to ICD-10 Diagnosis Codes
Meghan Beckowski, Deloitte Consulting, LLC; Nader Karamzadeh, Deloitte Consulting, LLC
10:05 AM Comparing Handwriting in Questioned Documents
Alan Julian Izenman, Temple University
 
 

397 !
Wed, 8/10/2022, 10:30 AM - 12:20 PM CC-143C
Modern Statistical Learning Methods for Dynamic Models — Invited Papers
Business and Economic Statistics Section, Section on Statistical Learning and Data Science
Organizer(s): Yao Zheng, University of Connecticut
Chair(s): Yao Zheng, University of Connecticut
10:35 AM A General Modeling Framework for Network Autoregressive Processes and Their Applications
George Michailidis, U Florida
11:00 AM Simultaneous Inference for Time-Varying Models
Wei Biao Wu, University of Chicago; Sayar Karmakar, University of Florida
11:25 AM Regularized Estimation of Impulse Response Function
David Matteson, Cornell University; Ines Wilms, Maastricht University; Sumanta Basu, Cornell University; Xiaojie Mao, Cornell University
11:50 AM Community Network Auto-Regression for High-Dimensional Time Series
Elynn Chen, New York University; Xuening Zhu, Fudan University; Jianqing Fan, Princeton University
12:15 PM Floor Discussion
 
 

398 * !
Wed, 8/10/2022, 10:30 AM - 12:20 PM CC-202B
Recent Advances in Streaming Data Analytics — Invited Papers
International Chinese Statistical Association, WNAR, Section on Statistical Learning and Data Science
Organizer(s): Lan Luo, The University of Iowa
Chair(s): Peter Song, University of Michigan
10:35 AM Multivariate Online Regression Analysis with Heterogeneous Streaming Data
Lan Luo, The University of Iowa; Peter Song, University of Michigan
11:00 AM Dynamic Statistical Inference in Massive Datastreams
Lilun Du, HKUST; Changliang Zou, Nankai University; Zhenke Wu, University of Michigan, Ann Arbor; Jingshen Wang, UC Berkeley
11:25 AM Assessing Time-Varying Causal Effect Moderation in the Presence of Cluster-Level Treatment Effect Heterogeneity
Walter Dempsey, University of Michigan; Zhenke Wu, University of Michigan, Ann Arbor; Jieru Shi, University of Michigan
11:50 AM Statistical Inference for Online Decision-Making via Stochastic Gradient Descent
Rui Song, North Carolina State University
12:15 PM Floor Discussion
 
 

403 * !
Wed, 8/10/2022, 10:30 AM - 12:20 PM CC-151A
Research Advances at the Interface of Uncertainty Quantification and Machine Learning for High-Consequence Problems — Invited Papers
Section on Statistics in Defense and National Security, Section on Statistical Learning and Data Science, IEEE Computer Society
Organizer(s): Ahmad Rushdi, Stanford University
Chair(s): Erin Acquesta, Sandia National Laboratories
10:35 AM Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Models
Yu Wang, Notre Dame University; Daniele Schiavazzi, University of Notre Dame; Fang Liu, Univerisity of Notre Dame
10:55 AM Assessing the Quality of Uncertainty Estimates in Deep Learning
Jason Adams, Sandia National Laboratories
11:15 AM Extreme Learning Machines for Variance-Based Global Sensitivity Analysis
John Darges, North Carolina State University; Alen Alexanderian, North Carolina State University; Pierre Gremaud, North Carolina State University
11:35 AM Efficient Variational Approach to Sparse BNN for Model Compression
Diptarka Saha, University of Illinois, Urbana-Champaign; Feng Liang, University of Illinois, Urbana-Champaign ; Zihe Liu, University of Illinois, Urbana-Champaign
11:55 AM Discussant: Daniel Ries, Sandia National Labs
12:15 AM Floor Discussion
 
 

408 * !
Wed, 8/10/2022, 10:30 AM - 12:20 PM CC-144C
Recent Advances in Statistical Machine Learning — Topic Contributed Papers
Section for Statistical Programmers and Analysts, Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, International Chinese Statistical Association
Organizer(s): Guan Yu, University of Pittsburgh
Chair(s): Gen Li, University of Michigan
10:35 AM Locally Weighted Nearest Neighbor Classifier
Guan Yu, University of Pittsburgh; Xingye Qiao, Binghamton University
10:55 AM Contextual Dynamic Pricing with Unknown Noise
Yiyun Luo, UNC; Will Wei Sun, Purdue University; Yufeng Liu, University of North Carolina
11:15 AM Optimal and Safe Estimation for High-Dimensional, Semi-Supervised Learning
Yang Ning, Cornell University
11:35 AM Learning Acceptance Regions for Many Classes with Anomaly Detection
Zhou Wang, Binghamton University; Xingye Qiao, Binghamton University
11:55 AM Floor Discussion
 
 

417 !
Wed, 8/10/2022, 10:30 AM - 12:20 PM CC-204A
Statistical Methods for Discovering Latent Structures in High-Dimensional and Complex Data — Topic Contributed Papers
Section on Bayesian Statistical Science, International Society for Bayesian Analysis (ISBA), Section on Statistical Learning and Data Science
Organizer(s): Zehang Richard Li, UCSC
Chair(s): Zhenke Wu, University of Michigan, Ann Arbor
10:35 AM Bayesian Pyramids: Identifiable Multilayer Discrete Latent Structure Models for Discrete Data
Yuqi Gu, Columbia University; David Dunson, Duke University
10:55 AM Statistical Neuroscience in the Single Trial Limit
Scott Linderman, Stanford University
11:15 AM Latent Class Models for Prevalence Estimation of Emerging Diseases Using Verbal Autopsy Data
Zehang Richard Li, UCSC
11:35 AM Exploration of Latent Structure in Test Review and Revision Log Data
Susu Zhang, University of Illinois at Urbana-Champaign; Anqi Li, University of Illinois at Urbana-Champaign; Shiyu Wang, University of Georgia
11:55 AM Discussant: Elena Erosheva, University of Washington
12:15 PM Floor Discussion
 
 

427 * !
Wed, 8/10/2022, 10:30 AM - 12:20 PM CC-140B
Intelligent Systems and Decision Support — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Nathaniel O'Connell, Wake Forest School of Medicine
10:35 AM The Better Simple Network Scale-Up Model (NSUM) Estimator Is the Average of Ratios, Not the Ratio of Averages
Jessica P Kunke, University of Washington; Tyler McCormick, University of Washington; Ian Laga, Penn State University; Xiaoyue Niu, The Pennsylvania State University
10:50 AM Optimizing Fraud Detection Using Machine Learning Techniques
Jennifer Renee Leach, Rowan University; Umashanger Thayasivam, Rowan University
11:05 AM A Statistical Learning Oracle to Support Decisions in Inference
Lucas Koepke, National Institute of Standards and Technology; Mary Gregg, National Institute of Standards and Technology; Michael Frey, National Institute of Standards and Technology
11:20 AM Statistical Learning Applications in Inverse Flight Dynamics
Cody Nichols, Federal Aviation Administration; Tyler Cook, University of Central Oklahoma
11:35 AM Policy Learning in Procurement
George Vassos, A.P. Moller Maersk A/S
11:50 AM Efficient Algorithms for Learning to Control Bandits with Unobserved Contexts
Mohamad Kazem Shirani Faradonbeh, University of Georgia; Hongju Park Park, University of Georgia
12:05 PM Letting Go with Eyes Wide Open: Automatic Model Retraining in Production in Industry
Sergiy Nesterko, Fidelity Investments
 
 

Register 450
Wed, 8/10/2022, 12:30 PM - 1:50 PM CC-Ballroom Level South Prefunction
Section on Statistical Learning and Data Science P.M. Roundtable Discussion (Added Fee) — Roundtables PM Roundtable Discussion
Section on Statistical Learning and Data Science
WL12: Data Science as a Curriculum
Julia Wrobel, Colorado School of Public Health; Elizabeth Sweeney, University of Pennsylvania
WL13: WITHDRAWN: Statistical Approaches to Algorithmic Fairness
Yuekai Sun, University of Michigan
 
 

455 * !
Wed, 8/10/2022, 2:00 PM - 3:50 PM CC-151A
Learning Under Nonstationarity — Invited Papers
Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, Section on Statistical Computing
Organizer(s): Piotr Fryzlewicz, London School of Economics
Chair(s): David Matteson, Cornell University
2:05 PM Nonstationary Reinforcement Learning Without Prior Knowledge: An Optimal Black-Box Approach Presentation
Chen-Yu Wei, University of Southern California; Haipeng Luo, University of Southern California
2:25 PM Testing Nonstationary and Policy Optimization in Reinforcement Learning
Chengchun Shi, London School of Economics and Political Science; Mengbing Li, University of Michigan; Zhenke Wu, University of Michigan, Ann Arbor; Piotr Fryzlewicz, London School of Economics
2:45 PM A Similarity Measure for Second-Order Properties of Nonstationary Functional Time Series with Applications to Clustering and Testing
Anne van Delft, Columbia University; Holger Dette, Ruhr University Bochum
3:05 PM Bandit Learning with Endogenous Drift
assaf zeevi, columbia university
3:25 PM Discussant: Piotr Fryzlewicz, London School of Economics
3:45 PM Floor Discussion
 
 

492 * !
Thu, 8/11/2022, 8:30 AM - 10:20 AM CC-150B
Why Probability, Then Statistics When It Can Be Probability, for Statistics? New Approaches for Teaching Mathematical Statistics — Invited Papers
Section on Statistics and Data Science Education, Section on Statistical Learning and Data Science, International Association for Statistical Education
Organizer(s): Peter E. Freeman, Carnegie Mellon University
Chair(s): Nicholas Joseph Seewald, Johns Hopkins Bloomberg School of Public Health
8:35 AM Utilizing Spiral Learning to Enhance Conceptual Retention in Mathematical Statistics Presentation
Peter E. Freeman, Carnegie Mellon University
8:55 AM Three-Course Dinner or Thanksgiving Feast? Putting the Pieces Together in a Modern Math/Stat Sequence
Randall Pruim, Calvin University
9:15 AM Teaching Probabability Theory in the Inverted Style
Jonathan Wells, Reed College
9:35 AM Calcu Less - Compute More: Rethinking Traditional Pathways for Increasing Access to Data Science
Ayona Chatterjee, Cal State Univ East Bay
9:55 AM Cutting Through the Theory: Emphasizing and Assessing Conceptual Understanding in Mathematical Statistics Presentation
Erin Blankenship, University of Nebraska-Lincoln; Jennifer Green, Michigan State University
10:15 AM Floor Discussion
 
 

496 * !
Thu, 8/11/2022, 8:30 AM - 10:20 AM CC-207A
Machine Learning Methods for Single-Cell Analysis — Invited Papers
WNAR, Section on Statistical Learning and Data Science, Section on Statistics in Genomics and Genetics
Organizer(s): Lingling An, University of Arizona
Chair(s): Joel Parker, Mel and Enid Zuckerman College of Public Health
8:35 AM Discovering Novel Cell Types Across Heterogeneous Single-Cell Experiments
Jure Leskovec, Stanford University
9:00 AM Statistical Machine Learning Models for Large-Scale Spatial Omics
James Zou, Stanford University
9:25 AM Benchmarking Computational Integration Methods for Spatial Transcriptomics Data
Lana Garmire, University of Michigan
9:50 AM Time-Course Single-Cell Multimodal Analysis and Trajectory Inference Using Deep Generative Models
Qiao Liu, Stanford University; Xi Chen, Stanford University; Jingxue Xin, Stanford University; Wanwen Zeng, Stanford University; Wing Hung Wong, Stanford University
10:15 AM Floor Discussion
 
 

502
Thu, 8/11/2022, 8:30 AM - 10:20 AM CC-144B
Recent Developments in Modeling of Multivariate Functional Data — Invited Papers
Section on Statistical Learning and Data Science, Business and Economic Statistics Section, ENAR
Organizer(s): Mladen Kolar, The University of Chicago
Chair(s): Mladen Kolar, The University of Chicago
8:35 AM Functional Graphical Models
Lexin Li, University of California, Berkeley
8:55 AM Bayesian Functional Graphical Models
Lin Zhang, University of Minnesota; Veera Baladandayuthapani, University of Michigan; Quinton Neville, University of Minnesota; Karina Quevedo, University of Minnesota; Jeffrey Morris, University of Pennsylvania
9:15 AM Quantifying Deviations from Separability in Space-Time Functional Processes
Tim Kutta, Ruhr-University Bochum; Holger Dette, Ruhr University Bochum; Gauthier Dierickx, Ruhr-University Bochum
9:35 AM Adaptive Functional Thresholding for Sparse Covariance Function Estimation in High Dimensions Presentation
Qin Fang, London School of Economics; Shaojun Guo, Renmin University of China; Xinghao Qiao, London School of Economics
9:55 AM Discussant: Samuel Wang, Cornell University
10:15 AM Floor Discussion
 
 

504 !
Thu, 8/11/2022, 8:30 AM - 10:20 AM CC-144C
Computational Challenges in Modern Statistical Inference — Invited Papers
IMS, Section on Statistical Learning and Data Science, IEEE Computer Society
Organizer(s): Anru Zhang, Duke University
Chair(s): Anru Zhang, Duke University
8:35 AM Testing Network Correlation Efficiently via Counting Trees Presentation
Cheng Mao, Georgia Institute of Technology; Yihong Wu, Yale University; Jiaming Xu, Duke University; Sophie H. Yu, Duke University
9:00 AM High-Dimensional Discriminant Analysis on Latent Variables
Marten Wegkamp, Cornell University; Xin Bing, University of Toronto; Florentina Bunea, Cornell University
9:25 AM Statistics Meets Optimization: Sharp Time-Data Tradeoffs for Iterative Algorithms in Random Nonconvex Programs
Ashwin Pananjady, Georgia Tech
9:50 AM Low-Rank Matrix Estimation with Groupwise Heteroskedasticity
Galen Reeves, Duke University
10:15 AM Floor Discussion
 
 

509 * !
Thu, 8/11/2022, 8:30 AM - 10:20 AM CC-143A
Recent Advances in High-Dimensional Time Series Analysis — Topic Contributed Papers
Business and Economic Statistics Section, IMS, Section on Statistical Learning and Data Science
Organizer(s): S. Yaser Samadi, Southern Illinois University Carbondale
Chair(s): Ali Shojaie, University of Washington
8:35 AM Fast, Optimal, and Targeted Predictions Using Parametrized Decision Analysis
Daniel Kowal, Rice University
8:55 AM Dimension Reduction for Vector Autoregressive Models
S. Yaser Samadi, Southern Illinois University Carbondale; H. M. Wiranthe Bandara Herath, Southern Illinois University Carbondale
9:15 AM Inference for Location of Change Points in High-Dimensional Non-Stationary Vector Auto-Regressive Models
Abolfazl Safikhani, University of Florida; Abhishek Kaul, Washington State University; Yue Bai, University of Florida
9:35 AM Divide-and-Conquer: A Distributed Hierarchical Factor Approach to Modeling Large-Scale Time Series Data
Ruey Tsay, University of Chicgao; Zhaoxing Gao, Zhejiang University
9:55 AM Floor Discussion
 
 

514 * !
Thu, 8/11/2022, 8:30 AM - 10:20 AM CC-101
Advancements in Multi-Omics Integration Techniques — Topic Contributed Papers
Section on Statistics in Epidemiology, Section on Statistical Learning and Data Science, Section on Statistics in Genomics and Genetics
Organizer(s): Ali Rahnavard, George Washington University
Chair(s): Himel Mallick, Merck Research Laboratories
8:35 AM Multiomics Analysis of Normal and Pathological Pregnancies
Nima Aghaeepour, Stanford University
8:55 AM Identifying Associations Between Genomic and Clinical Features of SARS-CoV-2 in the New Jersey Area During the Early Stages of the COVID-19 Pandemic
Tyson Dawson, The George Washington University; Ali Rahnavard, George Washington University
9:15 AM IntegratedLearner: An Integrated Bayesian Framework for Multi-Omics Prediction and Classification
Anupreet Porwal, University of Washington; Himel Mallick, Merck Research Laboratories; Erina Paul, Merck & Co., Inc.; Satabdi Saha, Michigan State University; Vladimir Svetnik, Merck Research Labs
9:35 AM Pathway Enrichment Analysis for Functional Integration of Multi-Omics in Roux-En-Y Gastric Bypass
Ali Rahnavard, George Washington University; Nima Saeidi, Harvard Medical School
9:55 AM Floor Discussion
 
 

522
Thu, 8/11/2022, 8:30 AM - 10:20 AM CC-144A
Life Science Applications of Data Science — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Kellie Archer, The Ohio State University
8:35 AM Regression Trees and Ensembles for Cumulative Incidence Functions
Youngjoo Cho, Konkuk University; Annette Molinaro, University of California San Francisco; Chen Hu, Johns Hopkins University ; Robert L Strawderman, University of Rochester
8:50 AM Long-Term Survival and Second Malignant Tumor Prediction in Pediatric, Adolescent, and Young Adult Cancer Survivors Using Random Survival Forests: A SEER Analysis
Ivy Zhang, Yale University; Jun Deng, Yale University; Gregory Hart, Bill & Melinda Gates Foundation; Bo Qin, Dartmouth College
9:05 AM Assessing Time-Varying Causal Effect Moderation in the Presence of Cluster-Level Treatment Effect Heterogeneity
Jieru Shi, University of Michigan; Zhenke Wu, University of Michigan, Ann Arbor; Walter Dempsey, University of Michigan
9:20 AM Evaluating Model Diagnostics Tools for Non-Genetic Associations in Large Scale Data Sets
Christophe Toukam Tchakoute, Stanford University; Stella Aslibekyan, 23andMe, INC; Teresa Filshtein Sonmez, 23andMe, INC; Robert Gentleman, Center for Computational Biomedicine, Harvard Medical School
9:35 AM Significance Tests Based on Sieve Quasi-Likelihood Ratio Test Using Neural Networks with Application to Genetic Association Studies
Xiaoxi Shen, Texas State University; Chang Jiang , University of Florida; LYUDMILA SAKHANENKO, Michigan State University; Qing Lu, University of Florida
9:50 AM Floor Discussion
 
 

532 * !
Thu, 8/11/2022, 10:30 AM - 12:20 PM CC-102A
Statistical Innovations Driven by the COVID-19 Pandemic — Invited Papers
Committee on Applied Statisticians, Section on Statistical Learning and Data Science, Biopharmaceutical Section
Organizer(s): Sameera Wijayawardana, Eli Lilly and Company
Chair(s): Sameera Wijayawardana, Eli Lilly and Company
10:35 AM Statistical Innovations Driven by the COVID-19 Pandemic
Juan Miguel Lavista Ferres, Microsoft
11:00 AM Developing COVID-19 Treatments During the Pandemic: Unique Challenges, Opportunities for Innovation
Lei Shen, Eli Lilly and Company
11:25 AM Excess Mortality Associated with COVID-19: January 2020 Through September 2021, United States
Lauren Rossen, National Center for Health Statistics, CDC
11:50 AM Assessing Transmissibility and Associated Risk Modifiers of Emerging Infectious Diseases from Contact Tracing Data
Yang Yang, University of Florida; Mingjin Liu, University of Florida; Neda Jalali, University of Florida
12:15 PM Floor Discussion
 
 

542 * !
Thu, 8/11/2022, 10:30 AM - 12:20 PM CC-204B
Advances in Topological and Geometric Data Analysis — Topic Contributed Papers
IMS, Section on Statistics in Imaging, Section on Statistical Learning and Data Science
Organizer(s): Yen-Chi Chen, University of Washington; Justin Strait, Los Alamos National Laboratory
Chair(s): Yen-Chi Chen, University of Washington
10:35 AM Methods for Testing Distributional Assumptions for Object Data
Leif Ellingson, Texas Tech University; Dong Xu, Suzhou University
10:55 AM Random Persistence Diagram Generation and Materials
Vasileios Maroulas, University of Tennesse, Knoxville; Theodore Papamarkou, The University of Manchester; Farzana Nasrin, University of Hawaii; Minh Quang Le, University of Tennesse, Knoxville
11:15 AM Density-Based Classification in Diabetic Retinopathy Through Thickness of Retinal Layers from Optical Coherence Tomography
Shariq Mohammed, Boston University; Tingyang Li, University of Michigan; Xing Chen, University of Michigan; Elisa Warner, University of Michigan; Anand Shankar, University of Michigan; Maria Fernanda Abalem, University of Michigan; Thiran Jayasundera, University of Michigan; Thomas Gardner, University of Michigan; Arvind Rao, University of Michigan
11:35 AM Featurization of Topological Data Analysis Using Persistence Landscape and Circular Coordinates
Jisu Kim, Inria; Kwangho Kim, Harvard; Manzil Zaheer, Google Research; Joon Sik Kim, Carnegie Mellon University; Frédéric Chazal, Inria; Larry Wasserman, Carnegie Mellon University; Hengrui Luo, Lawrence Berkeley National Laboratory; Alice Patania, Indiana University; Mikael Vejdemo-Johansson, CUNY
11:55 AM Discussant: Justin Strait, Los Alamos National Laboratory
12:15 PM Floor Discussion
 
 

543 * !
Thu, 8/11/2022, 10:30 AM - 12:20 PM CC-140B
CANCELED: Racial Equity in Data Science Education — Topic Contributed Papers
Section on Statistics and Data Science Education, Section on Statistical Learning and Data Science
 
 

545 !
Thu, 8/11/2022, 10:30 AM - 12:20 PM CC-154A
Statistical Advances in Learning Large-Scale Networks from Massive Data Sets — Topic Contributed Papers
Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, IMS
Organizer(s): Lili Zheng, Rice University
Chair(s): Genevera Allen, Rice University
10:35 AM Learning Point Process Network Models with Timing Uncertainty
Yao Xie, Georgia Institute of Technology; Xiuyuan Cheng, Duke University; Tingnan Gong, Georgia Institute of Technology
10:55 AM Statistical Inference for Networks of High-Dimensional Point Processes
Mladen Kolar, The University of Chicago ; Xu Wang, University of Washington; Ali Shojaie, University of Washington
11:15 AM Learning Gaussian Graphical Models with Differing Pairwise Sample Sizes Presentation
Lili Zheng, Rice University; Genevera Allen, Rice University
11:35 AM Learning Latent Causal Graphs via Mixture Oracles
Pradeep Ravikumar, Carnegie Mellon University
11:55 AM Frequency-Domain Graphical Modeling of Large-Scale Time Series
Sumanta Basu, Cornell University; Navonil Deb, Cornell University
12:15 PM Floor Discussion
 
 

560
Thu, 8/11/2022, 10:30 AM - 12:20 PM CC-144A
Latent Space Modeling and Dimensionality Reduction — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Jiae Kim, Indiana University
10:35 AM Generalizable Manifold Learning for Dimensional Reduction
Jungeum Kim, Purdue University; Xiao Wang, Purdue University
10:50 AM Inference for Canonical Directions in Canonical Correlation Analysis
Daniel Kessler, University of Michigan; Elizaveta Levina, University of Michigan
11:05 AM Direction Penalized Principal Component Analysis
Youhong Lee, University of California, Santa Barbara; Alex Shkolnik, University of California, Santa Barbara
11:20 AM Projection Expectile Regression for Sufficient Dimension Reduction
Abdul-Nasah Soale, University of Notre Dame
11:35 AM Entrywise Estimation of Singular Vectors of Low-Rank Matrices with Heteroskedasticity and Dependence
Joshua Agterberg, Johns Hopkins University; Zachary Lubberts, Johns Hopkins University; Carey E Priebe, Johns Hopkins University
11:50 AM A Quasi-Likelihood Approach to Latent Space Modeling for Compositional Data
Lun Li, The Ohio State University; Yoonkyung Lee, The Ohio State University
12:05 PM Debiasing Principal Component Score Estimation in Exponential Family PCA for Sparse Count Data
Ruochen Huang, The Ohio State University; Yoonkyung Lee, The Ohio State University