Online Program Home
My Program
All Times EDT
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* = applied session ! = JSM meeting theme
Activity Details
8 * !
Mon, 8/3/2020,
10:00 AM -
11:50 AM
Virtual
Recent Advances in Statistical Learning for High-Dimensional and Heterogeneous Complex Data — Invited Papers
Section on Statistical Learning and Data Science , International Chinese Statistical Association, Journal on Statistical Analysis and Data Mining, Section on Statistical Computing
Organizer(s): Ji Zhu, University of Michigan
Chair(s): Omid Shams Solari, UC Berkeley
10:05 AM
Heterogeneous Mediation Analysis for Causal Inference
Fei Xue, University of Pennsylvania; Xiwei Tang, University of Virgina; Annie Qu, University of Illinois at Urbana-Champaign
10:25 AM
L-zero regularization in the causal mediation analysis
Peter X.K. Song, University of Michigan ; Wen Wang, University of Michigan
10:45 AM
Large matrix estimation for time series data
Hai Shu, New York University; Bin Nan, University of California, Irvine
11:05 AM
High-dimensional factor regression for heterogeneous subpopulations
Peiyao Wang, University of North Carolina at Chapel Hill; Quefeng Li, UNC Chapel Hill; Yufeng Liu, University of North Carolina at Chapel Hill ; Dinggang Shen, University of North Carolina at Chapel Hill
11:25 AM
Correlation Tensor Decomposition and Its Application in Spatial Imaging Data
Yujia Deng, University of Illinois, Urbana-Champaign; Xiwei Tang, University of Virgina ; Annie Qu, University of California Irvine
11:45 AM
Floor Discussion
16 * !
Mon, 8/3/2020,
10:00 AM -
11:50 AM
Virtual
How Statistics and Data Science Help to Quantify Resilience of Power Systems — Invited Papers
Section on Statistics in Defense and National Security , Section on Risk Analysis, Section on Statistical Learning and Data Science
Organizer(s): Asim Dey, Princeton University and University of Texas at Dallas; Yulia Gel, University of Texas at Dallas
Chair(s): Yuzhou Chen, Southern Methodist Univ, Statistical Science Dept
10:05 AM
Topology-Based Machine-Learning for Modeling Power-System Responses to Contingencies
Brian W Bush, NREL
10:30 AM
Geography and Network-of-Networks Properties
Stephen J Young, Pacific Northwest National Laboratory
10:55 AM
Topological and Geometric Methods for Resilience Analysis of Power Grid Networks
H. Vincent Poor, Princeton University; Yulia Gel, University of Texas at Dallas; Asim Dey, Princeton University and University of Texas at Dallas ; Umar Islambekov, Bowling Green State University
11:20 AM
Floor Discussion
28 * !
Mon, 8/3/2020,
10:00 AM -
11:50 AM
Virtual
Advances in Bayesian Theory and Methods on Network Data Modeling — Topic Contributed Papers
Section on Bayesian Statistical Science , Section on Statistical Learning and Data Science, International Society for Bayesian Analysis (ISBA)
Organizer(s): Yanxun Xu, Johns Hopkins University
Chair(s): Guanyu Hu, University of Connecticut
10:05 AM
Optimal Bayesian Estimation for Low-Rank Random Graphs
Fangzheng Xie, Johns Hopkins University ; Yanxun Xu, Johns Hopkins University
10:25 AM
Joint Bayesian Variable and DAG Selection Consistency for High-Dimensional Regression Models with Network-Structured Covariates
Presentation
Xuan Cao, University of Cincinnati ; Kyoungjae Lee, Inha University
10:45 AM
Probabilistic Community Detection with Unknown Number of Communities
Presentation
Junxian Geng, Boehringer Ingelheim ; Debdeep Pati, Texas A&M University; Anirban Battacharya, Texas A&M University
11:05 AM
Optimization for Bayesian Inference
Leo Duan, University of Florida
11:25 AM
Discussant: Yanxun Xu, Johns Hopkins University
11:45 AM
Floor Discussion
68
Mon, 8/3/2020,
10:00 AM -
2:00 PM
Virtual
Modern Statistical Learning Methods — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Sai Li, University of Pennsylvania
Comparison of the Performance of Different Recurrent Neural Network Models on Sequence Classification: A Simulation Study
Presentation
Dawei Liu, Biogen ; Charlie Cao, Biogen
Improved Strategies for Clustering Objects on Subsets of Attributes
Maarten Kampert ; Jacqueline Meulman, Leiden University, Stanford University ; Jerome Friedman, Stanford University
Multilayer Recommender Systems Using with Dependent via Tensor
Jiuchen Zhang, Univ of California in Irvine ; Annie Qu, University of California Irvine; Yubai Yuan, University of Illinois at Urbana-Champaign
Classification Method Optimization
Brooke McGinley, Rowan University ; Liam Doherty, Rowan University; Umashanger Thayasivam, Rowan University
On the Consistent Estimation of Receiver Operating Characteristic (ROC) Curve
Renxiong Liu, Ohio State University ; Yunzhang Zhu, Ohio State University
Simultaneous Or-Of-And Rules for Binary Classification
Elena Khusainova, Yale University ; Emily Dodwell, Data Science & AI Research, AT&T Labs; Ritwik Mitra, Data Science & AI Research, AT&T Labs
69
Mon, 8/3/2020,
10:00 AM -
2:00 PM
Virtual
Network Analysis — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Adam Rothman, University of Minnesota
Bias-Variance Tradeoffs in Joint Spectral Embeddings
Benjamin Draves, Boston University ; Daniel L Sussman, Boston University
Graph Matching via Mutual Nearest Neighbors
Zihuan Qiao, Boston University ; Daniel L Sussman, Boston University
Statistical Analysis and Methods for Human Connectomes
Daniel Kessler, University of Michigan ; Elizaveta Levina, University of Michigan; Keith Levin, University of Michigan, Department of Statistics
Estimating Latent Space Geometry of Network Formation Models
Shane Lubold, Department of Statistics, University of Washington ; Tyler McCormick, University of Washington; Arun Chandrasekhar, Stanford University
Anomaly Detection on Complex Networks via Topological Data Analysis
Ignacio Segovia-Dominguez, The University of Texas at Dallas ; Dorcas Ofori-Boateng, NASA Jet Propulsion Laboratory; Cuneyt Gurcan Akcora, University of Manitoba; Murat Kantarcioglu, The University of Texas at Dallas; Yulia Gel, University of Texas at Dallas
Network Structure Inference from Grouped Data
Yunpeng Zhao, Arizona State Univ ; Peter Bickel, University of California, Berkeley; Charles Weko, U.S. Army
High-Order Embedding for Hyperlink Network Prediction
Yubai Yuan, University of Illinois at Urbana-Champaign ; Annie Qu, University of California Irvine
70
Mon, 8/3/2020,
10:00 AM -
2:00 PM
Virtual
Multivariate Statistical Methods — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Xinwei Zhang, Rutgers University
A Semiparametric Approach to Inner Envelope
Linquan Ma, University of Wisconsin-Madison ; Hyunseung Kang, University of Wisconsin-Madison; Lan Liu, University of Minnesota at Twin Cities
Multi-Categorical Crowdsourcing Using Subgroup Latent Factor Modeling
Qi Xu, University of California, Irvine ; Yubai Yuan, University of Illinois at Urbana-Champaign; Junhui Wang, City University of Hong Kong; Annie Qu, University of California Irvine
Interpretable Recurrent Nonlinear Group Factor Analysis
Lin Qiu ; Vernon M. Chinchilli , The Pennsylvania State University ; Lin Lin, The Pennsylvania State University
Analyzing Dynamic Stock Trading Network with Matrix Factor Models
Ruofan Yu
Automated Kronecker Product Approximation
Chencheng Cai, Rutgers University ; Rong Chen, Rutgers University; Han Xiao, Rutgers University
Envelope Huber Regression
Le Zhou, University of Minnesota ; Dennis Cook, University of Minnesota; Hui Zou, University of Minnesota
105 * !
Mon, 8/3/2020,
1:00 PM -
2:50 PM
Virtual
Deep Learning and Statistical Modeling with Applications — Invited Papers
International Chinese Statistical Association , Section on Statistical Learning and Data Science, IMS
Organizer(s): Ji Zhu, University of Michigan
Chair(s): Ji Zhu, University of Michigan
1:05 PM
Deep Learning and Statistical Modeling with Applications
Yingying Fan, USC
1:30 PM
Beyond Shallow Learning: New Results for Matrix Completion
Jianqing Fan, Princeton University ; Yuxin Chen, Princeton University; Cong Ma, Princeton University; Yulin Yan, Princeton University
1:55 PM
Statistical Challenges in Analyzing Two-Sided Marketplace
HONGTU ZHU, DiDi
2:20 PM
From Classical Statistics to Modern Machine Learning
Mikhail Belkin, Ohio State University
2:45 PM
Floor Discussion
119 *
Mon, 8/3/2020,
1:00 PM -
2:50 PM
Virtual
Statistical Learning Applications for Autonomous Systems in Defense and National Security — Topic Contributed Papers
Section on Statistics in Defense and National Security , Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Joseph D Warfield, Johns Hopkins University Applied Physics Laboratory
Chair(s): Justin T Newcomer, Sandia National Laboratories
1:05 PM
Tallis: A Statistical Approach for Dimension Reduction of Mixed-Type Variables
Alexander Foss, Sandia National Laboratories
1:25 PM
Challenges in Test and Evaluation of AI-Enabled Systems in the DoD
Jane Pinelis, DoD Joint Artificial Intelligence Center
1:45 PM
Multinomial Pattern Matching
John Richards, Sandia National Laboratories
2:05 PM
Leveraging Machine Learning for Autonomy Testing and Evaluation
Galen Mullins, Johns Hopkins University Applied Physics Laboratory
2:25 PM
Demystifying the Black Box: A Strategy for Testing AI-Enabled Systems
Heather Wojton, Institute for Defense Analyses; Daniel Porter, Institute for Defense Analyses
2:45 PM
Floor Discussion
129 * !
Mon, 8/3/2020,
1:00 PM -
2:50 PM
Virtual
Advances in Graph Inference and Network Analysis — Topic Contributed Papers
Section on Statistical Learning and Data Science , Section on Statistics in Defense and National Security, Caucus for Women in Statistics, Section on Statistical Computing
Organizer(s): Joshua Cape, University of Michigan
Chair(s): Joshua Cape, University of Michigan
1:05 PM
Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace
Jesus Arroyo ; Avanti Athreya, Johns Hopkins University; Joshua Cape, University of Michigan; Guodong Chen, Johns Hopkins University; Carey Priebe, Johns Hopkins University; Joshua Vogelstein, Johns Hopkins University
1:25 PM
Network Community Detection Using Higher Order Interactions
Xianshi Yu ; Ji Zhu, University of Michigan
1:45 PM
Online Change Point Detection in Network Sequences
Sharmodeep Bhattacharyya, Oregon State University ; Shirshendu Chatterjee, City University of New York
2:05 PM
Estimation and Inference in Latent Structure Random Graphs
Avanti Athreya, Johns Hopkins University ; Minh Tang, NC State University; Youngser Park, Johns Hopkins University; Carey Priebe, Johns Hopkins University
2:25 PM
Posterior Predictive Distributions in Network Inference
Anna Smith, University of Kentucky ; Tian Zheng, Columbia University
2:45 PM
Floor Discussion
135 * !
Tue, 8/4/2020,
10:00 AM -
11:50 AM
Virtual
Move Non/Semiparametrics Forward in Causal Inference, Missing Data Analysis, and Data Integration — Invited Papers
Section on Nonparametric Statistics , Section on Statistical Learning and Data Science, International Chinese Statistical Association
Organizer(s): Jiwei Zhao, State University of New York at Buffalo
Chair(s): Jiwei Zhao, State University of New York at Buffalo
10:05 AM
Combining Multiple Observational Data Sources to Estimate Causal Effects
Peng Ding, University of California, Berkeley ; Shu Yang, North Carolina State University
10:30 AM
Learning When-to-Treat Policies
Stefan Wager, Stanford University; Xinkun Nie, Stanford University ; Emma Brunskill, Stanford University
10:55 AM
A Bayesian Nonparametric Approach for Estimating the Causal Effect of a Time-Varying/Dynamic Treatment
Michael Daniels, University of Florida ; Kumaresh Dhara, University of Florida; Jason Roy, Rutgers University
11:20 AM
Minimax Optimal Estimation of Heterogeneous Treatment Effects
Edward Kennedy, Carnegie Mellon University
11:45 AM
Floor Discussion
142 * !
Tue, 8/4/2020,
10:00 AM -
11:50 AM
Virtual
Fairness and Equity in Clinical Risk Prediction: Healthcare Data for the Public Good — Invited Papers
Biometrics Section , Health Policy Statistics Section, Section on Statistical Learning and Data Science, Lifetime Data Science Section
Organizer(s): Yates Coley, Kaiser Permanente Washington Health Research Institute
Chair(s): Maricela Cruz, Kaiser Permanente Washington Health Research Institute
10:05 AM
Assessing Racial and Ethnic Fairness of a Suicide Risk Prediction Model
Yates Coley, Kaiser Permanente Washington Health Research Institute ; Eric Johnson, Kaiser Permanente Washington Health Research Institute; Susan Shortreed, Kaiser Permanente Washington Health Research Institute
10:25 AM
Can Individualized Risk Calculators Reduce Racial/Ethnic Disparities in Cancer Screening Guidelines?
Hormuzd Katki, US National Cancer Institute ; Corey Young, Morehouse School of Medicine; Li Cheung, US National Cancer Institute; Rebecca Landy, US National Cancer Institute
10:45 AM
Detecting Undercompensated Groups in Plan Payment Risk Adjustment
Presentation
Anna Zink, Harvard University ; Sherri Rose, Harvard Medical School
11:05 AM
Discussant: Kristian Lum, HRDAG
11:25 AM
Discussant: Ruth Pfeiffer, National Cancer Institute
11:45 AM
Floor Discussion
150 *
Tue, 8/4/2020,
10:00 AM -
11:50 AM
Virtual
Operating a Private Statistical Consulting and Collaboration Practice: Words of Wisdom from Experts in the Field — Invited Panel
Section on Statistical Consulting , Section on Statistical Learning and Data Science, Section on Statistics and Data Science Education
Organizer(s): Harry Dean Johnson, Washington State University
Chair(s): Clark Kogan, Washington State University
10:05 AM
Operating a Private Statistical Consulting and Collaboration Practice: Words of Wisdom from Experts in the Field
Presentation
Panelists:
Stephen Simon , P. Mean Consulting
Elaine Eisenbeisz , OMEGA STATISTICS
Karen Grace-Martin, The Analysis Factor
Kim Love , K. R. Love Quantitative Consulting and Collaboration
Nayak Polissar , The Mountain-Whisper-Light Statistics
11:40 AM
Floor Discussion
168
Tue, 8/4/2020,
10:00 AM -
11:50 AM
Virtual
SLDS Student Paper Awards — Topic Contributed Papers
Section on Statistical Learning and Data Science
Organizer(s): Genevera Allen, Rice University
Chair(s): Irina Gaynanova, Texas A&M University
10:05 AM
Statistical Inference for Networks of High-Dimensional Point Processes
Xu Wang
10:25 AM
Classification Accuracy as a Proxy for Two-Sample Testing
Ilmun Kim, Carnegie Mellon University ; Aaditya Ramdas, Carnegie Mellon University; Aarti Singh, Carnegie Mellon University; Larry Wasserman, Carnegie Mellon University
10:45 AM
High-Dimensional Nonparametric Density Estimation via Max-Random Forest
Yiliang Zhang, University of Pennsylvania ; Qi Long, University of Pennsylvania; Weijie Su, University of Pennsylvania
11:05 AM
Testing for Association in Multi-View Network Data
Lucy Gao, University of Washington ; Daniela Witten, University of Washington; Jacob Bien, University of Southern California
11:25 AM
Learning Optimal Distributionally Robust Individualized Treatment Rules
Presentation
Weibin Mo, University of North Carolina at Chapel Hill ; Zhengling Qi, George Washington University; Yufeng Liu, University of North Carolina at Chapel Hill
11:45 AM
Floor Discussion
170
Tue, 8/4/2020,
10:00 AM -
11:50 AM
Virtual
Data Science: How Federal Agencies Are Upskilling Staff for the Modern Data Environment — Topic Contributed Panel
Government Statistics Section , Section on Statistics and Data Science Education, Section on Statistical Learning and Data Science
Organizer(s): Rebecca Hutchinson, US Census Bureau
Chair(s): Stephanie Studds, US Census Bureau
10:05 AM
Data Science: How Federal Agencies Are Upskilling Staff for the Modern Data Environment
Panelists:
Rebecca Hutchinson, US Census Bureau
Christian Moscardi, US Census Bureau
Alex Measure, Bureau of Labor Statistics
Bethany Blakey, General Services Administration
11:40 AM
Floor Discussion
173
Tue, 8/4/2020,
10:00 AM -
2:00 PM
Virtual
Recent Advances in Statistical Learning and Missing Data Handling — Contributed Papers
Korean International Statistical Society , Section on Statistical Learning and Data Science
Chair(s): Yimin Zhang, Villanova University
Estimating High-Dimensional Covariance and Precision Matrices Under General Missing Dependence
Seongoh Park, The Research Institute of Basic Sciences at Seoul National University ; Xinlei (Sherry) Wang, Southern Methodist University; Johan Lim, Seoul National University
A Generalized Kernel Two-Sample Test
Hoseung Song, University of California, Davis ; Hao Chen, University of California, Davis
Imputation Approach Based on Latent Class Trajectory for Handling Missing Values in Self-Reported Data
MinJae Lee, University of Texas Southwestern Medical Center
Variable Selection with Metaheuristic Methods
Myung Soon Song, Kutztown University of Pennsylvania ; Francis J Vasco, Kutztown University of Pennsylvania; Yun Lu, Kutztown University of Pennsylvania; Kyle Callaghan, Kutztown University of Pennsylvania
Sparse Machine Learning Methods for Regression with Regularized Tensor Product Kernel
Hang Yu, University of North Carolina at Chapel Hill ; Yuanjia Wang, Columbia University; Donglin Zeng, University of North Carolina at Chapel Hill
203
Tue, 8/4/2020,
10:00 AM -
2:00 PM
Virtual
Contemporary Machine Learning — Contributed Papers
Section on Statistical Learning and Data Science , Text Analysis Interest Group
Chair(s): Michael Lavine, Army Research Office
Risk Minimization Under Sampling Bias Arising from Customer Interactions
Scott Rome, Comcast ; Michael Kreisel, Comcast
An Optimal Approach in Adaptive Collection Reducing the Bias
Tong Wang
Transfer Learning for Auto-Coding Free-Text Survey Responses
Peter Baumgartner, RTI International ; Amanda Smith, RTI International; Murrey Olmsted, RTI International; Dawn Ohse, RTI International; Bucky Fairfax, RTI International
Post-Hoc Mixture Models of the Best Linear Unbiased Predictors from Linear Mixed Effects Models to Classify Longitudinal Data with Haphazardly Spaced Intervals: A Simulation Study
Md Hossain, Nemours Biomedical Research, A.I. DuPont Children's Hospital ; Benjamin E Leiby, Division of Biostatistics
Modeling Temporary Shocks with Latent Processes for High-Dimensional Demand Time Series
Benedikt Sommer, Maersk ; Klaus Kähler Holst, Maersk Research & Data; Pierre Pinson, Technical University of Denmark
Real-Time Regression Analysis of Streaming Clustered Data Sets
Lan Luo, University of Michigan ; Peter X.K. Song, University of Michigan
204
Tue, 8/4/2020,
10:00 AM -
2:00 PM
Virtual
Experimental Design — Contributed Papers
Section on Statistical Learning and Data Science , Quality and Productivity Section
Chair(s): Shan Ba, LinkedIn
Generalization of Thompson Sampling for Multiple Categorical and Numerical Variables with Application for Fraud Detection
Alex Zolotovitski, T-Mobile
Design of Experiment-based Configuration of Hyperparameters Of An Artificial Neural Network
Luca Pegoraro, University of Padova ; Rosa Arboretti, University of Padova; Riccardo Ceccato, University of Padova; Luigi Salmaso, University of Padova
How Twitter Makes Causal Inference If AB Test Fails
Wutao Wei, Twitter
SoftBlock: Efficient and Optimal Treatment Assignment for Experiments
Peter Dimmery, Facebook ; David Arbour, Adobe Research; Anup Rao, Adobe Research
Satellite Images and Deep Learning to Indentify Discrepency in Mailing Addresses with Applications to Census 2020 in Houston
Zhaozhuo Xu, Rice University ; Beidi Chen, Rice University; Alan Ji, Rice University; Anshumali Shrivastava, Rice University
The Future Is Linked: Making Predictions with Data Sets Linked to Synthetic Populations
Emily Hadley, RTI International ; Caroline Kery, RTI International; Georgiy Bobashev, RTI International; Lauren Grattan, RTI International
Resampling Methods for FDR Control of A/B/N Tests with Arbitrary Dependencies
Michael Rotkowitz, Lyft
205
Tue, 8/4/2020,
10:00 AM -
2:00 PM
Virtual
Applications of Machine Learning — Contributed Papers
Section on Statistical Learning and Data Science , Text Analysis Interest Group
Chair(s): Jennifer Green, Montana State University
Summarizing and Extracting Insights from Consumer Review Data
Jingting Hui, PepsiCo ; Jason Parcon, PepsiCo
Comparison of Machine Learning Methods with Traditional Models for Use of Public Trial Registry Data to Predict Sites Needed and Time from Study Start to Primary Completion Date
Presentation
Linghui Li, AstraZeneca ; Gabriela Feldberg, AstraZeneca; Faisal Khan, AstraZeneca; Sandra Smyth, AstraZeneca; Karin Schiene, AstraZeneca
Comparing Machine Learning and Penalized Regression for Predicting Diabetic Kidney Disease Progression: Evidence from the Chronic Renal Insufficiency Cohort (CRIC) Study
Jing Zhang, Moores Cancer Center, University of California, San Diego ; Tobias Fuhrer, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland; Brian Kwan, University of California, San Diego; Daniel Montemayor, Department of Medicine, University of Texas Health Science Center at San Antonio; Kumar Sharma, Department of Medicine, University of Texas Health Science Center at San Antonio; Loki Natarajan, University of California, San Diego
Opportunities and Challenges in the Use of Smartphone and Smartwatch-Based Step Count Measures in Studies of Physical Activity and Health
Briana Cameron, 23andMe; Teresa Filshtein Sonmez, 23andMe ; Stella Aslibekyan, 23andMe; Robert Gentleman, 23andMe
Improving Productionized Insights in Machine Learning Models Through Data-Quality Quantification
Christopher Barbour, Atrium ; Paul Harmon, Atrium; Eric Loftsgaarden, Atrium
206
Tue, 8/4/2020,
10:00 AM -
2:00 PM
Virtual
Machine Learning Methodology — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Erin E Blankenship, University of Nebraska-Lincoln
Application of Stochastic Gradient Descent in Parameter Estimation for Models with Spatial Correlation
Gan Luan, New Jersey Institute of Tech
Interpreting Robust Optimization via Adversarial Influence Functions
Linjun Zhang, Rutgers University ; Zhun Deng, Harvard University; Jialiang Wang, Harvard University; Cynthia Dwork, Harvard University
Regularized High-Dimensional Low Tubal Rank Tensor Regression
Samrat Roy ; George Michailidis, University of Florida
Adversarial Networks for Robust Estimation
Ziyue Wang, Rutgers University-New Brunswick ; Zhiqiang Tan, Rutgers University
Validation of Neuro-Fuzzy Based Classifiers for Outcome from Longitudinal RCT
Venkata Sukumar Gurugubelli, University of Massachusetts - Dartmouth
Application of Machine Learning Imputation for Machine Learning
Jonathan Lisic, Cigna
215
Tue, 8/4/2020,
10:00 AM -
2:00 PM
Virtual
Contributed Poster Presentations: Section on Statistical Learning and Data Science — Contributed Poster Presentations
Section on Statistical Learning and Data Science
1:
Identifying Pareto-Based Multiobjective Solutions for Subset Selection
Joshua Lambert, University of Cincinnati
2:
Causal Effect Random Forest of Interaction Trees for Observational Data, Applied to Educational Interventions
Juanjuan Fan, San Diego State University ; Luo Li, San Diego State University; Xiaogang Su, University of Texas, El Paso; Richard Levine, San Diego State University
3:
Real-Time Classification of Atrial Fibrillation Using RR Intervals and Transition States
Jericho Lawson, University of Arizona
4:
Statistical Invariance of Betti Numbers in the Thermodynamic Regime
Siddharth Vishwanath, Penn State Univ
5:
Network Clustering with Entropy-Based Monte Carlo Method
Qiannan Zhai, Texas Tech University ; Fangyuan Zhang, Texas Tech University
6:
Context-Dependent Self-Exciting Point Processes: Models, Methods, and Risk Bounds in High Dimensions
Lili Zheng, University of Wisconsin-Madison ; Garvesh Raskutti, UW-Madison; Rebecca Willett, University of Chicago
7:
A Comparison of Machine Learning Models for Mortality Prediction: National Health and Nutrition Examination Survey (NHANES III)
Zoran Bursac, Florida International University; Roy Williams, Florida International University ; Miguel Alonso, Florida International University; Prasad Bhoite, Florida International University; Emir Veledar, Florida International University
8:
Incorporating Group Structure into Tree-Based Algorithms and Group Selection Through Importance Measures
Jiabei Yang, Brown University School of Public Health ; Emily Dodwell, Data Science & AI Research, AT&T Labs; Ritwik Mitra, Data Science & AI Research, AT&T Labs; DeDe Paul, Data Science & AI Research, AT&T Labs
9:
Stagewise Estimating Equations for Variable Selection with Longitudinal Rate Data
Gregory Vaughan, Bentley University
10:
Weakly Supervised Chinese Meta-Pattern Discovery and NER via TopWORDS 2
Jiaze Xu, Tsinghua University ; Ke Deng, Tsinghua University
11:
Random-Projection Based Classification from Big Tensor Data
Peide Li
13:
Connectivity Based Outlier Detection
Chang Liu, Rutgers University ; Rong Chen, Rutgers University
14:
An Algorithm for Adjusted Kernel Linear Discriminant Analysis
Lynn Huang, Iowa State University
15:
Deriving and Generalizing Kernel Linear Discriminant Analysis for Multiple Cases
Jackson Maris
16:
Adjusting Factor Models for Concomitant Variables by Adversarial Learning
Austin Talbot, Duke University ; David Carlson, Duke University; David Dunson, Duke University
18:
Covariance Estimation for Matrix-Variate Data with Missing Values and Mean Structure
Roger Fan, University of Michigan ; Shuheng Zhou, University of California, Riverside; Byoungwook Jang, University of Michigan
19:
Estimating Sleep from Sparse Screen-On/Screen-Off Smartphone Data
Melissa Martin, University of Pennsylvania
20:
Robust Extrinsic Framework for Manifold Valued Data Analysis
Hwiyoung Lee, Florida State University
21:
Clustering High Needs/Complex Patients Using Latent Class Analysis
Meghan Hatfield, Kaiser Permanente ; Jodi McCloskey, Kaiser Permanente; Connie Uratsu, Kaiser Permanente; Richard Grant, Kaiser Permanente
22:
SuperMICE: Multiple Imputation by Chained SuperLearners
Aaron Shev, University of California, Davis ; Hannah Laqueur, University of California, Davis; Rose Kagawa, University of California, Davis
23:
Feature Selection for Support Vector Regression Using a Genetic Algorithm
Shannon McKearnan, University of Minnesota ; David Vock, University of Minnesota; Julian Wolfson, University of Minnesota
24:
Time Varying Estimation of Tensor-On-Tensor Regression with Application in fMRI Data
Pratim Guha Niyogi, Michigan State University ; Tapabrata (Taps) Maiti, Michigan State University
25:
An Analytical Approach for Prediction Involving Classification of Data with Complex Structure
Li-Jung Liang, UCLA ; Joseph Maurer, UCLA; Li Li, UCLA
26:
Social Network Distributed Autoregressive Distributed Lag Model
Christopher Grubb, Virginia Tech ; Shyam Ranganathan, Virginia Tech; Srijan Sengupta, Virginia Tech; Jennifer Van Mullekom, Virginia Tech
27:
Assessment of Data Reduction Models Including Autoencoders for Optimal Visualization, Interpretability and Speed
Benedict Anchang, NIEHS
28:
Aggregate Estimation in Sufficient Dimension Reduction for Binary Responses
Han Zhang, The University of Alabama ; Qin Wang, The University of Alabama
29:
Tensor Clustering with Planted Structures: Statistical Optimality and Computational Limits
Yuetian Luo, University of Wisconsin-Madison ; Anru Zhang, University of Wisconsin-Madison
30:
Augmented Movelet Method for Activity Recognition Using Smartphone Gyroscope and Accelerometer Data
Emily Huang, Wake Forest University Department of Mathematics and Statistics ; Jukka-Pekka Onnela, Harvard University
31:
Robust Matrix Estimations Meet Frank-Wolfe Algorithms
Naimin Jing, Temple University ; Cheng Yong Tang, Temple University; Ethan Fang, Penn State University
32:
Optimal Transport for Stationary Markov Chains
Kevin O'Connor, University of North Carolina, Chapel Hill ; Andrew Nobel, University of North Carolina, Chapel Hill; Kevin McGoff, University of North Carolina, Charlotte
33:
Early Prediction of Alzheimer’s Disease with Deep Learning Using Data Integration of MRI Data and Clinical Data
Lisa Neums, University of Kansas Medical Center ; Jinxiang Hu, University of Kansas Medical Center; Jeffrey Thompson, University of Kansas Medical Center
34:
Anomaly Detection Methods for IoT Freeze Loss
Patrick Toman, University of Connecticut - Department of Statistics ; Ahmed Soliman, University of Connecticut; Nalini Ravishanker, University of Connecticut; Sanguthevar Rajasekaran, University of Connecticut; Nathan Lally, Hartford Steam Boiler; Yuchen Fama, Hartford Steam Boiler
242 * !
Tue, 8/4/2020,
1:00 PM -
2:50 PM
Virtual
Deep Learning Methods in Biomedical Studies — Invited Papers
WNAR , Section on Statistical Computing, Section on Statistical Learning and Data Science
Organizer(s): Wei Sun, Fred Hutchinson Cancer Research Center
Chair(s): Kin Yau Alex Wong, The Polytechnic University of Hong Kong
1:05 PM
Multi-Stage Sequential Deep Autoencoder-Based Monotone Nonlinear Dimensionality Reduction Methods
Youyi Fong, Fred Hutchinson Cancer Research Center ; Jun Xu, None
1:30 PM
A Computationally-Favorable Reframing of Proportional-Hazards Modeling for Large Time-to-Event Data Sets with Applications to Deep Learning
Noah Simon, University of Washington
1:55 PM
Deep Learning Methods for Single Cell Data Denoising and Multimodal Imputation
Nancy Zhang, The University of Pennsylvania
2:20 PM
Discussant: Wei Sun, Fred Hutchinson Cancer Research Center
2:40 PM
Floor Discussion
243 * !
Tue, 8/4/2020,
1:00 PM -
2:50 PM
Virtual
Thinking Beyond the P-Value: Advancing Bayesian Education for the Undergraduates — Invited Papers
Section on Bayesian Statistical Science , Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Jingchen (Monika) Hu, Vassar College
Chair(s): Jingchen (Monika) Hu, Vassar College
1:05 PM
Quiz: Are You a Bayesian?
Alicia A Johnson, Macalester College
1:25 PM
Bayes for Undergraduates: A Prior Is Modified
Presentation
Jeffrey A Witmer, Oberlin College
1:45 PM
Challenges of Teaching Bayesian Statistics to Undergraduates
Brian Reich, North Carolina State University
2:05 PM
Computation Infrastructure for Teaching Bayesian Modeling
Colin Witter Rundel, University of Edinburgh
2:25 PM
A Probability Plus Bayes Course
Jim H Albert, Bowling Green State University
2:45 PM
Floor Discussion
253 * !
Tue, 8/4/2020,
1:00 PM -
2:50 PM
Virtual
Innovations in AstroStatistics on Exploring Large Public Data — Invited Papers
Astrostatistics Special Interest Group , Section on Physical and Engineering Sciences, Section on Statistical Learning and Data Science, Quality and Productivity Section
Organizer(s): Hyungsuk Tak, Pennsylvania State University
Chair(s): Hyungsuk Tak, Pennsylvania State University
1:05 PM
Handling Model Uncertainty via Smoothed Inference
Sara Algeri, University of Minnesota
1:30 PM
Improving Exoplanet Detection Power: Multivariate Gaussian Process Models for Stellar Activity
David Edward Jones, Texas A&M University ; David Stenning, Imperial College London; Eric B Ford, Penn State University; Robert L Wolpert, Duke University; Thomas J Loredo, Cornell University; Xavier Dumusque, Observatoire Astronomique de l'Universite de Geneve
1:55 PM
Disentangling Stellar Activity and Planetary Signals Using Bayesian High-dimensional Analysis
Bo Ning, Yale University ; Jessi Cisewski-Kehe, Yale University; Allen Davis, Yale University; Parker Holzer, Yale University; Debra Fischer, Yale University
2:20 PM
Floor Discussion
254 * !
Tue, 8/4/2020,
1:00 PM -
2:50 PM
Virtual
Digital Phenotyping — Invited Papers
Mental Health Statistics Section , Section on Statistical Learning and Data Science, Biometrics Section
Organizer(s): Samprit Banerjee, Weill Medical College, Cornell University
Chair(s): Wenna Xi, Weill Medical College, Cornell University
1:05 PM
Digital Phenotype of Patients with Major Depressive Disorder
Samprit Banerjee, Weill Medical College, Cornell University ; Jihui Lee, Weill Cornell Medicine
1:25 PM
Physical Activity Recognition Using Smartphone Data
Jukka-Pekka Onnela, Harvard University
1:45 PM
Using Federated Learning to Address User Heterogeneity and Privacy in Mobile Health Data
Ambuj Tewari, University of Michigan
2:05 PM
A Pre-Processing Pipeline of MHealth Data
Jihui Lee, Weill Cornell Medicine ; Hongzhe Zhang, Weill Cornell Medicine; Samprit Banerjee, Weill Medical College, Cornell University
2:25 PM
Behavioral Monitoring and Change Point Detection in Digital Phenotyping Studies
Ian Barnett, University of Pennsylvania
2:45 PM
Floor Discussion
257 *
Tue, 8/4/2020,
1:00 PM -
2:50 PM
Virtual
Online Experimentation at Scale: Challenges and Solutions — Invited Panel
Section on Statistical Learning and Data Science , Business and Economic Statistics Section, Section for Statistical Programmers and Analysts, Quality and Productivity Section
Organizer(s): Martin Tingley, Netflix
Chair(s): Iavor Bojinov, Harvard Business School
1:05 PM
Online Experimentation at Scale: Challenges and Solutions
Panelists:
Martin Tingley, Netflix
Somit Gupta, Microsoft
Xiaolin Shi, Snap
Myoungji Lee, Lyft
Guillaume Saint-Jacques, LinkedIN
Dennis Sun, Cal Poly and Google
2:40 PM
Floor Discussion
260 * !
Tue, 8/4/2020,
1:00 PM -
2:50 PM
Virtual
Statistics and AI in Music — Topic Contributed Papers
Royal Statistical Society , Section on Statistical Learning and Data Science, IMS
Organizer(s): Jan Beran, University of Konstanz
Chair(s): Philipp Sibbertsen, Leibniz Universitaet Hannover
1:05 PM
Understanding Audio from Music Practice Sessions
Christopher Raphael, Indiana University
1:25 PM
Visualizing Music Information: Classical Composers Networks and Similarities
Presentation
Patrick Georges, University of Ottawa
1:45 PM
Statistics and AI in Music
Ahmed Elgammal, Artrendex / Rutgers University; Mark Gotham, Universität des Saarlandes / Cornell
2:05 PM
Fusing Audio and Semantic Technologies: Applying AI, Machine Learning and Data Science to Music Production and Consumption
Mark Sandler, Queen Mary University of London ; Johan Pauwels, Queen Mary University of London; David De Roure, University of Oxford; Kevin Page, University of Oxford
2:25 PM
Discussant: Jan Beran, University of Konstanz
2:45 PM
Floor Discussion
268 * !
Tue, 8/4/2020,
1:00 PM -
2:50 PM
Virtual
Extreme Machine Learning Methods and Applications: Domestic and International — Topic Contributed Papers
Section on Statistical Consulting , Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Kelly Toppin, IMT Corporation
Chair(s): Lu Chen, National Institute of Statistical Sciences
1:05 PM
Hierarchical Gaussian Processes for Outlier Detection
Felix Jimenez, National Institute of Standards and Technology
1:25 PM
Empirical data-fusion approaches to generate model covariates
Luca Sartore, National Institute of Statistical Sciences ; Jake Abernethy, NASS; Claire Boryan, USDA; Lu Chen, USDA; Kevin Hunt, USDA; CLIFFORD H SPIEGELMAN, Texas A&M University; Linda J Young, NASS
1:45 PM
Early Season Planted Acreage Estimates Using Machine Learning
Jake Abernethy, NASS ; Claire Boryan, USDA; Kevin Hunt, USDA; Luca Sartore, National Institute of Statistical Sciences
2:05 PM
Classifying Evolving Data Streams
Kelly Toppin, IMT Corporation ; Luca Sartore, National Institute of Statistical Sciences
2:25 PM
Floor Discussion
284 * !
Wed, 8/5/2020,
10:00 AM -
11:50 AM
Virtual
Statistical Learning for Dependent and Complex Data: New Directions and Innovation — Invited Papers
Section on Statistics in Marketing , Business and Economic Statistics Section, Section on Statistical Learning and Data Science
Organizer(s): Guannan Wang, College of William and Mary
Chair(s): Guannan Wang, College of William and Mary
10:05 AM
Reduced Rank Autoregressive Models for Matrix Time Series
Rong Chen, Rutgers University
10:30 AM
Fast and Fair Simultaneous Confidence Bands for Functional Parameters
Matthew Reimherr, Penn State University
10:55 AM
High-Dimensional Sparse Nonlinear Vector Autoregressive Models
Yuefeng Han, Rutgers University ; Wei Biao Wu, University of Chicago; Likai Chen, Washington University in St. Louis
11:20 AM
Spatiotemporal Dynamics, Nowcasting and Forecasting COVID-19 in the United States
Guannan Wang, College of William and Mary; Lily Wang, Iowa State University ; Yueying Wang, Iowa State University
11:45 AM
Floor Discussion
285 * !
Wed, 8/5/2020,
10:00 AM -
11:50 AM
Virtual
Statistical Inference for Probabilistic Graphical Models with Applications — Invited Papers
ENAR , Section on Statistical Learning and Data Science, Biometrics Section
Organizer(s): Mladen Kolar, University of Chicago
Chair(s): Mladen Kolar, University of Chicago
10:05 AM
Combinatorial Inference for Brain Imaging Data Sets
Junwei Lu, Harvard
10:30 AM
Bayesian Structure Learning for Dynamic Brain Connectivity
Presentation
Sanmi Koyejo, Department of Computer Science, University of Illinois at Urbana-Champaign
10:55 AM
Detecting Differences in Brain-Region Correlations Between Two Groups
Yuval Benjamini, Hebrew University of Jerusalem ; Itamar Faran, Hebrew University of Jerusalem; Michael Peer, Hebrew University of Jerusalem ; Shahar Arzy, Hebrew University of Jerusalem
11:20 AM
Direct Inference for Sparse Differential Network Analysis
Irina Gaynanova, Texas A&M University ; Byol Kim, University of Chicago; Mladen Kolar, University of Chicago
11:45 AM
Floor Discussion
286
Wed, 8/5/2020,
10:00 AM -
11:50 AM
Virtual
Learning Networks from Point Processes: Neuronal Connectivity Networks and Beyond — Invited Papers
Section on Statistical Learning and Data Science , IMS, ENAR
Organizer(s): Ali Shojaie, University of Washington
Chair(s): Ali Shojaie, University of Washington
10:05 AM
Context-Dependent Self-Exciting Point Processes: Models, Methods, and Risk Bounds in High Dimensions
Garvesh Raskutti, UW-Madison ; Lili Zheng, University of Wisconsin-Madison; Rebecca Willett, University of Chicago
10:30 AM
A Universal Nonparametric Event Detection Framework for Neuropixels Data
Shizhe Chen, University of California, Davis ; Hao Chen, University of California, Davis; Xinyi Deng, Columbia University
10:55 AM
Latent Network Structure Learning from High-Dimensional Multivariate Point Processes
Biao Cai, University of Miami; Emma Jingfei Zhang, University of Miami ; Yongtao Guan, University of Miami
11:20 AM
Theory and Modeling for the Truncated Hawkes Process
Victor Solo, UNSW, Sydney
11:45 AM
Floor Discussion
299 !
Wed, 8/5/2020,
10:00 AM -
11:50 AM
Virtual
Machine Learning in Causal Inference with Applications in Complicated Settings — Topic Contributed Papers
Biometrics Section , ENAR, Section on Statistical Learning and Data Science
Organizer(s): Rui Song, NC State University
Chair(s): Hongtu Zhu, University of North Carolina at Chapel Hill
10:05 AM
DOES the MARKOV DECISION PROCESS FIT the DATA: TESTING for the MARKOV PROPERTY in SEQUENTIAL DECISION MAKING
Presentation
Chengchun Shi, The London School of Economics ; Runzhe Wan, NC State Univeristy; Rui Song, NC State University; Wenbin Lu, North Carolina State University; Ling Leng, Amazon
10:25 AM
The Confidence Interval Method for Selecting Valid Instrumental Variables
Frank Windmeijer, University of Oxford
10:45 AM
Testing an Elaborate Theory of a Causal Hypothesis
Dylan Small, University of Pennsylvania ; Bikram Karmakar, University of Florida
11:05 AM
Personalized Policy Learning Using Longitudinal Mobile Health Data
Min Qian, Columbia University ; Xinyu Hu, Uber AI Lab; Bin Cheng, Columbia University; Ken Cheung, Columbia University
11:25 AM
Discussant: Jialiang Li, National University of Singapore
11:45 AM
Floor Discussion
301 *
Wed, 8/5/2020,
10:00 AM -
11:50 AM
Virtual
Natural Language Processing Applications in Defense and National Security — Topic Contributed Papers
Section on Statistics in Defense and National Security , Text Analysis Interest Group, Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Joseph D Warfield, Johns Hopkins University Applied Physics Laboratory
Chair(s): Joseph D Warfield, Johns Hopkins University Applied Physics Laboratory
10:05 AM
Neural Language Processing to Detect, Attribute, Characterize and Defend Against Digital Deception
Svitlana Volkova, Pacific Northwest National Laboratory
10:25 AM
A Sliding Information Distance for Change Point Detection in Text or Audio
Richard Field, Sandia National Laboratories ; Christina Ting, Sandia National Laboratories; Travis Bauer, Sandia National Laboratories
10:45 AM
Few-Shot Learning for Text Applications: Exploring Authorship Identification with Small Data
Lauren Phillips, Pacific Northwest National Laboratory ; Sarah Reehl, Pacific Northwest National Laboratory; Ana Usenko, Western Washington University
11:05 AM
Classifying Documents Through the Use of Artificial Intelligence
Kelly Townsend, Johns Hopkins University, Applied Physics Laboratory ; Alex Firpi, Johns Hopkins University Applied Physics Lab
11:25 AM
Discussant: David Marchette, US Naval Surface Warfare Center Dahlgren Division
11:45 AM
Floor Discussion
306
Wed, 8/5/2020,
10:00 AM -
11:50 AM
Virtual
Algorithmic and Inferential Advances in Univariate and Multivariate Tuning-Parameter-Free Nonparametric Procedures — Topic Contributed Papers
Section on Statistical Learning and Data Science , Section on Nonparametric Statistics, IMS
Organizer(s): Charles Doss, University of Minnesota
Chair(s): Guangwei Weng, University of Minnesota
10:05 AM
Multivariate Rank-Based Distribution-Free Nonparametric Testing Using Measure Transportation
Bodhisattva Sen, Columbia University ; Nabarun Deb, Columbia University
10:25 AM
Likelihood Ratio Tests and Confidence Intervals Based on the Shape Constraint of Concavity
Charles Doss, University of Minnesota ; Jon Wellner, University of Washington
10:45 AM
Multivariate Adaptation in Log-Concave Density Estimation
Arlene K. H. Kim, Korea University ; Richard Samworth, University of Cambridge; Oliver Feng, University of Cambridge; Adityanand Guntuboyina, University of California, Berkeley
11:05 AM
Dyadic CART Revisited
Sabyasachi Chatterjee, University of Illinois at Urbana-Champai
11:25 AM
Learning Multivariate Log-Concave Densities
Ilias Diakonikolas, UW Madison
11:45 AM
Floor Discussion
309 *
Wed, 8/5/2020,
10:00 AM -
11:50 AM
Virtual
Interface Between Machine Learning and Uncertainty Quantification — Topic Contributed Papers
Uncertainty Quantification in Complex Systems Interest Group , Section on Physical and Engineering Sciences, Section on Statistical Learning and Data Science, Quality and Productivity Section
Organizer(s): Ana Kupresanin, Lawrence Livermore National Laboratory
Chair(s): Kathleen Schmidt, Lawrence Livermore National Laboratory
10:05 AM
On-Site Surrogates for Large-Scale Calibration
Jiangeng Huang, University of California Santa Cruz ; Robert Gramacy, Virginia Tech
10:25 AM
Calibrating Uncertainties in Deep Learning
Bhavya Kailkhura, Lawrence Livermore National Laboratory ; Jize Zhang, Lawrence Livermore National Lab
10:45 AM
Physics-Informed Machine Learning for Uncertainty Quantification in Land Models
Presentation
Khachik Sargsyan, Sandia National Laboratories ; Cosmin Safta, Sandia National Laboratories; Vishagan Ratnaswamy, Sandia National Laboratories
11:05 AM
Quantifying Model Transfer Uncertainties Using Post Hoc Explainability in Deep Learning Models
Evangelina Brayfindley, Pacific Northwest National Laboratory ; Thomas Grimes, Pacific Northwest National Lab
11:25 AM
Floor Discussion
354
Wed, 8/5/2020,
10:00 AM -
2:00 PM
Virtual
Multivariate Analysis and Graphical Models — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Han Zhang, The University of Alabama
Fused-Lasso Regularized Cholesky Factors of Large Nonstationary Covariance Matrices of Longitudinal Data
Aramayis Dallakyan, Texas A&M University ; Mohsen Pourahmadi, Texas A&M University
Sparse Generalized Correlation Analysis and Thresholded Gradient Descent
Presentation
Sheng Gao, University of Pennsylvania ; Zongming Ma, University of Pennsylvania
Smooth Time-Varying Gaussian Graphical Models to Study Disease Progression
Erin Mcdonnell, Columbia University ; Shanghong Xie, Columbia University; Karen Marder, Columbia University; Yuanjia Wang, Columbia University
Tensor Mixture Model in High Dimensions
Biao Cai, University of Miami ; Emma Jingfei Zhang, University of Miami; Wei Sun, Purdue University
Robust Estimation of High-Dimensional Heavy Tailed Vector Autoregressive Models
Sagnik Halder ; George Michailidis, University of Florida
355 !
Wed, 8/5/2020,
10:00 AM -
2:00 PM
Virtual
Modern Model Selection — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Tianxi Li, University of Virginia
Controlling Costs: Feature Selection on a Budget
Presentation
Guo Yu, University of Washington ; Daniela Witten, University of Washington; Jacob Bien, University of Southern California
On the Robustness of LASSO-Type Estimators to Covariance Misspecification
Rebecca North, North Carolina State University ; Jonathan Stallrich, North Carolina State University
Sparse Group LASSO False Discovery Rate Path
Kan Chen ; Zhiqi Bu, University of Pennsylvania
Variable Selection with False Discovery Rate Control in Deep Neural Networks
Zixuan Song, University of Notre Dame ; Jun Li, University of Notre Dame
The Complete Lasso Trade-Off Diagram: Above the Donoho–Tanner Phase Transition
Yachong Yang, Univ of Pennsylvania, Wharton School of Business ; Hua Wang, University of Pennsylvania, Statistics Department of Wharton; Weijie Su, University of Pennsylvania
356
Wed, 8/5/2020,
10:00 AM -
2:00 PM
Virtual
Statistical Learning: Methods and Applications — Contributed Papers
Section on Statistical Learning and Data Science , Text Analysis Interest Group
Chair(s): Scott Rome, Comcast
Combination of Optical Character Recognition and Natural Language Processing to Identify Patients with Sleep Apnea in EHR Data
Enshuo Hsu, University of Texas Medical Branch ; Yong-Fang Kuo, University of Texas Medical Branch; Rizwana Sultana, University of Texas Medical Branch; Gulshan Sharma, University of Texas Medical Branch
Flexible Feature Selection and Cluster Analysis for Heterogeneous Data with Application to a Diffusion Tensor Imaging Study
Wanying Ma, Novartis Pharmaceuticals Company ; Luo Xiao, North Carolina State University; Jaroslaw Harezlak, Indiana University
Genetic Algorithms for Feature Selection
Huanjun Zhang, Texas A&M University ; Edward Jones, Texas A&M University
Towards an Adaptive Algorithm for Online Substance Use Episode Detection
Joshua Rumbut, University of Massachusetts Dartmouth, University of Massachusetts Medical School ; Hua Fang, University of Massachusetts Dartmouth, University of Massachusetts Medical School
Common and Distinctive Pattern Analysis Between High-Dimensional Data Sets
Hai Shu, New York University ; Zhe Qu, Tulane University
Achieving Impact with Data Science and Machine Learning in Drug Development
David Ohlssen, Novartis Pharmaceuticals
Automatic Identification and Classification of Different Types of Otitis from Free-Text Pediatric Medical Notes: A Deep-Learning Approach
Corrado Lanera, University of Padova
357
Wed, 8/5/2020,
10:00 AM -
2:00 PM
Virtual
Contemporary Multivariate Methods — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Alex Zolotovitski, T-Mobile
Uplift Modeling for Panel Data Using Switch Doubly Robust Method
Presentation
Hiroaki Naito, Doshisha University ; Hisayuki Hara, Doshisha University
Stepsize Selection in Langevin Monte Carlo via Coupling
Presentation
Matteo Sordello, University of Pennsylvania ; Weijie Su, University of Pennsylvania; James Johndrow, University of Pennsylvania
Model Selection Criteria for Biological Networks by Using Loop-Based Multivariate Regression Adaptive Splines Model
Gul Bulbul, Bowling Green State Univ ; Vilda Bahar Purutçuo?lu, Middle East Technical University
Generalized Linear Models with Partially Mismatched Data
Zhenbang Wang, George Mason University ; Emanuel Ben-David, US Census Bureau; Martin Slawski, George Mason Univ
Numerical Tolerance for Spectral Decompositions of Random Matrices
Zachary Lubberts, Johns Hopkins University ; Avanti Athreya, Johns Hopkins University; Vince Lyzinski, University of Maryland College Park; Minh Tang, NC State University; Carey Priebe, Johns Hopkins University; Michael J Kane, Yale University School of Public Health; Youngser Park, Johns Hopkins University; Bryan Lewis, Independent Researcher
Inference in Higher Order Spin Systems
Presentation
Somabha Mukherjee, University of Pennsylvania ; Jaesung Son, University of Pennsylvania; Bhaswar Bhattarcharya, University of Pennsylvania
389 * !
Wed, 8/5/2020,
1:00 PM -
2:50 PM
Virtual
Unsupervised Learning with Latent Variables for Biobehavioral Research — Invited Papers
Mental Health Statistics Section , Section on Statistical Learning and Data Science, Biometrics Section
Organizer(s): Douglas David Gunzler, Case Western Reserve University
Chair(s): Xiao-Li Meng, Harvard University
1:05 PM
Constructing Latent Risk Labels Aligned with Clinical Practice
Presentation
Booil Jo, Stanford University
1:25 PM
Looking Inside the Black Box: New Methods to Assess Causality in Unsupervised Machine Learning
Alessandro De Nadai, Texas State University ; Ryan Zamora, Texas State University; Douglas David Gunzler, Case Western Reserve University
1:45 PM
Dealing with Latent Variable Confounding Using Auxiliary Variables and PushForward
Presentation
Bryant Chen, Brex Inc.
2:05 PM
Discussant: Mark Van der Laan, University of California, Berkeley
2:25 PM
Discussant: Maya Mathur, Stanford University
2:45 PM
Floor Discussion
392 * !
Wed, 8/5/2020,
1:00 PM -
2:50 PM
Virtual
Big Tensor Data Analysis — Invited Papers
Section on Statistical Learning and Data Science , International Chinese Statistical Association, Section on Nonparametric Statistics, Section on Statistical Computing
Organizer(s): Anru Zhang, University of Wisconsin-Madison
Chair(s): Anru Zhang, University of Wisconsin-Madison
1:05 PM
Tensor Response Regression in the Presence of Heavy Tails
Xin Zhang, Florida State University
1:30 PM
Tensor Models for Large, Complex and High-Dimensional Data
Shuheng Zhou, University of California, Riverside
1:55 PM
Co-Clustering Tensors Using Fusion Penalties and CP-Decompositions
Eric Chi, North Carolina State University
2:20 PM
Duality of Graphical Models and Tensor Networks
Elina Robeva, University of British Columbia
2:45 PM
Floor Discussion
394 * !
Wed, 8/5/2020,
1:00 PM -
2:50 PM
Virtual
Challenges and New Directions in Precision Medicine for Large-Scale and Complex Data — Invited Papers
Biometrics Section , Section on Statistical Learning and Data Science, International Chinese Statistical Association, Biopharmaceutical Section
Organizer(s): Yichuan Zhao, Georgia State University
Chair(s): Min Qian, Columbia University
1:05 PM
Building Cancer Prognostic Models Generated via Automatic Data-Driven Sequential Processes
Hyokyoung (Grace) Hong, Michigan State University
1:30 PM
On Restricted Optimal Treatment Regime Estimation for Competing Risks Data
Jie Zhou, University of South Carolina; Jiajia Zhang, University of South Carolina; Wenbin Lu , North Carolina State University ; Xiaoming Li, University of South Carolina
1:55 PM
Depth Importance in Precision Medicine (DIPM): A Tree- and Forest-Based Method for Right-Censored Survival Outcomes
Victoria Chen, Yale University; Heping Zhang, Yale University
2:20 PM
Discussant: Yichuan Zhao, Georgia State University
2:40 PM
Floor Discussion