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
395 * !
Wed, 8/5/2020,
1:00 PM -
2:50 PM
Virtual
The Need for Interpretable and Fair Algorithms in Health Policy — Invited Panel
Health Policy Statistics Section , Biometrics Section, Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Sherri Rose, Harvard Medical School
Chair(s): Laura Hatfield, Harvard Medical School
1:05 PM
The Need for Interpretable and Fair Algorithms in Health Care and Policy
Presentation
Panelists:
Marzyeh Ghassemi, University of Toronto
Melody Goodman, NYU
Sherri Rose, Harvard Medical School
Julius Adebayo, MIT
2:40 PM
Floor Discussion
398 * !
Wed, 8/5/2020,
1:00 PM -
2:50 PM
Virtual
Beyond Traditional Approaches: Evolving Artificial Intelligence and Machine Learning to Advance Clinical Research and Drug Development — Topic Contributed Papers
Biometrics Section , Biopharmaceutical Section, Section on Statistical Learning and Data Science, Text Analysis Interest Group
Organizer(s): Demissie Alemayehu, Pfizer Inc.
Chair(s): Birol Emir, Pfizer Inc.
1:05 PM
Adaptive Online Machine Learning for Real-Time Individualized Forecasting in Clinician-AI Team
Rachael Phillips, University of California, Berkeley ; Mark Van der Laan, University of California, Berkeley
1:25 PM
Recent Advances in the Application of Natural Language Processing to Unstructured and Semi-Structured Data in the Pharmaceutical Industry
Presentation
Peter Henstock, Pfizer Inc
1:45 PM
Learning Decision Rules with Observational Data
Xinkun Nie, Stanford University ; Stefan Wager, Stanford University
2:05 PM
“Data Nuggets” Tools for Analyzing Big Data
Javier Cabrera, Rutgers University
2:25 AM
Floor Discussion
403 * !
Wed, 8/5/2020,
1:00 PM -
2:50 PM
Virtual
Sufficient Dimension Reduction and Variable Selection for High-Dimensional Inference — Topic Contributed Papers
Section on Nonparametric Statistics , Section on Statistical Learning and Data Science, International Chinese Statistical Association
Organizer(s): Wenbo Wu, University of Texas at San Antonio
Chair(s): Wenbo Wu, University of Texas at San Antonio
1:05 PM
Principal Asymmetric Least Squares for Sufficient Dimension Reduction
Yuexiao Dong, Temple University ; Abdul-Nasah Soale, Temple University
1:25 PM
On Sufficient Dimension Reduction for Functional Data via Weak Conditional Moments
Jun Song, UNC Charlotte ; Bing Li, Pennsylvania State University
1:45 PM
Sufficient Variable Selection via Expected Conditional Hilbert-Schmidt Independence Criterion
Chenlu Ke, Virginia Commonwealth University
2:05 PM
On Sufficient Dimension Reduction with Mixture Normally Distributed Predictors
Wei Luo, Zhejiang University
2:25 PM
Floor Discussion
406 * !
Wed, 8/5/2020,
1:00 PM -
2:50 PM
Virtual
Advances of Statistical Methodologies in Proteogenomics Research — Topic Contributed Papers
Section on Statistics in Genomics and Genetics , Biometrics Section, Section on Statistical Learning and Data Science
Organizer(s): Shrabanti Chowdhury, Icahn school of Medicine at Mount Sinai
Chair(s): Pei Wang, Icahn Medical School at Mount Sinai
1:05 PM
Statistical Characterization of Quantitative Changes in Global Post-Translational Modification Profiling Experiments
Tsung-Heng Tsai, Kent State University ; Erik Verschueren, Genentech, Inc.; Olga Vitek, Northeastern University
1:25 PM
Repulsive Mixtures for the Classification of Single Cells
Francesca Petralia, Icahn School of Medicine At Mount Sinai
1:45 PM
Extracting Biological Information from Extreme Values in Proteogenomics Data
Wenke Liu ; Kelly Ruggles, NYU school of medicine; David Fenyo, NYU school of medicine
2:05 PM
Sparse Multiple Co-Inertia Analysis with Application to Integrative Analysis of Multi -Omics Data
Eun Jeong Min, Univerisity of Pennsylvania ; Qi Long, University of Pennsylvania
2:25 PM
A New Molecular Signature Method for Prediction of Driver Cancer Pathways from Transcriptional Data
Boris Reva
2:45 AM
Floor Discussion
412 * !
Wed, 8/5/2020,
1:00 PM -
2:50 PM
Virtual
Incorporation of Real-World Evidence in Clinical Trial Designs and Associated Statistical Methodologies — Topic Contributed Papers
Section for Statistical Programmers and Analysts , Biopharmaceutical Section, Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Jeffrey Joseph, Covance; Swarna Reddy, Covance
Chair(s): Swarna Reddy, Covance
1:05 PM
Using Real World Data to Evaluate the Generalizability of Evidence from Randomized Clinical Trials
Wei Shen, Eli Lilly and Company ; Douglas Faries, Lilly Research Laboratories; Mark Belger, Eli Lilly and Company; Chen-Yen Lin, Eli Lilly and Company
1:25 PM
Real World Evidence Use at CBER: Emerging Issues from Submissions
Jennifer Kirk, FDA, Center for Biologics Evaluation and Research (CBER)
1:45 PM
Data Standardization to Facilitate Comparison of Real World Data and Clinical Trial Data
Aaron Galaznik, Acorn AI, a Medidata Company
2:05 PM
Analytic Considerations for Constructing Real-World Control Arms
Katherine Tan, Flatiron Health ; Brian Segal, Flatiron Health; Jonathan Bryan, Flatiron Health; Melissa Curtis, Flatiron Health; Nathan Nussbaum, Flatiron Health; Rebecca Miksad, Flatiron Health; Meghna Samant, Flatiron Health; Somnath Sarkar, Flatiron, Inc.; Aracelis Torres, Flatiron Health
2:25 PM
Leverage Real World Evidence in Drug Development and Regulatory Decision Making
Rongmei Zhang, FDA, Center for Drug Evaluation and Research (CDER).
2:45 PM
Floor Discussion
417 * !
Wed, 8/5/2020,
1:00 PM -
2:50 PM
Virtual
Best Practices for Preparing Students for a Career in Business Analytics/Data Science — Topic Contributed Panel
Business Analytics/Statistics Education Interest Group , Section on Statistical Learning and Data Science, Business and Economic Statistics Section
Organizer(s): Amy L Phelps, Duquesne University
Chair(s): George Recck, Babson University
1:05 PM
Best Practices for Preparing Students for a Career in Business Analytics/Data Science
Presentation
Panelists:
Sudipta Dasmohapatra, Duke University
Michael Posner, Villanova University
Roger Hoerl, Union College
Kimberly Hockman, Genesis Equipping Ministries
2:45 PM
Floor Discussion
421 !
Thu, 8/6/2020,
10:00 AM -
11:50 AM
Virtual
Recent Advances in Time Series and Temporal Data Analysis — Invited Papers
Business and Economic Statistics Section , Section on Statistical Learning and Data Science, Social Statistics Section
Organizer(s): Shujie Ma, University of California, Riverside
Chair(s): Shujie Ma, University of California, Riverside
10:05 AM
Semiparametric Modeling of Structured Point Processes Using Multi-Level Log-Gaussian Cox Processes
Yongtao Guan, University of Miami
10:30 AM
Nonparametric Standard Errors for High Frequency Data
Per Mykland, University of Chicago ; Lan Zhang, University of Illinois at Chicago
10:55 AM
Relevant Two-Sample Tests for the Eigenfunctions of Covariance Operators
Presentation
Alexander Aue, University of California-Davis
11:20 AM
Smoothed Log-Concave Probability Mass Functions with Application to Time-Series of Counts
Thibault Vatter, Columbia University
11:45 AM
Floor Discussion
429 * !
Thu, 8/6/2020,
10:00 AM -
11:50 AM
Virtual
Strategic Statistical Partnerships Impacting Health and Education for the Public Good — Invited Papers
Stats. Partnerships Among Academe Indust. & Govt. Committee , Health Policy Statistics Section, Section on Statistical Learning and Data Science
Organizer(s): Ying Ding, University of Pittsburgh; Pamela McGovern, USDA National Agricultural Statistics Service; Fanni Natanegara, Eli Lilly and Company
Chair(s): Jungwha Julia Lee, Northwestern University
10:05 AM
Public-Private Collaborations Aiming to Improve the Reliability and Consistency of Clinical Tests to Guide Cancer Care
Lisa Meier McShane, National Cancer Institute
10:25 AM
Furthering Public Health Through the Fred Hutchinson Cancer Research Center/Sanofi Pasteur Dengue Vaccine Collaboration
Zoe Moodie, Fred Hutchinson Cancer Research Center; Peter Gilbert, Fred Hutchinson Cancer Research Center; Michal Juraska, Fred Hutchinson Cancer Research Center; Ying Huang, Fred Hutchinson Cancer Research Center ; Youyi Fong, Fred Hutchinson Cancer Research Center; Brenda Price, University of Washington; Carlos DiazGranados, Sanofi Pasteur; Stephen Savarino, Sanofi Pasteur; Saranya Sridhar, Sanofi Pasteur; Edith Langevin, Sanofi Pasteur; Tifany Machabert, Sanofi Pasteur; Ming Zhu, Sanofi Pasteur
10:45 AM
An Academic and Industry Partnership Training the Next Generation of Data Scientists
Presentation
Amy Wagaman, Amherst College; Nicholas Horton, Amherst College
11:05 AM
Engaging Industry and Academia to Drive Meaningful Social Impact
Christine Pfeil, MassMutual ; Sears Merritt, MassMutual
11:25 AM
Discussant: Sally Morton, Virginia Tech
11:45 AM
Floor Discussion
438
Thu, 8/6/2020,
10:00 AM -
11:50 AM
Virtual
Statistical Methods for Topological Data Analysis — Topic Contributed Papers
Section on Statistical Learning and Data Science , Korean International Statistical Society, IMS
Organizer(s): Chul Moon, Southern Methodist University
Chair(s): Hengrui Luo, The Ohio State University
10:05 AM
Solution Manifold and Its Statistical Applications
Yen-Chi Chen, University of Washington
10:25 AM
Persistent Topological Descriptors for Functional Brain Network
Hyunnam Ryu, University of Georgia ; Nicole Lazar, University of Georgia
10:45 AM
Uncovering the Holes in the Universe with Topological Data Analysis
Jessi Cisewski-Kehe, Yale University
11:05 AM
Confidence Band for Persistent Homology
Presentation
Jisu Kim, Inria
11:25 AM
Discussant: Chul Moon, Southern Methodist University
11:45 AM
Floor Discussion
444 !
Thu, 8/6/2020,
10:00 AM -
11:50 AM
Virtual
Highlights from the Journal STAT — Topic Contributed Papers
SSC (Statistical Society of Canada) , International Statistical Institute, Section on Statistical Learning and Data Science, WNAR
Organizer(s): Hao Helen Zhang, University of Arizona
Chair(s): Hao Helen Zhang, University of Arizona
10:05 AM
Baum-Welch Algorithm on Directed Acyclic Graph for Mixtures with Latent Bayesian Networks
Jia Li, Penn State University ; Lin Lin, The Pennsylvania State University
10:25 AM
Penalized Euclidean Distance Regression
Daniel Vasiliu, College of William & Mary ; Ian Dryden, University of Nottingham; Tanujit Dey, Harvard Medical School and Brigham and Women's Hospital
10:45 AM
Syncytial Clustering
Ranjan Maitra, Iowa State University ; Israel A. Almodovar-Rivera, University of Puerto Rico
11:05 AM
Clustering and Bi-Clustering for High-Frequency Financial Time Series Based on Mutual Information
Jian Zou, Worcester Polytechnic Institute ; Haitao Liu, Worcester Polytechnic Institute; Nalini Ravishanker, University of Connecticut
11:25 AM
From Causal Inference to Gene Regulation
Caroline Uhler, Massachusetts Institute of Technology
11:45 AM
Floor Discussion
448 !
Thu, 8/6/2020,
10:00 AM -
11:50 AM
Virtual
The Contribution of Convex Optimization to New Statistical Concepts — Topic Contributed Papers
Section on Statistical Computing , Section on Statistical Graphics, Section on Statistical Learning and Data Science
Organizer(s): Michael G. Schimek, IMI Statistical Bioinformatics, Medical University of Graz, Austria
Chair(s): Michael G. Schimek, IMI Statistical Bioinformatics, Medical University of Graz, Austria
10:05 AM
Supervised Convex Clustering
Tianyi Yao, Rice University ; Minjie Wang, Rice University; Genevera Allen, Rice University
10:25 AM
A Computational Framework for Multivariate Convex Regression
Rahul Mazumder, Massachusetts Institute of Technology
10:45 AM
TopKSignal: A Convex Optimization Tool for Signal Reconstruction from Multiple Ranked Lists
Bastian Pfeifer, IMI Statistical Bioinformatics, Medical University of Graz, Austria ; Luca Vitale, Medical University of Graz, Austria; University of Salerno, Salerno, Italy; Michael G. Schimek, IMI Statistical Bioinformatics, Medical University of Graz, Austria
11:05 AM
Novel Results on the Sorted L-One Penalized Estimator
Presentation
Malgorzata Bogdan, University of Wroclaw
11:25 AM
Discussant: David W. Scott, Rice University, Department of Statistics, Noah Harding Chair
11:45 AM
Floor Discussion
455 * !
Thu, 8/6/2020,
10:00 AM -
11:50 AM
Virtual
Big Data, Technology Platform and Digital Innovation with Measurable Impact — Topic Contributed Panel
Text Analysis Interest Group , Section on Statistical Learning and Data Science, Quantum Computing in Statistics and Machine Learning, Section on Statistical Computing
Organizer(s): Kelly Zou, Pfizer Inc
Chair(s): Kelly Zou, Pfizer Inc
10:05 AM
Big Data, Technology Platform and Digital Innovation with Measurable Impact
Panelists:
Siddhartha Dalal, Columbia University
Mike Henderson, SAS
Joseph Imperato, Pfizer
Stanislav Kolenikov, Abt Associates
Lourenco Miranda, Society Generale
Mike Porath, The Mighty
May Yamada-Lifton, SAS
11:45 AM
Floor Discussion
495 !
Thu, 8/6/2020,
10:00 AM -
2:00 PM
Virtual
Statistical Methods for Networks — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Adam Rothman, University of Minnesota
Estimation of the Epidemic Branching Factor in Noisy Contact Networks
Wenrui Li, Boston University ; Eric Kolaczyk, Boston University; Daniel L Sussman, Boston University
Matrix Factorization Methods for Community Detection in Dynamic Networks
Yan Liu, University of Illinois at Urbana-Champaign ; Yuguo Chen, University of Illinois at Urbana-Champaign
A Bayesian Nonparametric Latent Space Approach for Modeling Evolving Communities in Dynamic Networks
Joshua Loyal, University of Illinois at Urbana-Champaign ; Yuguo Chen, University of Illinois at Urbana-Champaign
Consistent Nonparametric Hypothesis Testing for Low Rank Random Graphs with Negative or Repeated Eigenvalues
Joshua Agterberg, Johns Hopkins University ; Minh Tang, NC State University; Carey Priebe, Johns Hopkins University; Mao Hong, Johns Hopkins University
A Statistical Interpretation of Spectral Embedding: The Generalized Random Dot Product Graph
Patrick Rubin-Delanchy, University of Bristol; Joshua Cape, University of Michigan ; Minh Tang, NC State University; Carey Priebe, Johns Hopkins University
Dynamic Latent Space Network Models with Attractors for Flocking and Polarization
Xiaojing Zhu, Boston University ; Eric Kolaczyk, Boston University; Konstantinos Spiliopoulos, Boston University; Dylan Walker, Boston University
Spectral Filtering for Core Structure Identification in Complex Networks
Ruizhong Miao, University of Virginia ; Tianxi Li, University of Virginia
496 !
Thu, 8/6/2020,
10:00 AM -
2:00 PM
Virtual
Dimension Reduction — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Aramayis Dallakyan, Texas A&M University
Bayesian Model Averaging Sufficient Dimension Reduction
Michael Declan Power, Temple University ; Yuexiao Dong, Temple University
Principal Curve Approaches for Inferring 3D Chromatin Architecture
Presentation
Elena Tuzhilina, Stanford University, Department of Statistics ; Trevor Hastie, Stanford University, Department of Statistics ; Mark Segal, UCSF, Department of Epidemiology and Biostatistics
On Sufficient Dimension Reduction via Principal Asymmetric Least Squares
Abdul-Nasah Soale, Temple University ; Yuexiao Dong, Temple University
Learning Hierarchical Structures in Latent Attribute Models
Chenchen Ma, University of Michigan ; Gongjun Xu, University of Michigan
A Supervised Framework for Linear Dimension Reduction Induced by Hypothesis Testing
Kisung You, University of Notre Dame ; Lizhen Lin, University of Notre Dame
Canonical Correlation Analysis and Fusion Methods on a Large Face Database for Computer Vision
Cuixian Chen, University of North Carolina, Wilmington ; Jasmine Gaston, University of North Carolina Wilmington; Summerlin Thompson, University of North Carolina Wilmington; Suhaela Eledkawi, Wright State University; Caroline Werther, University of North Carolina Wilmington; Yaw Chang, University of North Carolina Wilmington; Yishi Wang, University of North Carolina Wilmington; Guodong Guo, West Virginia University
497 !
Thu, 8/6/2020,
10:00 AM -
2:00 PM
Virtual
Variable Selection — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Guo Yu, Cornell
Controlled Group Variable Selection Using a Model-Free Knockoff Filter with a Generative Adversarial Networks (GANs) Generator
Xinran Qi, Medical College of Wisconsin
The Sylvester Graphical Lasso (SyGlasso)
Yu Wang, University of Michigan ; Byoungwook Jang, University of Michigan; Alfred Hero, University of Michigan
Feature Screening for High-Dimensional Quadratic Generalized Linear Model via Point Biserial Correlation
Jinzhu Jiang, Bowling Green State University ; Junfeng Shang, Bowling Green State University
Simultaneous Outlier Detection and Feature Selection Using Mixed-Integer Programming
Ana Kenney, Pennsylvania State University ; Luca Insolia, Scuola Normale Superiore (Pisa Italy); Francesca Chiaromonte, Pennsylvania State University and Sant’Anna School of Advanced Studies (Pisa Italy); Giovanni Felici, IIASI CNR
Cluster Group Variable Selection Method for High-Dimensional Data
Qingcong Yuan, Miami University ; Zhiyuan Li, Miami University
Structure Adaptive Lasso
Presentation
Sandipan Pramanik, Texas A&M University ; Xianyang Zhang, Texas A&M University
498
Thu, 8/6/2020,
10:00 AM -
2:00 PM
Virtual
Modern Machine Learning — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Rebecca North, North Carolina State University
Random Forest Kernels: Utility and Insights for Interpretable Statistical Learning
Dai Feng, AbbVie ; Richard Baumgartner, Merck
Uniform Regret Bounds for Quantile Regression Tree Process in Offline and Online Settings
Fei Fang, Duke University ; Alexandre Belloni, Duke University
Deep Learning with Gaussian Differential Privacy
Zhiqi Bu, University of Pennsylvania
An Optimal Statistical and Computational Framework for Generalized Tensor Estimation
Rungang Han, University of Wisconsin-Madison ; Rebecca Willett, University of Chicago; Anru Zhang, University of Wisconsin-Madison
Nonparametric Individual Treatment Effect Estimation for Survival Data with Random Forests
Denis Larocque, HEC Montreal ; Sami Tabib, HEC Montreal
Sequential Changepoint Detection for Classifier Label Shift
Presentation
Ciaran Evans, Carnegie Mellon University ; Max G'Sell, Carnegie Mellon University
Machine Learning Oracle to Guide Statistical Data Processing
Lucas Koepke, National Institute of Standards and Techology ; Michael Frey, National Institute of Standards and Technology
499
Thu, 8/6/2020,
10:00 AM -
2:00 PM
Virtual
New Methods for Machine Learning — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Dawei Liu, Biogen
Transfer Learning in High-Dimensional Sparse Regression: Estimation, Prediction, and Minimax Optimality
Sai Li, University of Pennsylvania ; Hongzhe Li, University of Pennsylvania; Tony Cai, University of Pennsylvania
Large-Scale Anomaly Detection Based on Ensemble Learning
Xi Zhang, Huawei Technologies ; Kai Chen, Huawei Technologies; Zhihua Lv, Huawei Technologies; Rongjun Xu, Huawei Technologies
Random Forest-Classification of Area Socioeconomic Status (SES) and Mortality Risk in Pediatric Acute Lymphoblastic Leukemia (ALL) in the US
Fatima Boukari, Delaware State University ; Hacene Boukari, Delaware State University; Md Hossain, Nemours Biomedical Research, A.I. DuPont Children's Hospital
Augmented Bagging as an Alternative to Random Forests
Siyu Zhou, University of Pittsburgh ; Lucas Mentch, University of Pittsburgh
Machine Learning Techniques for Prediction of Retail Violation of Tobacco Products
Adams Kusi Appiah, University of Nebraska Medical Center ; Hongying Dai, University of Nebraska Medical Center
500
Thu, 8/6/2020,
10:00 AM -
2:00 PM
Virtual
Statistical Learning — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Linghui Li, AstraZeneca
Effects of Stopping Criterion in the Growth of Trees in Regression Random Forests
Aryana Arsham, National Cancer Institute ; Philip Rosenberg, National Cancer Institute; Mark Peter Little, National Cancer Institute
Using Machine Learning to Improve Propensity Score Matching Methods in Observational Studies
Nan Zhang, Imperial College London ; Daniel J. Graham, Imperial College London
Inference for BART with Multinomial Outcomes
Yizhen Xu, Brown University ; Rami Kantor, Brown University; Ann Mwangi, Moi University; Michael Daniels, University of Florida; Joseph Hogan, Brown University
Privacy-Preserving Distributed Learning from Electronic Health Records Across Multiple Heterogenous Clinical Sites
Jiayi Tong, University of Pennsylvania ; Chongliang Luo, University of Pennsylvania; Rui Duan, University of Pennsylvania; Mackenzie Edmondson, University of Pennsylvania; Christopher Forrest, Children's Hospital of Philadelphia; Yong Chen, University of Pennsylvania
When Black Box Algorithms Are (Not) Appropriate: A Principled Prediction-Problem Ontology
Jordan Rodu, University of Virginia ; Michael Baiocchi, Stanford University
Semi-Supervised Logistic Learning Based on Exponential Tilt Mixture Models
Xinwei Zhang, Rutgers University ; Zhiqiang Tan, Rutgers University
501
Thu, 8/6/2020,
10:00 AM -
2:00 PM
Virtual
Time Series Methods — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Ignacio Segovia-Dominguez, The University of Texas at Dallas
Dynamic structure estimation of time-varying networks
Presentation
Ahyoung Amy Kim, The University of Arizona ; Hongseok Ko, The University of Arizona; Xueying Tang; Hao Helen Zhang, University of Arizona
Fast Functional Change Point Detection with Total Variation Denoising
Trevor Harris, University of Illinois at Urbana-Champaign ; Bo Li, University of Illinois at Urbana-Champaign; Derek Tucker, Sandia National Lab
A Dynamic Neural ODE Model for Nonlinear Time Series
Yijia Liu, Purdue University ; Lexin Li, University of California, Berkeley; Xiao Wang, Purdue University
Modeling Spiky Functional Data with Derivatives of Smooth Functions in Function-On-Function Regression
Ruiyan Luo
Completion of Partially Observed Curves with Application to Classification of Bovid Teeth
Gregory Matthews, Loyola University Chicago ; Ofer Harel, University of Connecticut; George Thiruvathukal, Loyola University Chicago; Juliet Brophy, Louisiana State University; Sebastian Kurtek, The Ohio State University; Karthik Bharath, University of Nottingham
High-Dimensional Dynamic Pricing with Online Tuning
Chi-Hua Wang ; Zhanyu Wang, Purdue University; Wei Sun, Purdue University; Guang Cheng, Purdue University
524 * !
Thu, 8/6/2020,
1:00 PM -
2:50 PM
Virtual
Emerging Statistical Learning Methods in Modern Data Science — Invited Papers
Section on Statistical Learning and Data Science , Section on Nonparametric Statistics, IMS
Organizer(s): Ping Ma, University of Georgia
Chair(s): Ping Ma, University of Georgia
1:05 PM
A Powerful AI Tool for CHD Screening
Wenxuan Zhong, Department of Statistics, University of Georgia
1:30 PM
A Community Model for Partially Observed Networks from Surveys
Tianxi Li, University of Virginia ; Elizaveta Levina, University of Michigan; Ji Zhu, University of Michigan
1:55 PM
Reverse Engineering a Deep Network
Presentation
Douglas Nychka, Colorado School of Mines
2:20 PM
Statistical Methods for Some Problems in Physics
Larry Wasserman, Carnegie Mellon University
2:45 PM
Floor Discussion
538 !
Thu, 8/6/2020,
1:00 PM -
2:50 PM
Virtual
Emerging Topics in Private Data Analysis — Topic Contributed Papers
IMS , Section on Statistical Learning and Data Science, Royal Statistical Society
Organizer(s): Weijie Su, University of Pennsylvania
Chair(s): Weijie Su, University of Pennsylvania
1:05 PM
Differentially Private Mean and Covariance Estimation
Presentation
Gautam Kamath, University of Waterloo
1:25 PM
KNG: The K-Norm Gradient Mechanism
Jordan Awan, Penn State University ; Matthew Reimherr, Penn State University
1:45 PM
Locally Private Learning, Estimation, Inference and Optimality
Feng Ruan, University of California at Berkeley
2:05 PM
Gaussian Differential Privacy, with Applications to Deep Learning
Jinshuo Dong, University of Pennsylvania ; Aaron Roth, University of Pennsylvania; Weijie Su, University of Pennsylvania
2:25 PM
Discussant: Xiao-Li Meng, Harvard University
2:45 PM
Floor Discussion
546
Thu, 8/6/2020,
1:00 PM -
2:50 PM
Virtual
Foundational Issues in Machine Learning — Topic Contributed Papers
Section on Statistical Learning and Data Science , Uncertainty Quantification in Complex Systems Interest Group, Section on Risk Analysis, Section on Statistical Computing
Organizer(s): Bertrand Clarke, U. Nebraska-Lincoln
Chair(s): Tri Le, Mercer Univ - Atlanta
1:05 PM
Joint Robust Multiple Inference on Large-Scale Multivariate Regression
Wen Zhou, Colorado State University ; Wenxin Zhou, University of California, San Diego; Youngseok Song, Colorado State University
1:25 PM
BET on Independence
Kai Zhang, UNC Chapel Hill
1:45 PM
Sparse Logistic Classification Using Multi-Type Predictors
Arkaprava Roy, University of Florida ; Bertrand Clarke, U. Nebraska-Lincoln; Subhashis Ghosal, NCSU; Diego Jarquin, UNL
2:05 PM
Floor Discussion
548 * !
Thu, 8/6/2020,
1:00 PM -
2:50 PM
Virtual
Using Artificial Intelligence and Advanced Statistical Methods to Improve Official Statistics — Topic Contributed Papers
Government Statistics Section , Section on Statistical Learning and Data Science, Survey Research Methods Section
Organizer(s): Barry W Johnson, Statistics of Income, Internal Revenue Service
Chair(s): Barry W Johnson, Statistics of Income, Internal Revenue Service
1:05 PM
Recommender Algorithms for Form Anomaly Detection
William Roberts, Deloitte ; Anne Parker, Internal Revenue Service; Danielle Gewurz, Deloitte
1:25 PM
Statistically Robust Estimation of Unprotected Identity Theft in Individual Tax Returns: A Non-Parametric Simulation Based Approach
Sabyasachi Guharay, US Internal Revenue Service
1:45 PM
NAICS Code Prediction Using Supervised Methods
Presentation
Anne Parker, Internal Revenue Service; Evan Schulz, Internal Revenue Service; Christine Oehlert, Internal Revenue Service
2:05 PM
An Active Learning Approach for Collecting Tax Revenue
Kristen Altenburger, Stanford University; Brandon Anderson, Stanford University; Ben Chugg, Stanford University; Jacob Goldin, Stanford University; Daniel En-Wenn Ho, Stanford Law School ; Ahmad Qadri, Internal Revenue Service; Evelyn Smith, University of Michigan
2:25 PM
Discussant: Karl Branting, MITRE Corporation
2:45 PM
Floor Discussion
553 * !
Thu, 8/6/2020,
3:00 PM -
4:50 PM
Virtual
Improving Patient Care Through Personalized/Stratified Medicine: Modern Data Science and Perspectives from Pharmaceutical Industry Leads and Regulatory — Invited Papers
Biopharmaceutical Section , Section on Statistical Learning and Data Science, Section on Statistics and Data Science Education
Organizer(s): Wen Li, Merck
Chair(s): Ilya Lipkovich, Eli LIlly and Company
3:05 PM
Machine Learning, AI and Personalized Intervention
Haoda Fu, Eli Lilly and Company
3:30 PM
Pruned Targeted-Learning for Personalized Medicine
Yixin Fang, AbbVie Inc.
3:55 PM
PRISM: Patient Response Identifiers for Stratified Medicine
Thomas Jemielita, Merck & Co., Inc ; Devan Mehrotra, Merck
4:20 PM
Discussant: Maria Matilde S. Kam, OTS/CDER, FDA
4:45 PM
Floor Discussion
559 * !
Thu, 8/6/2020,
3:00 PM -
4:50 PM
Virtual
Foundations of Data Science: The TRIPODS Experience — Invited Papers
Section on Statistical Learning and Data Science , Committee on Funded Research, Section on Bayesian Statistical Science, Section on Physical and Engineering Sciences, Quality and Productivity Section
Organizer(s): Scott H. Holan, University of Missouri
Chair(s): Catherine Calder, University of Texas at Austin
3:05 PM
Build a Data Science Team
Hao Helen Zhang, University of Arizona
3:25 PM
Transdisciplinary Research Institute for Advancing Data Science (TRIAD @ Georgia Tech)
Xiaoming Huo, Georgia Institute of Technology
3:45 PM
Foundations of Data Science: Dynamical, Statistical and Economic Perspectives
Presentation
Michael I. Jordan, University of California, Berkeley
4:05 PM
Riemannian Embedding Models for Relational Data
Abel Rodriguez, University of California, Santa Cruz
4:45 PM
Floor Discussion
580 * !
Thu, 8/6/2020,
3:00 PM -
4:50 PM
Virtual
Statistical and Computational Challenges in Nonparametric Learning — Topic Contributed Papers
Section on Nonparametric Statistics , Section on Statistical Learning and Data Science, Section on Statistical Computing
Organizer(s): Lingzhou Xue, Penn State University and NISS
Chair(s): Lingzhou Xue, Penn State University and NISS
3:05 PM
A Scale Invariant Approach for Sparse Signal Recovery
Yifei Lou, University of Texas At Dallas
3:25 PM
Learning Non-Monotone Optimal Individualized Treatment Regimes
Trinetri Ghosh, Pennsylvania State University ; Yanyuan Ma, The Pennsylvania State University ; Wensheng Zhu, Northeast Normal University
3:45 PM
Nonparametric Screening Under Conditional Strictly Convex Loss for Ultrahigh Dimensional Sparse Data
Xu Han, Temple University
4:05 PM
A Screening Algorithm for Cross-Validated Kernel Support Vector Machines
Boxiang Wang, University of Iowa ; Yi Yang, McGill University
4:25 PM
Floor Discussion
581 * !
Thu, 8/6/2020,
3:00 PM -
4:50 PM
Virtual
Advanced Cross-Disciplinary Statistical Methods in Statistical Genomics — Topic Contributed Papers
Section on Statistics in Genomics and Genetics , Section on Statistics in Epidemiology, Section on Statistical Learning and Data Science
Organizer(s): Suvo Chatterjee, National Institute of Child Health and Development (NICHD)/ National Institutes of Health
Chair(s): Shrabanti Chowdhury, Icahn school of Medicine at Mount Sinai
3:05 PM
Utilizing Patient Information to Identify Subtype Heterogeneity of Cancer Driver Genes
Bin Zhu, NCI ; Ho-Hsiang Wu, Food and Drug Administration; Xing Hua, Fred Hutchinson Cancer Research Center; Jianxin Shi, National Cancer Institute; Nilanjan Chatterjee, Johns Hopkins University
3:25 PM
Bayesian Functional Data Analysis Over Dependent Regions and Its Application for Identification of Differentially Methylated Regions
Suvo Chatterjee, National Institute of Child Health and Development (NICHD)/ National Institutes of Health ; Shrabanti Chowdhury, Icahn school of Medicine at Mount Sinai; Duchwan Ryu, Northern Illinois University; Fasil Tekola Ayele, NICHD/NIH
3:45 PM
A Bayesian Precision Medicine Framework for Calibrating Individualized Therapeutic Indices in Cancer
ABHISEK SAHA, NICHD, NIH ; Veera Baladandayuthapani, University of Michigan; Min Jin Ha, UT MD Anderson Cancer Center
4:05 PM
Multi-Resolution Clustering of Omics Data for Pattern Discovery
Presentation
Ali Rahnavard, George Washington University
4:25 PM
Multiomics Analysis of the Immunome, Transcriptome, Microbiome, Proteome, and Metabolome in Pregnancy
Nima Aghaeepour, Stanford University - Stanford, CA
4:45 PM
Floor Discussion
583 !
Thu, 8/6/2020,
3:00 PM -
4:50 PM
Virtual
Learning Network Structure in Heterogeneous Populations — Topic Contributed Papers
Section on Statistical Learning and Data Science , Royal Statistical Society, International Indian Statistical Association
Organizer(s): Sandipan Roy, University of Bath
Chair(s): Sandipan Roy, University of Bath
3:05 PM
Modeling Network Time Series Using Generalized Network AutoRegression (GNAR)
Kathryn Leeming, University of Warwick ; Marina Knight, University of York; Guy Nason, Imperial College London; Matthew Nunes, University of Bath
3:25 PM
Sparse Locally-Stationary Wavelet Processes
Alexander Gibberd, Lancaster Unviersity
3:45 PM
Efficient Estimation of Change Points in Regime Switching Dynamic Markov Random Fields
Jing Ma, Texas A&M University
4:05 PM
Fast Algorithms for Detection of Structural Breaks in High-Dimensional Data
George Michailidis, University of Florida
4:25 PM
Optimistic Binary Segmentation with an Application in Change Point Detection Methodologies for Graphical Models in the Presence of Missing Values
Solt Kovács, ETH Zurich ; Peter Bühlmann, ETH Zurich; Lorenz Haubner, ETH Zurich; Housen Li, University of Göttingen; Malte Londschien, ETH Zurich; Axel Munk, University of Göttingen
4:45 PM
Floor Discussion
585
Thu, 8/6/2020,
3:00 PM -
4:50 PM
Virtual
Bayesian Neural Networks — Topic Contributed Papers
International Society for Bayesian Analysis (ISBA) , Section on Bayesian Statistical Science, Section on Statistical Learning and Data Science, Text Analysis Interest Group
Organizer(s): Deborshee Sen, Duke University
Chair(s): Rudradev Sengupta, Janssen Pharmaceutical Companies of Johnson and Johnson, Beerse, Belgium
3:05 PM
Bayesian Dimension Reduction Using Neural Networks
Presentation
Deborshee Sen, Duke University ; David Dunson, Duke University; Theodore Papamarkou, Oak Ridge National Laboratory
3:25 PM
Bayesian Deep Net GLM and GLMM
Robert Kohn, University of New South Wales; Minh Ngoc Tran, The University of Sydney
3:45 PM
Challenges in Bayesian Inference via Markov Chain Monte Carlo for Neural Networks
Theodore Papamarkou
4:05 PM
Practical Bayesian Inference for Shallow CNNs in NLP
Jacob Hinkle, Oak Ridge National Lab ; Devanshu Agrawal, Oak Ridge National Lab; Theodore Papamarkou, Oak Ridge National Laboratory
4:25 PM
Floor Discussion