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