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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
1: 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
2: Exploring Efficacious FDA Approved Drugs and Their Subgroups in a Large Claims Database for Protection Against COVID-19
Joshua W Lambert, University of Cincinnati
3: 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
4: 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
5: Sparse Bayesian Expectation-Maximization Algorithm for High-Dimensional Linear Mixed Models
Anja Zgodic, University of South Carolina; Alexander McLain, University of South Carolina
6: 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
7: 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
8: 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
9: 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