Contributed Poster Presentations: Section on Statistical Learning and Data Science -- JSM 2025

37: A Computational Framework for Enhancing 16S Sequencing Using Long-Read Phylogenetic Reference Data

10:30 AM - 12:20 PM

Co-authors: Xinran Qi (City of Hope), Xinran Qi (City of Hope), Yushu Shi, Robert Jenq (City of Hope), Antonio Gomes (City of Hope)

16S sequencing Long-read sequencing Phylogenetic reference tree Ancestral state reconstruction Microbiome analysis

38: A Variational Non-Asymptotic Testing Procedure for Causal Discovery

10:30 AM - 12:20 PM

Co-authors: Paolo Borello, Yixin Wang, Paolo Borello, Yian Ma (University of California San Diego)

Causal DIscovery KL Divergence Finite samples

39: A stochastic approach to k-nearest neighbors search using a fixed radius method

10:30 AM - 12:20 PM

Co-authors: Brahian Cano Urrego (University of Kansas Medical Center ASA Student Chapter), Brahian Cano Urrego (University of Kansas Medical Center ASA Student Chapter), Alexander Alsup, Jeffrey Thompson (Department of Biostatistics and Data Science at KUMC), Devin Koestler (University of Kansas Medical Center)

kNN search Computational efficiency fixed-radius search Branch and bound Machine learning

40: Optimal Sampling under Class Imbalance: A Kernel-Based IPW Estimator for Efficient Classification

10:30 AM - 12:20 PM

Co-authors: Hyelim Jung (Auburn University), Hyelim Jung (Auburn University), JooChul Lee (Auburn University)

Active learning Optimal sampling Imbalanced data Label noise Binary classification Semi-supervised learning

41: An interpretable and efficient autoregressive model for high-dimensional tensor-valued time series

10:30 AM - 12:20 PM

Co-authors: Yuxi CAI, Lan Li (NA), Lan Li (NA)

CP decomposition ENSO area detection High-dimensional time series data Low-rank plus sparse modeling Tensor autoregression

42: Basis Bayesian Additive Regression Trees

10:30 AM - 12:20 PM

Co-authors: Joshua Marvald, Tanzy Love (University of Rochester), Joshua Marvald, Angela Groves (University of Rochester)

Bayesian Additive Regression Trees BART Functional data

43: Comparative Time Series Analysis of the Temporal Fusion Transformer (TFT) and ARIMA Model

10:30 AM - 12:20 PM

Co-authors: Aaron Abromowitz, Aaron Abromowitz, Catherine Ticzon (Classmate)

Time Series Transformer Temporal Fusion Transformer (TFT)ARIMA Simulated data

44: Developing Models for Mortality and Community Discharge After Skilled Nursing Facility Admission

10:30 AM - 12:20 PM

Co-authors: Bocheng Jing, Bocheng Jing, John Boscardin (UCSF Medicine & Biostatistics), Siqi Gan, W. James Deardorff (Division of Geriatrics, University of California, San Francisco)

Prognostic Models Prediction Bayesian Machine Learning Skilled Nursing Facility

45: Exact Confidence Intervals for Randomization-Based Inference

10:30 AM - 12:20 PM

Co-authors: Biyonka Liang, Yash Nair (Stanford University), Biyonka Liang, Kelly Zhang (Columbia University), Lucas Janson (Harvard University)

Randomization testing Exact confidence intervals Semiparametric inference Finite-sample inference

46: Explainable Machine Learning to Assess the Value of Sustainable Housing

10:30 AM - 12:20 PM

Co-authors: Myriam Maumy-Bertrand (Universite De Technologie De Troyes), Elizaveta Logosha (Groupe Vivialys), Myriam Maumy-Bertrand (Universite De Technologie De Troyes), Frederic Bertrand (University of Technology of Troyes)

Machine Learning Shapley Values Green Effect Hedonic Regression Random Forests XGBoost

47: Extending Bayesian additive regression trees to handle non-stationary spatial data

10:30 AM - 12:20 PM

Co-authors: Hwanggwan Gwon, Hwanggwan Gwon, Veronica Berrocal (University of California, Irvine), Ana Kenney (UC Irvine)

Random Forest Non-stationary spatial data

48: Feature Selection for Latent Factor Models

10:30 AM - 12:20 PM

Co-authors: Rittwika Kansabanik (Florida State University), Adrian Barbu (UCLA), Rittwika Kansabanik (Florida State University)

Feature Selection Latent Factor Models Robust Loss Optimization Multiclass Classification Signal-to-Noise Ratio(SNR)

49: Functional Regression for SERS Spectrum Transformation Across Diverse Instruments

10:30 AM - 12:20 PM

Co-authors: Tao Wang, Tao Wang

Functional Regression spectrum transformation surface-enhanced Raman scattering

50: Generalized Tree-Informed Mixed Model Regression

10:30 AM - 12:20 PM

Co-authors: Jeremiah Allis, Xin Jin (The University of Tampa), Jeremiah Allis, Riddhi Pratim Ghosh (Bowling Green State Universty)

tree based regression clustered data mixed effects penalized quasi-likelihood stochastic gradient descent prediction

51: High-dimensional Functional Graphical Modeling in the Presence of Hidden Confounders

10:30 AM - 12:20 PM

Co-authors: Filippo Fiocchi (Cornell University), Y. Samuel Wang (Cornell University), Filippo Fiocchi (Cornell University)

Functional Data Analysis Graphical Models High-dimensional Statistics Structure Learning Hidden Confounders Covariance Estimator

52: Leveraging Large Language Models (LLMs) and Research-Based Prompts for Cleaning Unstructured Text

10:30 AM - 12:20 PM

Co-authors: Joshua Lambert (University of Cincinnati), Joshua Lambert (University of Cincinnati)

Data management Data cleaning Large Language Model (LLM)unstructured text data

54: Minimum-Norm Interpolation Under Covariate Shift

10:30 AM - 12:20 PM

Co-authors: Austin Zane (University of California, Berkeley), Austin Zane (University of California, Berkeley), Neil Mallinar (University of California, San Diego), Spencer Frei, Bin Yu (University of California at Berkeley)

high-dimensional statistics statistical learning distribution shift generalization deep learning theory transfer learning

55: Mitigating Data Imbalance in Credit Card Fraud Detection

10:30 AM - 12:20 PM

Co-authors: Chuqing Zhao (Harvard University), Yisong Chen, Chuqing Zhao (Harvard University), Chuanhao Nie, Yixin Xu

Fraud Detection Synthetic Data Machine Learning Neural Network Deep Learning

56: Optimal Shrinkage Estimation for Linear Discriminant Analysis in Ultra-high Dimension

10:30 AM - 12:20 PM

Co-authors: Wonjun Seo, Xiucai Ding, Wonjun Seo

Linear discriminant analysis Optimal shrinkage estimation Spiked model Random matrix theory

57: Sparse Functional Canonical Correlation Analysis for Multiview Data Integration

10:30 AM - 12:20 PM

Co-authors: Limeng Liu, Guannan Wang (College of William and Mary), Limeng Liu, Sandra Safo (University of Minnesota)

High-dimensional data Longitudinal data Microbiome data Sparsity Variable selection

58: Spatial Characterization of the Tumor Microenvironment using Self Supervised Learning

10:30 AM - 12:20 PM

Co-authors: Vincent Pisztora, Vincent Pisztora, Venkatraman Seshan (MSKCC), Colin Begg (Memorial Sloan-Kettering Cancer Center), Ronglai Shen (Memorial Sloan-Kettering Cancer Center)

Deep Learning Self Supervised Learning Gigapixel Imaging Cancer - Melanoma Image Segmentation Multiplexed Immunofluorescence

59: Statistical Inference for Latent Space Model on Network data with Edge Covariates

10:30 AM - 12:20 PM

Co-authors: Feifan Jiang (University of Michigan), Feifan Jiang (University of Michigan), Gongjun Xu (University of Michigan), Ji Zhu (University of Michigan)

latent space models maximum likelihood inference network with edge covariates

60: Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction

10:30 AM - 12:20 PM

Co-authors: Maximilian Schuessler (Stanford University School of Medicine), Maximilian Schuessler (Stanford University School of Medicine), Erik Sverdrup (Monash University), Robert Tibshirani (Stanford University)

Causal inference Heterogeneous treatment effects statistical learning

61: Using RNN to analyze electricity residential load curves and perform short-term prediction

10:30 AM - 12:20 PM

Co-authors: Frederic Bertrand (University of Technology of Troyes), Fatima Fahs (Electricity of Strasbourg (France)), Frederic Bertrand (University of Technology of Troyes), Myriam Maumy-Bertrand (Universite De Technologie De Troyes)

Machine Learning Deep Learning RNN-LSTM Electricity load curves Short-term individual prediction

Contributed Poster Presentations: Section on Statistical Learning and Data Science

24 Presentations