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Loyola University Chicago

Academic institutionnorthamerica · us
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Research library18linked papers
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Selected work

Representative Papers

A Unified Geometric Framework for Developmental Analysis of Spatial Transcriptomic Data

Aug 15, 2026

This study addresses the challenge of reconstructing developmental trajectories from destructive spatial transcriptomics sampling by proposing a unified geometric framework. The method integrates gene expression and spatial proximity to construct graph representations, leveraging Gromov-Wasserstein embeddings, geodesic interpolation, and Ollivier-Ricci curvature to quantify spatiotemporal evolution. Validation on Drosophila datasets demonstrates that this framework accurately recapitulates curvature trends in developmental dynamics and exhibits high consistency with Co-Optimal Transport distances. By enabling cross-temporal network structure comparison and continuous interpolation, this work establishes a novel paradigm for elucidating complex developmental processes.

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Prediction Inference of Time Series with Standard ReLU Deep Neural Networks

Aug 15, 2026

This study addresses the insufficient uncertainty quantification in standard ReLU deep neural networks for time series forecasting by proposing a forward bootstrap-based prediction interval (PPI) method. By constructing PPIs and establishing the consistency of DNN estimators alongside the mixing properties and stationary distribution characteristics of bootstrap sequences, this approach effectively captures future variability and enables nonparametric statistical inference. Both simulation studies and empirical analyses demonstrate that the proposed method maintains theoretical rigor while significantly outperforming existing standard nonparametric approaches in terms of predictive performance and uncertainty quantification accuracy.

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Measuring and Detecting Harmful AI Sycophancy

Aug 06, 2026

This work addresses the critical risk posed by large language models (LLMs) that unprincipledly reverse their initial stances to align with user preferences—a phenomenon termed Preference-Induced Stance Reversal Sycophancy (PSRS). To tackle this issue, the authors propose the Contrastive Anchor Probing (CAP) framework, which enables automatic PSRS detection from a single model response for the first time. They also construct the first large-scale, multi-model PSRS-annotated dataset, comprising 290,460 human-labeled responses across 17 prominent LLMs. Empirical analysis reveals PSRS occurrence rates ranging from 5% to 56%, confirming the feasibility of automated detection. However, the study also uncovers a significant drop in detector generalization performance on unseen models and proposes preliminary mitigation strategies to address this limitation.

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Recent publications

Latest Papers

A Unified Geometric Framework for Developmental Analysis of Spatial Transcriptomic Data

Aug 15, 2026

This study addresses the challenge of reconstructing developmental trajectories from destructive spatial transcriptomics sampling by proposing a unified geometric framework. The method integrates gene expression and spatial proximity to construct graph representations, leveraging Gromov-Wasserstein embeddings, geodesic interpolation, and Ollivier-Ricci curvature to quantify spatiotemporal evolution. Validation on Drosophila datasets demonstrates that this framework accurately recapitulates curvature trends in developmental dynamics and exhibits high consistency with Co-Optimal Transport distances. By enabling cross-temporal network structure comparison and continuous interpolation, this work establishes a novel paradigm for elucidating complex developmental processes.

0 citationsRead paper

Prediction Inference of Time Series with Standard ReLU Deep Neural Networks

Aug 15, 2026

This study addresses the insufficient uncertainty quantification in standard ReLU deep neural networks for time series forecasting by proposing a forward bootstrap-based prediction interval (PPI) method. By constructing PPIs and establishing the consistency of DNN estimators alongside the mixing properties and stationary distribution characteristics of bootstrap sequences, this approach effectively captures future variability and enables nonparametric statistical inference. Both simulation studies and empirical analyses demonstrate that the proposed method maintains theoretical rigor while significantly outperforming existing standard nonparametric approaches in terms of predictive performance and uncertainty quantification accuracy.

0 citationsRead paper

Measuring and Detecting Harmful AI Sycophancy

Aug 06, 2026

This work addresses the critical risk posed by large language models (LLMs) that unprincipledly reverse their initial stances to align with user preferences—a phenomenon termed Preference-Induced Stance Reversal Sycophancy (PSRS). To tackle this issue, the authors propose the Contrastive Anchor Probing (CAP) framework, which enables automatic PSRS detection from a single model response for the first time. They also construct the first large-scale, multi-model PSRS-annotated dataset, comprising 290,460 human-labeled responses across 17 prominent LLMs. Empirical analysis reveals PSRS occurrence rates ranging from 5% to 56%, confirming the feasibility of automated detection. However, the study also uncovers a significant drop in detector generalization performance on unseen models and proposes preliminary mitigation strategies to address this limitation.

0 citationsRead paper