arXiv subjects

Yeseul Jeon

Publications and source records attributed to Yeseul Jeon.

14 recordsLinked to original sources

arXiv2026

Kirigami-Structured Electronic Capsule for Long-Term Continuous Gastric Monitoring

Ingestible electronic systems enable non-invasive, in situ sensing within the gastrointestinal (GI) tract, yet clinical translation has been limited by uncontrolled transit, short operational lifetimes, and unreliable wireless communication that prevent continuous monitoring. Here, we present a gastric-resident ingestible robotic platform that achieves week-long operation through integration of a bioinspired, electrically triggered release mechanism with a kirigami-enabled electronic architecture. A kirigami-patterned flexible printed circuit board spans the capsule body and deployable superelastic arms, enabling high-density integration of sensing, power management, and wireless modules within a constrained volume while tolerating large mechanical deformation during gastric residence. Stable retention and on-demand disassembly are achieved using thermally responsive polycaprolactone joints that transition from rigid to compliant states under electrical activation, avoiding dependence on variable chemical triggers. Reliable telemetry in the highly attenuating gastric environment is maintained using a dual-band Bluetooth Low Energy and sub-gigahertz module with RSSI- and throughput-aware adaptive transmission, balancing link robustness and energy consumption. We demonstrate long-term, continuous monitoring of gastric radiation exposure, enabling early detection of dose accumulation and providing a promising in vivo alternative to wearable or handheld dosimeters. Swine studies confirm stable gastric residence, sustained real-time telemetry, and safe gastrointestinal passage following triggered disassembly. This work establishes kirigami-enabled integration as a scalable strategy for long-term gastric-resident robotic systems.

eess.SY↗

arXiv2026

Deep Probabilistic Spatial Modeling for Multivariate Mixed-Type Responses

Many scientific applications involve mixed spatially indexed outcomes of heterogeneous types that are driven by shared latent mechanisms. Modeling such data is challenging due to complex, nonlinear, and potentially nonstationary spatial dependence, as well as the need for coherent joint inference across mixed outcome distributions. Existing multivariate mixed outcome models often rely on restrictive linear assumptions, while recent deep learning approaches emphasize predictive flexibility but typically lack coherent joint modeling and uncertainty quantification for spatial data. We develop MultiDeepGP, a scalable and statistically principled framework for joint modeling of multivariate mixed outcomes in spatial settings. The proposed approach introduces a shared latent spatial component that governs cross-outcome dependence while allowing outcome-specific distributions. Spatial dependence and nonlinear structure are captured through a deep latent representation, and uncertainty quantification is enabled via an efficient Monte Carlo-based inference strategy. This construction balances modeling flexibility with probabilistic interpretability and computational feasibility. The proposed method is evaluated through simulation studies designed to reflect key challenges in mixed outcome spatial modeling, as well as an application to georeferenced environmental and public health data from the African Great Lakes region. The results demonstrate that the proposed framework provides accurate joint prediction and reliable uncertainty quantification in complex spatial settings.

stat.ME↗

arXiv2026

Uncertainty-Aware Neural Multivariate Geostatistics

We propose Deep Neural Coregionalization, a scalable framework for uncertainty-aware multivariate geostatistics. DNC models multivariate spatial effects through spatially varying latent factors and loadings, assigning deep Gaussian process (DGP) priors to both the factors and the entries of the loading matrix. This joint construction learns shared latent spatial structure together with response-specific, location-dependent mixing weights, enabling flexible nonlinear and space-dependent associations within and across variables. A key contribution is a variational formulation that makes the DGP to deep neural network (DNN) correspondence explicit: maximizing the DGP evidence lower bound (ELBO) is equivalent to training DNNs with weight decay and Monte Carlo (MC) dropout. This yields fast mini-batch stochastic optimization without Markov Chain Monte Carlo (MCMC), while providing principled uncertainty quantification through MC-dropout forward passes as approximate posterior draws, producing calibrated credible surfaces for prediction and spatial effect estimation. Across simulations, DNC is competitive with existing spatial factor models, particularly under strong nonstationarity and complex cross-dependence, while delivering substantial computational gains. In a multivariate environmental case study, DNC captures spatially varying cross-variable interactions, produces interpretable maps of multivariate outcomes, and scales uncertainty quantification to large datasets with orders-of-magnitude reductions in runtime.

stat.ME↗

arXiv2025

Quantifying Fire Risk Index in Chemical Industry Using Statistical Modeling Procedure

Fire incident reports contain detailed textual narratives that capture causal factors often overlooked in structured records, while financial damage amounts provide measurable outcomes of these events. Integrating these two sources of information is essential for uncovering interpretable links between descriptive causes and their economic consequences. To this end, we develop a data-driven framework that constructs a composite Risk Index, enabling systematic quantification of how specific keywords relate to property damage amounts. This index facilitates both the identification of high-impact terms and the aggregation of risks across semantically related clusters, thereby offering a principled measure of fire-related financial risk. Using more than a decade of Korean fire investigation reports on the chemical industry classified as Special Buildings (2013 through 2024), we employ topic modeling and network-based embedding to estimate semantic similarities from interactions among words and subsequently apply Lasso regression to quantify their associations with property damage amounts, thereby estimate fire risk index. This approach enables us to assess fire risk not only at the level of individual terms but also within their broader textual context, where highly interactive related words provide insights into collective patterns of hazard representation and their potential impact on expected losses. The analysis highlights several domains of risk, including hazardous chemical leakage, unsafe storage practices, equipment and facility malfunctions, and environmentally induced ignition. The results demonstrate that text-derived indices provide interpretable and practically relevant insights, bridging unstructured narratives with structured loss information and offering a basis for evidence-based fire risk assessment and management.

stat.AP↗

arXiv2025

Deep Generative Modeling with Spatial and Network Images: An Explainable AI (XAI) Approach

This article addresses the challenge of modeling the amplitude of spatially indexed low frequency fluctuations (ALFF) in resting state functional MRI as a function of cortical structural features and a multi-task coactivation network in the Adolescent Brain Cognitive Development (ABCD) Study. It proposes a generative model that integrates effects of spatially-varying inputs and a network-valued input using deep neural networks to capture complex non-linear and spatial associations with the output. The method models spatial smoothness, accounts for subject heterogeneity and complex associations between network and spatial images at different scales, enables accurate inference of each images effect on the output image, and allows prediction with uncertainty quantification via Monte Carlo dropout, contributing to one of the first Explainable AI (XAI) frameworks for heterogeneous imaging data. The model is highly scalable to high-resolution data without the heavy pre-processing or summarization often required by Bayesian methods. Empirical results demonstrate its strong performance compared to existing statistical and deep learning methods. We applied the XAI model to the ABCD data which revealed associations between cortical features and ALFF throughout the entire brain. Our model performed comparably to existing methods in predictive accuracy but provided superior uncertainty quantification and faster computation, demonstrating its effectiveness for large-scale neuroimaging analysis. Open-source software in Python for XAI is available.

stat.ME↗

arXiv2025

Interpretable Deep Neural Network for Modeling Functional Surrogates

Developing surrogates for computer models has become increasingly important for addressing complex problems in science and engineering. This article introduces an artificial intelligent (AI) surrogate, referred to as the DeepSurrogate, for analyzing functional outputs with vector-valued inputs. The relationship between the functional output and vector-valued input is modeled as an infinite sequence of unknown functions, each representing the relationship at a specific location within the functional domain. These spatially indexed functions are expressed through a combination of basis functions and their corresponding coefficient functions, both of which are modeled using deep neural networks (DNN). The proposed framework accounts for spatial dependencies across locations, while capturing the relationship between the functional output and scalar predictors. It also integrates a Monte Carlo (MC) dropout strategy to quantify prediction uncertainty, enhancing explainability in the deep neural network architecture. The proposed method enables efficient inference on datasets with approximately 50,000 spatial locations and 20 simulations, achieving results in under 10 minutes using standard hardware. The approach is validated on extensive synthetic datasets and a large-scale simulation from the Sea Lake and Overland Surge from Hurricanes (SLOSH) simulator. An open-source Python package implementing the method is made available.

stat.ME↗

arXiv2024

A Bayesian Convolutional Neural Network-based Generalized Linear Model

Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in prediction accuracy, statistical inference, such as estimating the effects of covariates and quantifying the prediction uncertainty, is not trivial due to the highly complicated model structure and overparameterization. To address this challenge, we propose a new Bayesian approach by embedding CNNs within the generalized linear models (GLMs) framework. We use extracted nodes from the last hidden layer of CNN with Monte Carlo (MC) dropout as informative covariates in GLM. This improves accuracy in prediction and regression coefficient inference, allowing for the interpretation of coefficients and uncertainty quantification. By fitting ensemble GLMs across multiple realizations from MC dropout, we can account for uncertainties in extracting the features. We apply our methods to biological and epidemiological problems, which have both high-dimensional correlated inputs and vector covariates. Specifically, we consider malaria incidence data, brain tumor image data, and fMRI data. By extracting information from correlated inputs, the proposed method can provide an interpretable Bayesian analysis. The algorithm can be broadly applicable to image regressions or correlated data analysis by enabling accurate Bayesian inference quickly.

stat.ME↗

arXiv2024

Integrative Variational Autoencoders for Generative Modeling of an Image Outcome with Multiple Input Images

Understanding relationships across multiple imaging modalities is central to neuroimaging research. We introduce the Integrative Variational Autoencoder (InVA), the first hierarchical VAE framework for image-on-image regression in multimodal neuroimaging. Unlike standard VAEs, which are not designed for predictive integration across modalities, InVA models outcome images as functions of both shared and modality-specific features. This flexible, data-driven approach avoids rigid assumptions of classical tensor regression and outperforms conventional VAEs and nonlinear models such as BART. As a key application, InVA accurately predicts costly PET scans from structural MRI, offering an efficient and powerful tool for multimodal neuroimaging.

eess.IV↗

arXiv2024

Network-based Topic Structure Visualization

In the real world, many topics are inter-correlated, making it challenging to investigate their structure and relationships. Understanding the interplay between topics and their relevance can provide valuable insights for researchers, guiding their studies and informing the direction of research. In this paper, we utilize the topic-words distribution, obtained from topic models, as item-response data to model the structure of topics using a latent space item response model. By estimating the latent positions of topics based on their distances toward words, we can capture the underlying topic structure and reveal their relationships. Visualizing the latent positions of topics in Euclidean space allows for an intuitive understanding of their proximity and associations. We interpret relationships among topics by characterizing each topic based on representative words selected using a newly proposed scoring scheme. Additionally, we assess the maturity of topics by tracking their latent positions using different word sets, providing insights into the robustness of topics. To demonstrate the effectiveness of our approach, we analyze the topic composition of COVID-19 studies during the early stage of its emergence using biomedical literature in the PubMed database. The software and data used in this paper are publicly available at https://github.com/jeon9677/gViz .

stat.AP↗

arXiv2024

A Novel Interpretable Fusion Analytic Framework for Investigating Functional Brain Connectivity Differences in Cognitive Impairments

Functional magnetic resonance imaging (fMRI) data is characterized by its complexity and high--dimensionality, encompassing signals from various regions of interests (ROIs) that exhibit intricate correlations. Analyzing fMRI data directly proves challenging due to its intricate structure. Nevertheless, ROIs convey crucial information about brain activities through their connections, offering insights into distinctive brain activity characteristics between different groups. To address this, we propose a cutting-edge interpretable fusion analytic framework that facilitates the identification and understanding of ROI connectivity disparities between two groups, thereby revealing their unique features. Our novel approach encompasses three key steps. Firstly, we construct ROI functional connectivity networks (FCNs) to effectively manage fMRI data. Secondly, employing the FCNs, we utilize a self--attention deep learning model for binary classification, generating an attention distribution that encodes group differences. Lastly, we employ a latent space item-response model to extract group representative ROI features, visualizing these features on the group summary FCNs. We validate the effectiveness of our framework by analyzing four types of cognitive impairments, showcasing its capability to identify significant ROIs contributing to the differences between the two disease groups. This novel interpretable fusion analytic framework holds immense potential for advancing our understanding of cognitive impairments and could pave the way for more targeted therapeutic interventions.

stat.AP↗

arXiv2023

Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives

Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis.

q-bio.GN↗

arXiv2022

Network-based Topic Interaction Map for Big Data Mining of COVID-19 Biomedical Literature

Since the emergence of the worldwide pandemic of COVID-19, relevant research has been published at a dazzling pace, which yields an abundant amount of big data in biomedical literature. Due to the high volum of relevant literature, it is practically impossible to follow up the research manually. Topic modeling is a well-known unsupervised learning that aims to reveal latent topics from text data. In this paper, we propose a novel analytical framework for estimating topic interactions and effective visualization to improve topics' relationships. We first estimate topic-word distributions using the biterm topic model and estimate the topics' interaction based on the word distribution using the latent space item response model. We mapped these latent topics onto networks to visualize relationships among the topics. Moreover, in the proposed approach, we developed a score that is helpful in selecting meaningful words that characterize the topic. We figure out how topics are related by looking at how their relationships change. We do this with a "trajectory plot" that is made with different levels of word richness. These findings provide a thoroughly mined and intuitive representation of relationships between topics related to a specific research area. The application of this proposed framework to the PubMed literature demonstrates utility of our approach in understanding of the topic composition related to COVID-19 studies in the stage of its emergence.

cs.IR↗

arXiv2022

Interpretable Fusion Analytics Framework for fMRI Connectivity: Self-Attention Mechanism and Latent Space Item-Response Model

There have been several attempts to use deep learning based on brain fMRI signals to classify cognitive impairment diseases. However, deep learning is a hidden black box model that makes it difficult to interpret the process of classification. To address this issue, we propose a novel analytical framework that interprets the classification result from deep learning processes. We first derive the region of interest (ROI) functional connectivity network (FCN) by embedding functions based on their similar signal patterns. Then, using the self-attention equipped deep learning model, we classify diseases based on their FCN. Finally, in order to interpret the classification results, we employ a latent space item-response interaction network model to identify the significant functions that exhibit distinct connectivity patterns when compared to other diseases. The application of this proposed framework to the four types of cognitive impairment shows that our approach is valid for determining the significant ROI functions.

cs.LG↗

arXiv2021

Bayesian Shrinkage for Functional Network Models, with Applications to Longitudinal Item Response Data

Longitudinal item response data are common in social science, educational science, and psychology, among other disciplines. Studying the time-varying relationships between items is crucial for educational assessment or designing marketing strategies from survey questions. Although dynamic network models have been widely developed, we cannot apply them directly to item response data because there are multiple systems of nodes with various types of local interactions among items, resulting in multiplex network structures. We propose a new model to study these temporal interactions among items by embedding the functional parameters within the exponential random graph model framework. Inference on such models is difficult because the likelihood functions contain intractable normalizing constants. Furthermore, the number of functional parameters grows exponentially as the number of items increases. Variable selection for such models is not trivial because standard shrinkage approaches do not consider temporal trends in functional parameters. To overcome these challenges, we develop a novel Bayes approach by combining an auxiliary variable MCMC algorithm and a recently-developed functional shrinkage method. We apply our algorithm to survey and review data sets, illustrating that the proposed approach can avoid the evaluation of intractable normalizing constants as well as the detection of significant temporal interactions among items. Through a simulation study under different scenarios, we examine the performance of our algorithm. Our method is, to our knowledge, the first attempt to select functional variables for models with intractable normalizing constants.

stat.ME↗