Biomedical Mathematics Group

Machine learning methods trained on simple models can predict critical transitions in complex natural systems - Shingo Gibo
2025-07-04 14:00 - 16:00 (Asia/Seoul) | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Machine learning methods trained on simple models can predict critical transitions in complex natural systems” by Smita Deb, Sahil Sidheekh, Christopher F. Clements, Narayanan C. Krishnan, and Partha S. Dutta, in Royal Society Open Science, (2022).
Abstract:
Forecasting sudden changes in complex systems is a critical but challenging task, with previously developed methods varying widely in their reliability. Here we develop a novel detection method, using simple theoretical models to train a deep neural network to detect critical transitions—the Early Warning Signal Network (EWSNet). We then demonstrate that this network, trained on simulated data, can reliably predict observed real-world transitions in systems ranging from rapid climatic change to the collapse of ecological populations. Importantly, our model appears to capture latent properties in time series missed by previous warning signals approaches, allowing us to not only detect if a transition is approaching, but critically whether the collapse will be catastrophic or non-catastrophic. These novel properties mean EWSNet has the potential to serve as an indicator of transitions across a broad spectrum of complex systems, without requiring information on the structure of the system being monitored. Our work highlights the practicality of deep learning for addressing further questions pertaining to ecosystem collapse and has much broader management implications.

Optimal transport for generating transition states in chemical reactions - Gyuyoung Hwang
2025-07-11 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Optimal transport for generating transition states in chemical reactions” by C. Duan et.al., Nat. Machine. Intelligence, 2025.
Abstract
Transition states (TSs) are transient structures that are key to understanding reaction mechanisms and designing catalysts but challenging to capture in experiments. Many optimization algorithms have been developed to search for TSs computationally. Yet, the cost of these algorithms driven by quantum chemistry methods (usually density functional theory) is still high, posing challenges for their applications in building large reaction networks for reaction exploration. Here we developed React-OT, an optimal transport approach for generating unique TS structures from reactants and products. React-OT generates highly accurate TS structures with a median structural root mean square deviation of 0.053 Å and median barrier height error of 1.06 kcal mol−1 requiring only 0.4 s per reaction. The root mean square deviation and barrier height error are further improved by roughly 25% through pretraining React-OT on a large reaction dataset obtained with a lower level of theory, GFN2-xTB. We envision that the remarkable accuracy and rapid inference of React-OT will be highly useful when integrated with the current high-throughput TS search workflow. This integration will facilitate the exploration of chemical reactions with unknown mechanisms.

scGPT: toward building a foundation model for single-cell multi-omics using generative AI - Hyun Kim
2025-07-18 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “scGPT: toward building a foundation model for single-cell multi-omics using generative AI” by Haotian Cui, et.al. Nature Methods, 2024.
Abstract
Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between language and cellular biology (in which texts comprise words; similarly, cells are defined by genes), our study probes the applicability of foundation models to advance cellular biology and genetic research. Using burgeoning single-cell sequencing data, we have constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction and gene network inference.

Jae-Kwang Kim - Weight calibration for causal inference and transfer learning
2025-07-21 11:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Abstract: Weight calibration is a popular technique in handling covariate-shift problem in causal inference. It can be viewed as a dual optimization problem for incorporating the implicit regression model. We introduce the generalized entropy calibration as a general tool for weight calibration. Several interesting applications will be introduced in the context of causal inference. Furthermore, weight calibration can be used to transfer learning, which combines information from two different samples, one for source data and the other for target data.

Effective Markovian dynamics method of solving non-Markovian dynamics of stochastic gene expression - Dongju Lim
2025-07-25 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Effective Markovian dynamics method of solving non-Markovian dynamics of stochastic gene expression” by Youming Li and Chen Jia, Physical Review Letters, to appear.
Abstract
Experiments have shown that over 10% of proteins are degraded non-exponentially. Gene expression models for non-exponentially degraded proteins are notoriously difficult to solve since the underlying stochastic dynamics is non-Markovian. Here we develop an effective Markovian dynamics (EMD) method which converts a large class of non-Markovian models into effective Markovian ones so that they have the same mRNA and protein distributions at any fixed time. Using the EMD approach, we analytically solve some classical gene expression models with non-exponential or delayed protein decay, whose exact distributions are previously unknown and fail to be obtained using conventional queueing theory. Our theory successfully explains why non-exponentially degraded proteins on average have smaller mRNA-protein correlation than exponentially degraded proteins, and it predicts that bimodality is significantly enhanced in the presence of delayed protein degradation.

Quantifying the energy landscape of high-dimensional oscillatory systems by diffusion decomposition - Eui Min Jeong
2025-08-01 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Quantifying the energy landscape of high-dimensional oscillatory systems by diffusion decomposition” by S. Bian et.al., Cell Reports Physical Science, 2025.
Abstract
High-dimensional networks producing oscillatory dynamics are ubiquitous in biological systems. Unraveling the mechanism of oscillatory dynamics in biological networks with stochastic perturbations becomes of paramount significance. Although the classical energy landscape theory provides a tool to study this problem in multistable systems and explain cellular functions, it remains challenging to accurately quantify the landscape for high-dimensional oscillatory systems. Here, we propose an approach called the diffusion decomposition of Gaussian approximation (DDGA). We demonstrate the efficacy of the DDGA in quantifying the energy landscape of oscillatory systems and corresponding stochastic dynamics in comparison with existing approaches. By further applying the DDGA to high-dimensional biological networks, we are able to uncover more intricate biological mechanisms efficiently, which deepens our understanding of cellular functions.

Jooyoung Hahn - Topological Data Analysis with two applications: Tumor Microenvironment and 2D Chromatography with High-Resolution Mass Spectrometry
2025-08-06 15:00 - 17:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Abstract
Topological Data Analysis (TDA) has emerged as a powerful framework for uncovering meaningful structure in high-dimensional, complex datasets. In this talk, we present two applications of TDA in analyzing patterns, one in the tumor microenvironment (TME) and the other in high-resolution chemical profiling. In the first case, we develop a TDA-based framework to quantify malignant-immune cell interactions in Diffuse Large B Cell Lymphoma using multiplex immunofluorescence imaging. By introducing Topological Malignant Clusters (TopMC) and leveraging persistence diagrams, we capture both global infiltration patterns and local density-based features. This robust approach enables consistent prognostic assessment regardless of tumor region heterogeneity and reveals correlations with patient survival. In the second application, we utilize the Ball Mapper algorithm to simplify and visualize high-dimensional data obtained from 2D Chromatography with high-resolution mass spectrometry. This enables interpretable chemical profiling of complex mixtures and supports tasks such as sample authentication and environmental analysis. Together, these studies demonstrate the versatility and interpretability of TDA for extracting biologically and chemically meaningful information.
Seminar Video Link: https://www.youtube.com/watch?v=mz9pY6nk3n4&t=12s

Circadian rhythm analysis using wearable-based accelerometry as a digital biomarker of aging and healthspan - Yun Min Song
2025-08-08 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Circadian rhythm analysis using wearable-based accelerometry as a digital biomarker of aging and healthspan” by J. Shim et.al., npj digital medicine, 2024.
Abstract
Recognizing the pivotal role of circadian rhythm in the human aging process and its scalability through wearables, we introduce CosinorAge, a digital biomarker of aging developed from wearable-derived circadian rhythmicity from 80,000 midlife and older adults in the UK and US. A one-year increase in
CosinorAge corresponded to 8–12% higher all-cause and cause-specific mortality risks and 3–14% increased prospective incidences of age-related diseases. CosinorAge also captured a non-linear decline in resilience and physical functioning, evidenced by an 8–33% reduction in self-rated health
and a 3–23% decline in health-related quality of life score, adjusting for covariates and multiple testing. The associations were robust in sensitivity analyses and external validation using an independent cohort from a disparate geographical region using a different wearable device. Moreover, we
illustrated a heterogeneous impact of circadian parameters associated with biological aging, with young (<45 years) and fast agers experiencing a substantially delayed acrophase with a 25-minute difference in peak timing compared to slow agers, diminishing to a 7-minute difference in older adults
(>65 years). We demonstrated a significant enhancement in the predictive performance when integrating circadian rhythmicity in the estimation of biological aging over physical activity. Our findings underscore CosinorAge’s potential as a scalable, economic, and digital solution for promoting healthy longevity, elucidating the critical and multifaceted circadian rhythmicity in aging processes. Consequently, our research contributes to advancing preventive measures in digital medicine.

Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters - Kevin Spinicci
2025-08-22 15:30 - 17:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters” by L. Xia et.al. Nature Communications, 2024.
Abstract
Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAP’s 2D embeddings might not reliably inform the similarities among cell clusters. Motivated by this challenge, we present a statistical method, scDEED, for detecting dubious cell embeddings output by a 2D-embedding method. By calculating a reliability score for every cell embedding based on the similarity between the cell’s 2D-embedding neighbors and pre-embedding neighbors, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover, by minimizing the number of dubious cell embeddings, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. We show the effectiveness of scDEED on multiple datasets for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.

Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains - Jinwoo Hyun
2025-08-29 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains” by K. Lee.
Abstract
The ability of deep networks to learn superior representations hinges on leveraging the proper inductive biases, considering the inherent properties of datasets. In tabular domains, it is critical to effectively handle heterogeneous features (both categorical and numerical) in a unified manner and to grasp irregular functions like piecewise constant functions. To address the challenges in the self-supervised learning framework, we propose a novel pretext task based on the classical binning method. The idea is straightforward: reconstructing the bin indices (either orders or classes) rather than the original values. This pretext task provides the encoder with an inductive bias to capture the irregular dependencies, mapping from continuous inputs to discretized bins, and mitigates the feature heterogeneity by setting all features to have category-type targets. Our empirical investigations ascertain several advantages of binning: compatibility with encoder architecture and additional modifications, standardizing all features into equal sets, grouping similar values within a feature, and providing ordering information. Comprehensive evaluations across diverse tabular datasets corroborate that our method consistently improves tabular representation learning performance for a wide range of downstream tasks. The codes are available in the supplementary material.

Weak form SciML in the Life Sciences: The Weak Form is Stronger than you Think - David Bortz
2025-09-03 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract
The creation and inference of mathematical models is central to modern scientific discovery in the life sciences. As more realism is demanded of models, however, the conventional framework of biology-guided model proposal, discretization, parameter estimation, and model refinement becomes unwieldy, expensive, and computationally daunting. Recent advances in Weak form-based Scientific Machine Learning (WSciML) allow for the creation and inference of interpretable models directly from data via advanced numerical functional analysis, computational statistics, and numerical linear algebra techniques. This class of methods completely bypasses the need for forward-solve numerical discretizations and yields both parsimonious mathematical models and efficient parameter estimates. These methods are orders of magnitude faster and more accurate than traditional approaches and far more robust to the high noise levels common to data in the biological sciences. The combination of these features in a single framework provides a compelling alternative to both traditional modeling approaches as well as modern black-box neural networks. In this talk, I will present our weak form approach, describing our equation learning (WSINDy) and parameter estimation (WENDy) algorithms. I will demonstrate these performance properties via applications to several canonical problems in structured population modeling, cell migration, and mathematical epidemiology.

Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics - Kang Min Lee
2025-09-05 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics” by J. Thompson et al., bioarxiv, 2025.
Abstract
Microbial communities play essential roles in shaping ecosystem functions and predictive modeling frameworks are crucial for understanding, controlling, and harnessing their properties. Competition and cross-feeding of metabolites drives microbiome dynamics and functions. Existing mechanistic models that capture metabolite-mediated interactions in microbial communities have limited flexibility due to rigid assumptions. While machine learning models provide flexibility, they require large datasets, are challenging to interpret, and can over-fit to experimental noise. To overcome these limitations, we develop a physics-constrained machine learning model, which we call the Neural Species Mediator (NSM), that combines a mechanistic model of metabolite dynamics with a machine learning component. The NSM is more accurate than mechanistic or machine learning components on experimental datasets and provides insights into direct biological interactions. In summary, embedding a neural network into a mechanistic model of microbial community dynamics improves prediction performance and interpretability compared to its constituent mechanistic or machine learning components.

Decomposing causality into its synergistic, unique, and redundant components - Olive Cawiding
2025-09-12 14:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Decomposing causality into its synergistic, unique, and redundant components” by Álvaro Martínez-Sánchez et al., Nature Communications, 2024.
Abstract
Causality lies at the heart of scientific inquiry, serving as the fundamental basis for understanding interactions among variables in physical systems. Despite its central role, current methods for causal inference face significant challenges due to nonlinear dependencies, stochastic interactions, self-causation, collider effects, and influences from exogenous factors, among others. While existing methods can effectively address some of these challenges, no single approach has successfully integrated all these aspects. Here, we address these challenges with SURD: Synergistic-Unique-Redundant Decomposition of causality. SURD quantifies causality as the increments of redundant, unique, and synergistic information gained about future events from past observations. The formulation is non-intrusive and applicable to both computational and experimental investigations, even when samples are scarce. We benchmark SURD in scenarios that pose significant challenges for causal inference and demonstrate that it offers a more reliable quantification of causality compared to previous methods.

SCassist: An AI Based Workflow Assistant for Single-Cell Analysis - Aqsa Awan
2025-09-19 16:00 - 18:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “SCassist: An AI Based Workflow Assistant for Single-Cell Analysis ” by Vijayaraj Nagarajan et al., bioarxiv, 2025.
Abstract
Single-cell RNA sequencing (scRNA-seq) data analysis often involves complex iterative workflow, requiring significant expertise and time. To navigate this complexity, we have developed SCassist, an R package that leverages the power of the large language models (LLM’s) to guide and enhance scRNA-seq analysis. SCassist integrates LLM’s into key workflow steps, to analyze user data and provide relevant recommendations for filtering, normalization and clustering parameters. It also provides LLM guided insightful interpretations of variable features and principal components, along with cell type annotations and enrichment analysis. SCassist provides intelligent assistance using popular LLM’s like Google’s Gemini, OpenAI’s GPT and Meta’s Llama3, making scRNA-seq analysis accessible to researchers at all levels.

Sleep as part of the 24-hour day: Methods and Applications in Oncology - Joshua Wiley
2025-09-24 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract
Sleep is commonly analysed as an independent factor. However, because of the 24-hour constraints on a day, changes in sleep will co-occur with changes in remaining time use. This talk introduces compositional data analysis (CoDA) for sleep research. CoDA is illustrated using 24-hour sleep and activity data from accelerometry, first cross-sectionally showing associations between sleep and activity with daily emotions. Next, CoDA is extended to multilevel models, which commonly occur in sleep research as sleep across multiple days. A Bayesian implementation in R using the new multilevelcoda package is presented and results of a simulation study discussed. Multilevel CoDA is used to analyse how nightly sleep architecture, collected via at home EEG, predicts next day affect. CoDA solves a common constraint in sleep research with sleep stages that time in all stages sums to total sleep.

Tackling inter-subject variability in smartwatch data using factorization models - Myna Lim
2025-09-26 10:00 - 11:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Tackling inter-subject variability in smartwatch data using factorization models” by Arman Naseri et. al, Scientific Reports, 2025.
Abstract
Smartwatches enable longitudinal and continuous data acquisition. This has the potential to remotely monitor (changes) of the health of users. However, differences among subjects (inter-subject variability) limit a model to generalize to unseen subjects. This study focused on binary classification tasks using heart rate and step counter from smartwatches, including night/day and inactive/active classification, as well as sleep and SpO2-related (oxygen saturation) tasks. To address inter-subject variability, we explored different transforming and normalization regimes for time series including per-subject and population-based strategies. We propose a modified factorized autoencoder, which separates the data into two latent spaces capturing class-specific and subject-specific information. Our proposed generalized factorized autoencoder and triplet factorized autoencoder improved classification accuracy over the baseline from 74.8 (± 10.5) to 83.1 (± 5.1) and 83.4 (± 5.3), respectively, for night/day classification, gains for inactive/active classification were modest, improving from 84.3 (± 9.4) to 86.9 (± 4.4) and 86.6 (± 4.3), respectively. Our study highlights challenges of handling inter-subject variability in smartwatch data and how factorization models can be used to enable more robust and personalized health monitoring solutions for diverse populations.

Excess Mortality, Two Lenses : Healthcare Access and Cross-National Time Trends - Daeil Jang
2025-09-29 16:00 - 18:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Abstract
Background : Excess mortality captures both the direct and indirect impacts of the pandemic. We examine (1) within-country heterogeneity by healthcare access over distinct viral waves in Korea, and (2) cross-country associations between excess mortality and preparedness (Global Health Security, GHS), stratified by IMF development stage.
Methods : Study 1 assembled a region-level panel linking excess deaths (observed–expected) with healthcare access indicators (capacity, travel time, etc.) and estimated fixed-effects/event-study models across epidemic phases. Study 2 analyzed 60 countries, relating standardized excess mortality rates to GHS scores, with correlations/regressions reported within IMF development groups.
Results : In Korea, healthcare access was significantly associated with higher excess mortality only during the Omicron surge, with no consistent differences in earlier phases—suggesting that access constraints translate into excess deaths primarily under acute demand spikes. Globally, excess mortality showed an overall negative association with GHS scores, but the magnitude and significance differed by IMF development stage, indicating effect modification
by structural context.
Conclusions : During rapid surges (e.g., Omicron), access bottlenecks—beds, staffing, transport—are closely linked to excess deaths. Internationally, preparedness capacity matters, yet its protective association varies with development level. Policy priorities include
surge capacity, timely access pathways, and context-tailored preparedness investments; future work should strengthen causal inference with age standardization, reporting-delay adjustments, and cause-specific linkages.

Topological Data Analysis for Multiscale Biology - Heather Harrington
2025-10-01 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract
Many processes in the life sciences are inherently multi-scale and dynamic. Spatial structures and patterns vary across levels of organisation, from molecular to multi-cellular to multi-organism. With more sophisticated mechanistic models and data available, quantitative tools are needed to study their evolution in space and time. Topological data analysis (TDA) provides a multi-scale summary of data. I will review the main tools in topological data analysis and how single and multi-parameter persistent homology provide insights to biological systems.

Developing time-series machine learning methods to unlock new insights from large-scale biomedical resources - Aiden Doherty
2025-10-15 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract
Smartphones and wearable devices provide a major opportunity to transform our understanding of the mechanisms, determinants, and consequences of diseases. For example, around 9 in 10 people own a smartphone in the United Kingdom, while one-fifth of US adults own wearable technologies. This high level of device ownership means that many people could contribute to health research from the comfort of their home by offering small amounts of time to share data and help address health-related questions that matter to them. A leading example is the seven day wrist-worn accelerometer data measured in 100,000 UK Biobank participants between 2013-2015 that has led to important new findings. These include discoveries of: new genetic variants for sleep and activity; small amounts of vigorous non-exercise physical activity being associated with substantially lower mortality; and no apparent upper threshold to the benefits of physical activity with respect to cardiovascular disease risk. However, challenges exist around cost, access, validity, and training. In this talk I will review progress made in this exciting new area of health data science and share opportunities for self-supervised time-series machine learning to provide new insights into physical activity, sleep, heart rhythms and other exposures relevant to health and disease.

Simulating the Spread of Infection in Networks with Quantum Computers - Shingo Gibo
2025-10-17 10:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Simulating the Spread of Infection in Networks with Quantum Computers” by Xiaoyang Wang, Yinchenguang Lyu, Changyu Yao and Xiao Yuan, Physical Review Applied, vol.19, 064035 (2023).
Abstract
We propose to use quantum computers to simulate infection spreading in networks. We first show the analogy between the infection distribution and spin-lattice configurations with Ising-type interactions. Then, since the spreading process can be modeled as a classical Markovian process, we show that the spreading process can be simulated using the evolution of a quantum thermal dynamic model with a parameterized Hamiltonian. In particular, we analytically and numerically analyze the evolution behavior of the Hamiltonian, and prove that the evolution simulates a classical Markovian process, which describes the well-known epidemiological stochastic susceptible and infectious (SI) model. A practical method to determine the parameters of the thermal dynamic Hamiltonian from epidemiological inputs is exhibited. As an example, we simulate the infection spreading process of the SARS-Cov-2 variant Omicron in a small-world network.

Dynamical Mean-Field Theory of Complex Systems on Sparse Directed Networks - Gyuyoung Hwang
2025-10-24 10:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Dynamical Mean-Field Theory of Complex Systems on Sparse Directed Networks” by Fernando L. Metz, Phys. Rev. Letters, 2025.
Abstract
Although real-world complex systems typically interact through sparse and heterogeneous networks, analytic solutions of their dynamics are limited to models with all-to-all interactions. Here, we solve the dynamics of a broad range of nonlinear models of complex systems on sparse directed networks with a random structure. By generalizing dynamical mean-field theory to sparse systems, we derive an exact equation for the path probability describing the effective dynamics of a single degree of freedom. Our general solution applies to key models in the study of neural networks, ecosystems, epidemic spreading, and synchronization. Using the population dynamics algorithm, we solve the path-probability equation to determine the phase diagram of a seminal neural network model in the sparse regime, showing that this model undergoes a transition from a fixed-point phase to chaos as a function of the network topology.

Dynamical data science and AI for Biology and Medicine - Luonan Chen
2025-10-29 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract
I will present a talk on “Dynamical data science and AI” for quantifying dynamical biological processes, disease progressions and various phenotypes, including dynamic network biomarkers (DNB) for early-warning signals of critical transitions, spatial-temporal information (STI) transformation for short-term time-series prediction, knockoff conditional mutual information (KOCMI) for quantifying interventional causality, partial cross-mapping (PCM) for causal inference among variables, and further AI applications to medicine. These methods are all data-driven or model-free approaches but based on the theoretical frameworks of nonlinear dynamics. We show the principles and advantages of dynamical data-science approaches for phenotype quantification as explicable, quantifiable, and generalizable. In particular, different from statistical data-science, dynamical data-science approaches exploit the essential features of dynamical systems in terms of data, e.g. strong fluctuations near a bifurcation point, low-dimensionality of a center manifold or an attractor, and phase-space reconstruction from a single variable by delay embedding theorem, and thus are able to provide different or additional information to the traditional approaches, i.e. statistics-based data science approaches. The dynamical data-science approaches for the quantifications of various phenotypes will further play an important role in the systematical research of various fields in biology and AI.

Dosing Time of Day Impacts the Safety of Antiarrhythmic Drugs in a Computational Model of Cardiac Electrophysiology - Chitaranjan Mahapatra
2025-10-31 09:00 - 10:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Dosing Time of Day Impacts the Safety of Antiarrhythmic Drugs in a Computational Model of Cardiac Electrophysiology” by Ning Wei and Casey O Diekman, J. Biol. Rhythms, 2025.
Abstract
Circadian clocks regulate many aspects of human physiology, including cardiovascular function and drug metabolism. Administering drugs at optimal times of the day may enhance effectiveness and reduce side effects. Certain cardiac antiarrhythmic drugs have been withdrawn from the market due to unexpected proarrhythmic effects such as fatal Torsade de Pointes (TdP) ventricular tachycardia. The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a recent global initiative to create guidelines for the assessment of drug-induced arrhythmias that recommends a central role for computational modeling of ion channels and in silico evaluation of compounds for TdP risk. We simulated circadian regulation of cardiac excitability and explored how dosing time of day affects TdP risk for 11 drugs previously classified into risk categories by CiPA. The model predicts that a high-risk drug taken at the most optimal time of day may actually be safer than a low-risk drug taken at the least optimal time of day. Based on these proof-of-concept results, we advocate for the incorporation of circadian clock modeling into the CiPA paradigm for assessing drug-induced TdP risk. Since cardiotoxicity is the leading cause of drug discontinuation, modeling cardiac-related chronopharmacology has significant potential to improve therapeutic outcomes.

Principled PCA separates signal from noise in omics count data - Hyun Kim
2025-11-07 10:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Principled PCA separates signal from noise in omics count data” by Jay S. Stanley III et al., bioarxiv, 2025.
Abstract
Principal component analysis (PCA) is indispensable for processing high-throughput omics datasets, as it can extract meaningful biological variability while minimizing the influence of noise. However, the suitability of PCA is contingent on appropriate normalization and transformation of count data, and accurate selection of the number of principal components; improper choices can result in the loss of biological information or corruption of the signal due to excessive noise. Typical approaches to these challenges rely on heuristics that lack theoretical foundations. In this work, we present Biwhitened PCA (BiPCA), a theoretically grounded framework for rank estimation and data denoising across a wide range of omics modalities. BiPCA overcomes a fundamental difficulty with handling count noise in omics data by adaptively rescaling the rows and columns – a rigorous procedure that standardizes the noise variances across both dimensions. Through simulations and analysis of over 100 datasets spanning seven omics modalities, we demonstrate that BiPCA reliably recovers the data rank and enhances the biological interpretability of count data. In particular, BiPCA enhances marker gene expression, preserves cell neighborhoods, and mitigates batch effects. Our results establish BiPCA as a robust and versatile framework for high-throughput count data analysis.

Digital Health Care in Samsung - DongHyun Lee
2025-11-10 16:00 - 16:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
본 발표는 삼성에서 개발 중인 디지털 헬스케어 기술을 소개합니다.
먼저, 삼성 갤럭시 웨어러블 센서의 기능과 활용 가능성을 설명하며, 웰니스 및 의료기기 서비스의 상품화 사례를 소개합니다.
또한, 삼성이 디지털 헬스케어를 통해 추구하는 방향과 비전을 제시합니다.
마지막으로, 삼성 개발자로서 디지털 헬스케어의 미래 전망과 기술 발전 가능성에 대해 논의합니다.
이를 통해 디지털 헬스케어가 개인 건강 관리와 의료 산업에 미치는 영향을 조명합니다.

제목: Digital Health Care in Samsung
목적: Samsung에서 개발하고 있는 Digital Health Care 소개
상세 내용:
1. Samsung Galaxy Wearable Sensor 소개
2. Samsung 에서 상품화 하고 있는 Wellness 와 Medical Device 서비스 소개
3. Samsung이 Digital Health Care 를 바라보는 방향
4. Samsung 개발자로써 바라보는 Digital Health Care 의 미래

Bioinstrumentation System for Digital Health Platform: Sleep Health Monitoring Technologies Using Watch-Type Wearable - Hyunjun Jung
2025-11-10 16:30 - 17:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Digital health leverages information and communication technologies to transform healthcare, enabling diverse solutions for continuous health management. Among these, wearable-based digital health plays a key role by collecting, monitoring, and analyzing physiological data over extended periods. In this lecture, I will introduce the sleep-related features of Samsung’s Galaxy Watch series, focusing on the biosignals that can be acquired from the wrist and how they are processed. I will also share practical insights from the research and validation processes that enabled these features. Through this, I aim to show how your specialized, in-depth research can be translated into real-world, impactful digital health applications, and what key factors must be considered in that process.

Biography
Hyunjun Jung is a Principle Engineer at Samsung Electronics’ MX Division (formerly Mobile Division), where he has been leading the research and development of various commercial solutions for the Galaxy Watch since April 2018. He played a pivotal role in the development of Samsung’s first wearable ECG device, the Galaxy Watch Active 2, and contributed to the FDA 510(k) approval for its AFib(Atrial Fibrillation) detection feature. Additionally, he spearheaded the development of the world’s first FDA-approved (De Novo) sleep apnea detection function. He has also independently developed and validated solutions for oxygen saturation, sleep score, respiration rate during sleep, and heart rate, aiming to deliver more accurate health monitoring features to users.

Mathematical modeling of infectious disease dynamics - Sang Woo Park
2025-11-12 15:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Abstract
Recent emergence and re-emergence of infectious disease pathogens have caused major disruptions to our society, highlighting the importance of managing ongoing outbreaks and predicting future epidemics. In this talk, I will use mathematical models to test biological hypotheses about pathogen transmission and leverage these findings to inform public health guidance. I will begin by modeling the transmission dynamics of Enterovirus D68 as a case study. I then use mathematical models from ecological perspective to answer questions about pathogen coexistence, responses to perturbations, and climate drivers. Overall, I will provide a broad overview to highlight the use of mathematical models in answering core questions in infectious disease ecology.

(Cancelled) TBD - Amir Sharafkhaneh
2025-11-12 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
–

Generative Models and Causality - Kyungwoo Song
2025-11-12 16:00 - 17:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
This seminar examines how generative AI advances three foundational tasks in causality, treated as distinct, modular problems: (1) causal inference via intervention‑effect estimation, (2) causal graph analysis, and (3) detection of causal mechanism shifts and change points. First, for causal inference, we consider procedures in which generative models align domain knowledge with observational signals to represent treatment, confounding, and temporal context. This enables stable estimation of intervention effects and principled policy evaluation without relying on explicit counterfactual generation. Second, for causal graph analysis, we outline strategies that combine language‑grounded knowledge extraction and constraint proposals with statistical checks to improve the reliability of directionality and structure, yielding interpretable hypothesis spaces and testable causal claims. Third, for shift detection, we describe methods that disentangle changes in functional mechanisms from changes in noise, supporting early diagnosis of performance degradation, targeting of monitoring resources, and evidence‑based model updates in deployed settings. Across these tasks, generative AI serves as a computational aide for knowledge alignment, hypothesis proposal and pruning, uncertainty annotation, and experiment‑design suggestions. We conclude with a brief outlook on a causal agent that orchestrates data ingestion, hypothesis formation, intervention‑effect estimation, shift monitoring, and policy revision, offering an integrated, yet auditable and modular, workflow for reliability‑centered decision support.

Modeling personalized heart rate response to exercise and environmental factors with wearables data - Dongju Lim
2025-11-21 10:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Modeling personalized heart rate response to exercise and environmental factors with wearables data” by Nazaret et al., npj digital medicine, 2023.
Abstract
Heart rate (HR) response to workout intensity re ects tness and cardiorespiratory health. Physiological models have been developed to describe such heart rate dynamics and characterize cardiorespiratory tness. However, these models have been limited to small studies in controlled lab environments and are challenging to apply to noisy—but ubiquitous—data from wearables. We propose a hybrid approach that combines a physiological model with exible neural network components to learn a personalized, multidimensional representation of tness. The physiological model describes the evolution of heart rate during exercise using ordinary differential equations (ODEs). ODE parameters are dynamically derived via a neural network connecting personalized representations to external environmental factors, from area topography to weather and instantaneous workout
intensity. Our approach ef ciently ts the hybrid model to a large set of 270,707 workouts collected from wearables of 7465 users from the Apple Heart and Movement Study. The resulting model produces tness representations that accurately predict full HR response to exercise intensity in future workouts, with a per-workout median error of 6.1 BPM [4.4–8.8 IQR]. We further demonstrate that the learned representations correlate with traditional metrics of cardiorespiratory tness, such as VO2 max (explained variance
0.81 ± 0.003). Lastly, we illustrate how our model is naturally interpretable and explicitly describes the effects of environmental factors such as temperature and humidity on heart rate, e.g., high temperatures can increase heart rate by 10%. Combining physiological ODEs with exible neural networks can yield interpretable, robust, and expressive models for health applications.
