Biomedical Mathematics Group

Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics - Kang Min Lee
2025-09-05 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 “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.

2025 KAI-X Global Conference in Sleep Synergy
2025-12-01 09:30 - 2025-12-02 15:00 | KAIST W13 Conference Room (1F), 291 Daehak-ro Yuseong-gu, Daejeon, Daejeon, 34141, Korea, Republic of
Conference Webpage Link: https://sites.google.com/view/2025-kai-x-sleep-synergy/home

Empirical modeling of bifurcations and chaos from time series - Stephan Munch
2025-12-05 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract
Many natural systems exhibit complex dynamics and are prone to sudden changes or ‘regime shifts’. At the same time, many of these systems are sparsely observed posing considerable challenges for modeling and control. Here I will describe recent developments in empirical dynamic modeling (EDM) for inference of bifurcations and anticipation of unseen dynamical regimes from ecological time series.

Quantifying interventional causality by knockoff operation - Yun Min Song
2025-12-12 11:00 - 12:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Causal disentanglement for single-cell representations and controllable counterfactual generation” by Yicheng Gao et al., Nature Communications, 2025.
Abstract
Conducting disentanglement learning on single-cell omics data offers a promising alternative to traditional black-box representation learning by separating the semantic concepts embedded in a biological process. We present CausCell, which incorporates the factual information about causal relationships among disentangled concepts within a diffusion model to generate more reliable disentangled cellular representations, with the aim of increasing the explainability, generalizability and controllability of single-cell data, including spatial-temporal omics data, relative to those of the existing black-box representation learning models. Two quantitative evaluation scenarios, i.e., disentanglement and reconstruction, are presented to conduct the first comprehensive single-cell disentanglement learning benchmark, which demonstrates that CausCell outperforms the state-of-the-art methods in both scenarios. Additionally, CausCell can implement controllable generation by intervening with the concepts of single-cell data when given a causal structure. It also has the potential to uncover biological insights by generating counterfactuals from small and noisy single-cell datasets.

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting - Jinwoo Hyun
2025-12-26 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 “N-BEATS: Neural basis expansion analysis for interpretable time series forecasting” by B. Oreshkin et al., ICLR, 2020.
Abstract
We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable without modification to a wide array of target domains, and fast to train. We test the proposed architecture on several well-known datasets, including M3, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for two configurations of N-BEATS for all the datasets, improving forecast accuracy by 11% over a statistical benchmark and by 3% over last year’s winner of the M4 competition, a domain-adjusted hand-crafted hybrid between neural network and statistical time series models. The first configuration of our model does not employ any time-series-specific components and its performance on heterogeneous datasets strongly suggests that, contrarily to received wisdom, deep learning primitives such as residual blocks are by themselves sufficient to solve a wide range of forecasting problems. Finally, we demonstrate how the proposed architecture can be augmented to provide outputs that are interpretable without considerable loss in accuracy.

Distribution shift in machine learning: robustness, invariance, and a causal view - Wooseok Ha
2025-12-29 16:30 - 18:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Classical machine learning models are typically trained under the assumption that the training (source) and test (target) data are drawn from the same distribution. However, real-world data are rarely clean or consistent, and distribution shifts between the source and target domains are ubiquitous. Despite its importance, addressing distribution shifts is highly difficult. The fundamental challenge is that the problem is mathematically ill-posed: shifts can occur in many different forms, and no single method can handle all of them. While numerous algorithms have been proposed in recent years to solve distribution shifts, most are empirical-driven and lack solid foundations. In this talk, I will provide a broad overview of approaches to address distribution shift based on invariance and distributional robustness, and explain how these methods are intrinsically connected to a causal perspective. In particular, I will show why it is crucial to carefully formulate assumptions that relate the source and target domains for reliable generalization, and how assumptions grounded in the causal system enable the analysis of algorithms under both unsupervised and semi-supervised settings.

Expanding the Data Analysis Toolkit: Explainable AI, Causal Learning, and Time-Series Foundation Models - Daeil Jang
2025-12-30 15:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Recent advances in data science have expanded the scope of data analysis beyond prediction accuracy toward interpretability, causal understanding, and generalizable learning across complex data structures. This lecture introduces three emerging methodological approaches that can be directly leveraged in modern data analysis workflows.
First, the lecture presents explainable artificial intelligence (XAI) techniques, focusing on SHAP and its extension to time-series explainability, to illustrate how model predictions can be decomposed into meaningful variable- and time-specific contributions. Second, it introduces machine-learning and deep-learning–based causal inference models, highlighting how these methods move beyond association to estimate intervention effects and heterogeneous impacts while maintaining interpretability. Third, the lecture explores recent time-series foundation models—such as Lag-LLaMA and TabPFN-based approaches—that enable transferable learning across diverse time-series tasks with minimal task-specific training.
Rather than treating these approaches as isolated research trends, this lecture frames them as complementary analytical tools that address key questions in data analysis: What drives model predictions? What would change under intervention? And how can models generalize across time and settings? Through this integrated perspective, the lecture aims to provide practical insight into how these three methods can be applied to real-world data analysis and inspire new research and application opportunities.

Rationalizing Therapeutics: Mathematical Insights into Drug and Cell Therapy Development - Seokjoo Chae
2025-12-30 16:00 - 17:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Mathematical modeling provides essential quantitative insights that accelerate drug and cell therapy development. In this presentation, we utilize kinetic frameworks to optimize the design of molecular glues by elucidating their biophysical determinants and identify a key target for NK cell-mediated immunotherapy through systematic data analysis. Collectively, we demonstrate how mathematical strategies can effectively guide and advance the development of next-generation therapeutics.

Seasonal timing and interindividual differences in shiftwork adaptation - Kang Min Lee
2026-01-02 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 “Seasonal timing and interindividual differences in shiftwork adaptation” by R. Kim et al., npj digital medicine, 2025.
Abstract
Millions of shift workers in the U.S. face an increased risk of depression, cancer, and metabolic disease, yet individual responses to shift work vary widely. We find that a conserved biological system of morning and evening oscillators, which evolved for seasonal timing, may contribute to these interindividual differences. In this study, we analyze seasonality in medical interns working shifts, revealing that summer-winter variation correlates with increased circadian misalignment after shift work. Mathematical modeling suggests that seasonal timing influences the rate of adaptation to new schedules, predicting differential effects on morning and evening oscillators. Additionally, we examine genetic polymorphisms linked to seasonality in animals and find that human variants can impact how quickly circadian rhythms respond to schedule changes. Based on our findings, we hypothesize that the vast interindividual differences in shift work adaptation—critical for shift worker health—can in part be explained by biological mechanisms for seasonal timing.

scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction - Aqsa Awan
2026-01-09 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 “scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction” by Z. Liang et al., arxiv, 2025.
Abstract
This paper introduces the Single-Cell Perturbation Prediction Diffusion Model (scPPDM), the first diffusion-based framework for single-cell drug-response prediction from scRNA-seq data. scPPDM couples two condition channels, pre-perturbation state and drug with dose, in a unified latent space via non-concatenative GD-Attn. During inference, factorized classifier-free guidance exposes two interpretable controls for state preservation and drug-response strength and maps dose to guidance magnitude for tunable intensity. Evaluated on the Tahoe-100M benchmark under two stringent regimes, unseen covariate combinations (UC) and unseen drugs (UD), scPPDM sets new state-of-the-art results across log fold-change recovery, delta correlations, explained variance, and DE-overlap. Representative gains include +36.11%/+34.21% on DEG logFC-Spearman/Pearson in UD over the second-best model. This control interface enables transparent what-if analyses and dose tuning, reducing experimental burden while preserving biological specificity.

Leveraging Large-Scale Perturbome Data for Complex Disease Target Discovery- Sang-Min Park
2026-01-14 15:00 - 17:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Complex diseases, such as cancer, sarcopenia, and immune disorders, arise from abnormalities in multiple genes and pathways, posing significant challenges to conventional single-target drug discovery strategies. To address this, we developed a perturbome-based analytical framework that integrates transcriptomic signatures, network pharmacology, and machine learning to identify effective therapeutic candidates. Central to this approach is the KORE-Map (Korean Medicine Omics Resource Extension Map), a systematically curated transcriptomic repository of herbal medicine perturbations in diverse cellular and disease contexts. Using KORE-Map, we reconstructed perturbome landscapes of traditional prescriptions such as Bojungikki-tang and Jakyak-gamcho-tang, as well as natural compounds including ginsenosides and licochalcone B. By integrating differential expression, pathway activity, and synergic index modeling, we demonstrated how perturbome data can reveal druggable axes for overcoming resistance to targeted and immune therapies in cancer, attenuating muscle atrophy, and modulating inflammatory responses. Importantly, perturbome-guided candidate prioritization was validated through multi-omics profiling and functional assays, underscoring its translational value. Our findings highlight perturbome data analysis as a powerful strategy for navigating the complexity of disease biology and accelerating drug discovery.

Quantifying interventional causality by knockoff operation - Olive Cawiding
2026-01-15 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, “Quantifying interventional causality by knockoff operation” by Xinyan Zhang and Luonan Chen, Science Advances, 2025.
Abstract
Causal inference between measured variables is crucial to understand the underlying mechanism of complex biological processes at a network level but remains challenging in computational biology. We propose an innovative causal criterion, knockoff conditional mutual information (KOCMI), to accurately infer interventional direct causality without prior knowledge of the network structure using either time-independent or time-series data. KOCMI performs knockoff operation on a variable as its virtual intervention, which preserves the original network structure, and then identifies the causality between two variables by estimating the distributional invariance before and after such a virtual intervention. We show that, algorithmically, KOCMI enables quantification of causal relationship, even for networks with loops, and, theoretically, is also consistent with the do-calculus causal analyses but without their prerequisite of the network structure. KOCMI shows superior performance on benchmark and real datasets, comparing with existing methods. Overall, KOCMI provides a powerful tool in inferring interventional causality, which is theoretically ensured and experimentally validated by real intervention data.
