기초과학연구원 의생명수학그룹
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 …
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 …
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 …
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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 …
In this talk, we discuss the paper "Homeostatic forces underlying the daily pattern of sleep propensity" by Vasili Kharchenko et al., SLEEP Advances, 2025. Abstract The mismatch between rising sleep need and the fluctuating ability to fall asleep underlies insomnia—the most common sleep disorder—yet remains poorly understood. While sleep need increases steadily with time awake, …
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, …
In this talk, we discuss the paper "FilterNet: Harnessing Frequency Filters for Time Series Forecasting" by Kun Yi et al., NeurIPS, 2024. Abstract Given the ubiquitous presence of time series data across various domains, precise forecasting of time series holds significant importance and finds widespread real-world applications such as energy, weather, healthcare, etc. While numerous …
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 …