Biomedical Mathematics Group

Evolution of error correction through a need for speed - Kangmin Lee
2026-08-14 10:00 - 12:00 (Asia/Seoul) | 108, Conference Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of
In this talk, we discuss the paper “Evolution of error correction through a need for speed” by Riccardo Ravasio et al., Science, 2026.
Abstract:
Kinetic proofreading is a class of error-correcting mechanisms in biology that expend energy to avoid mistakes during replication, transcription, and translation. Proofreading is typically assumed to evolve when selection for fidelity outweighs costs in energy and the speed of replication. We show that when stalling after misincorporations is accounted for, proofreading can instead speed up replication. Consistent with data on polymerase mutagenesis, our results suggest that proofreading can evolve under selection for speed alone. We generalize to multicomponent self-assembly and show that analogous error-correcting processes, such as dynamic instability, can likewise emerge purely from selection for rapid assembly. Thus, nonequilibrium error correction can evolve from selection for speed, even without direct fidelity advantages. We discuss implications for mutation-rate evolution, molecular assembly processes, and models of early life.

수학이 공학의 언어라면 - 민태기
2026-08-17 16:00 - 17:00 | 109, Conference room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of
이번 세미나에서는 “조선이 만난 아인슈타인”, “판타 레이” 등을 저술하신 민태기 소장님을 모시고 “수학이 공학의 언어라면”이라는 주제로 강연을 진행할 예정입니다.

Tangent-Space Regularization for Neural-Network Models of Dynamical Systems - Olive Cawiding
2026-08-21 10:00 - 12:00 | 108, Conference Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of
In this talk, we discuss the paper “Tangent-Space Regularization for Neural-Network Models of Dynamical Systems” by Fredrik B Carlson et al, arXiv (2026).
Abstract:
This work introduces the concept of tangent space regularization for neural-network models of dynamical systems. The tangent space to the dynamics function of many physical systems of interest in control applications exhibits useful properties, e.g., smoothness, motivating regularization of the model Jacobian along system trajectories using assumptions on the tangent space of the dynamics. Without assumptions, large amounts of training data are required for a neural network to learn the full non-linear dynamics without overfitting. We compare different network architectures on one-step prediction and simulation performance and investigate the propensity of different architectures to learn models with correct input-output Jacobian. Furthermore, the influence of L2 weight regularization on the learned Jacobian eigenvalue spectrum, and hence system stability, is investigated.

Towards a General Intelligence and Interface for Wearable Health Data - Aqsa Awan
2026-08-28 10:00 - 12:00 | 108, Conference Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of
In this talk, we discuss the paper “Towards a General Intelligence and Interface for Wearable Health Data” by Girish Narayanswamy et al., arXiv, 2026.
Abstract:
While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging. Specifically, converting low-level sensor data into representations capable of characterizing higher-level states is difficult due to high phenotypic diversity and variation in individual baseline health, physiology, and lifestyle factors. Moreover, collecting wearable data paired with health outcome annotations is laborious and expensive, and retrospective annotation remains practically unfeasible, contributing to a scarcity of data with high-quality labels. To overcome these limitations, we propose a foundation model for wearable health that is pretrained on more than one trillion minutes of unlabeled sensor signals drawn from a large cohort of five million participants. We demonstrate that the joint scaling of model capacity and pretraining data volume leads to systematic improvements in performance, as evaluated on a diverse set of 35 health prediction tasks, spanning cardiovascular, metabolic, sleep, and mental health, as well as lifestyle choices and demographic factors. We find that this population scale representation unlocks label-efficient few-shot learning and generative capabilities for robust daily metric estimation. To further leverage this learned representation, we deploy a classroom of LLM agents to autonomously search the space of downstream predictive heads built on the model embeddings, showing broad performance improvements that increase with LLM model capacity. Finally, we show how integrating these downstream predictors into a Personal Health Agent can support model responses that are more relevant, contextually aware, and safe, and we validate this via 1,860 ratings from a cohort of clinicians.
