Biomedical Mathematics Seminar

Title: Theoretical studies on biological oscillations by using waveform data and mathematical models Abstract: Temporal waveforms of biological oscillations are of various shapes. In our research, we have explored the functional implications of these waveform shapes. In particular, we theoretically showed that the period of circadian clocks is proportional to the waveform distortion from sinusoidal wave. It suggests that the circadian period can be stable against temperature changes only if the waveform becomes more distorted at higher temperatures. In this talk, I will explain my past research and discuss my future plans. Reference: Shingo Gibo, Gen Kurosawa, Non-sinusoidal Waveform in Temperature Compensated Circadian Oscillations, Biophysical Journal 116 (4) 741-751 (2019). doi: 10.1016/j.bpj.2018.12.022 Shingo Gibo, Gen Kurosawa, Theoretical study on the regulation of circadian rhythms by RNA methylation, Journal of Theoretical Biology 490, 110140 (2020). doi; 10.1016/j.jtbi.2019.110140 Shingo Gibo, Teiji Kunihiro, Tetsuo Hatsuda, Gen Kurosawa, Waveform …

In this talk, we discuss the paper : “Solving Inverse Problems in Medical Imaging with Score-Based Generative Models” by Y Song et al. Reconstructing medical images from partial measurements is an important inverse problem in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Existing solutions based on machine learning typically train a model to directly …

In this talk, we discuss the paper : “Diffusion Posterior Sampling for Linear Inverse Problem Solving- A Filtering Perspective” by Z. Dou& Y. Song Diffusion models have achieved tremendous success in generating high-dimensional data like images, videos and audio. These models provide powerful data priors that can solve linear inverse problems in zero shot through …

Abstract: In this talk, we propose novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementation of the classical GP-UCB algorithm, but the additional random exploration step accelerates their convergence, nearly achieving the optimal convergence rate. …

Over the past decade, the phase behavior of biomolecules has garnered significant attention, particularly due to its biological implications, such as the reversible formation and dissociation of biomolecular condensates. These condensates perform diverse and essential functions within cells, including the acceleration of chemical reactions. Recent advances aim to uncover the fundamental principles of these systems …

본 세미나에서는 성신여자대학교 서수연 교수님께서 "심리학이 알려주는 연구의 기술: 논리적 사고부터 논문 작성까지"라는 내용으로 강연을 해주실 예정입니다.    

Abstract: 임의로 정한 Initial Distribution Q1 와 Terminal Distribution Q2가 주어 졌을 때 시점과 종점 사이의 contiinious time상에  정의 되는 의미 있는 최적의 Probability Path Measure P 를 찾는 Schrodinger Bridges Problem 은 자연과학,공학, 의료 및 생명공학,경제학 및 금융공학 등의 여러 분야에 나타나는 모델들을 푸는 Unified AI Model 사용 되고 있습니다. Schrodinger Bridges Problem은  유일한 해가 존재 하는 정리는( Follmer,1988)  증명 되었으므로 데이터를 이용하여  Neural Network Models에 대한 효율적으로 학습방법,  빠른 알고리즘 연구에 집중 되고 있습니다. Tong et al 연구팀은 2023년 부터 ODE에 기반한 획기적인 생성모델인  Flow Matching for Generative Modeling 기법을  SDE 기반 Diffusion Generative Models에 접목하여 Schrodinger Bridges Problem의 해법을 제시하였습니다.

Abstract: This talk consists of two main parts. In the first part, I will discuss a numerical method for solving PDEs based on a stochastic representation of the solution. This approach captures the underlying particle dynamics associated with the physical processes described by the PDE. By aggregating information from the particles’ collective exploration, the method …

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.

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 …