Wednesday 10:00 -10:50 am, https://ucr.zoom.us/j/97606227247
Organizers : Weitao Chen / Heyrim Cho / Yat Tin Chow / Qixuan Wang / Jia Gou / Mykhailo Potomkin / Yiwei Wang / Maziar Raissi / Siting Liu
Past Organizers : Mark Alber / James Kelliher / Amir Moradifam
Archives: Fall 2019 / Winter 2020 / Spring 2020 / Fall 2020 / Winter 2021 / Spring 2021 / Fall 2021 / Winter 2022 / Spring 2022 / Fall 2022 / Winter 2023 / Spring 2023 / Fall 2023 / Winter 2024 / Spring 2024 /Fall 2024 / Winter 2025 / Spring 2025 / Fall 2025
Links : Interdisciplinary Center for Quantitative Modeling in Biology (ICQMB) seminar
In Fall 2025, the PDE & Applied Math seminar will be held through Zoom or in person (Skye 268). Specific information about the format of each talk will be provided in the email announcement and posted below. If you're interested in attending the seminar, please contact Dr. Yat-Tin Chow (yattinc@ucr.edu) and Dr. Yiwei Wang (yiweiw@ucr.edu).
Fall 2025 Schedule
Oct 01 10:00 (Wed) Organizational meeting
Oct 08 10:00 (Wed) Dr. Yifan Chen (UCLA) online
Oct 15 10:00 (Wed) Dr. Seirin Lee (Kyoto University) in-person
Oct 22 10:00 (Wed) Dr. Yiping Lu (Northwestern University) online
Oct 29 10:00 (Wed) Dr. Romit Maulik (Penn State University) online
Nov 05 10:00 (Wed) Dr. Jiequn Han (Flatiron Institute) online
Nov 12 10:00 (Wed) Dr. Rongjie Lai (Purdue University) online
Nov 19 10:00 (Wed) Dr. Truong Vu (IPAM and Michigan State) online
Nov 26 10:00 (Wed)
Dec 03 10:00 (Wed) Dr. Arash Fathi (ExxonMobil Corporate Strategic Research) online
Upcoming Talks:
Oct 22, 2025, 10:00 AM - 11:00 AM PT
Dr. Yiping Lu (Northwestern University)
Title: Scaling Scientific Machine Learning: Integrating Theory and Numerics in Both Training and Inference
Abstract: Scaling scientific machine learning (SciML) requires overcoming bottlenecks at both training and inference. On the training side, we study the statistical convergence rate and limits of deep learning for solving elliptic PDEs from random samples. While our theory predicts optimal polynomial convergence for PINNs, optimization becomes prohibitively ill-conditioned as networks widen. By adapting descent strategies to the optimization geometry, we obtain scale-invariant training dynamics that translate polynomial convergence into concrete compute and yield compute-optimal configurations. On the inference side, I will introduce Simulation-Calibrated SciML (SCaSML), a physics-informed post-processing framework that improves surrogate models without retraining or fine-tuning. By enforcing physical laws, SCaSML delivers trustworthy corrections (via Feynman-Kac simulation) with approximate confidence intervals, achieves faster and near-optimal convergence rates, and supports online updates for digital twins. Together, these results integrate theory and numerics to enable predictable, reliable scaling of SciML in both training and inference. This is based on joint work with Lexing Ying, Jose Blanchet, Haoxuan Chen, Zexi Fan, Youheng Zhu, Shihao Yang, Jasen Lai, Sifan Wang, and Chunmei Wang.
Oct 08, 2025, 10:00 AM - 11:00 AM PT
Dr. Yifan Chen (UCLA)
Title: Exploring high dimensions in dynamical sampling: flattening the scaling curve
Abstract: Dynamical sampling of probability distributions based on model or data (i.e., generative modeling) is a central task in scientific computing and machine learning. I'll present recent work on understanding and improving algorithms in high-dimensional settings. This includes a novel "delocalization of bias" phenomenon in Langevin dynamics, where biased methods could achieve dimension-free scaling for low-dimensional marginals while unbiased methods cannot—a finding motivated by molecular dynamics simulations. I'll also briefly mention a new unbiased affine-invariant Hamiltonian sampler that outperforms popular samplers in emcee package (routinely used in astrophysics literature) in high dimensions, and introduce the optimal Lipschitz energy criteria for design of measure transport in generative modeling of multiscale scientific data, as alternative to optimal kinetic energy in optimal transport. These examples show how dimensional scaling could be flattened, allowing efficient stochastic algorithms for high-dimensional sampling and generative modeling in relevant scientific applications.
Oct 15, 2025, 10:00 AM - 11:00 AM PT
Dr. Seirin Lee (Kyoto University)
Title: Pattern Formation in Skin Diseases and Its Application to Personalized Treatment in Dermatology
Abstract: Skin diseases typically appear as visible information-skin eruptions distributed across the body. However, the biological mechanisms underlying these manifestations are often inferred from fragmented, time-point-specific data such as skin biopsies. The challenge is further compounded for human-specific conditions like urticaria, where animal models are ineffective, leaving researchers to rely heavily on in vitro experiments and sparse clinical observations.To overcome the current limitations in understanding the pathophysiology of skin diseases, we propose a novel framework that connects the visible morphology of skin eruptions with the underlying pathophysiological dynamics in vivo, using a multidisciplinary approach that integrates mathematical modeling, in vitro experiments, clinical data, and data science. Furthermore, we will introduce an innovative methodology that combines mathematical modeling with topological data analysis and machine learning, allowing for the estimation of patient-specific parameters directly from morphological patterns of skin eruptions. This framework offers a new pathway for personalized analysis and mechanistic insight into
complex skin disorders.
Bio: Prof. Seirin-Lee is a Professor of Mathematical Medicine at the Kyoto University Institute for Advanced Study (KUIAS) and the Graduate School of Medicine, Kyoto University. She obtained her PhD from Okayama University, Japan in 2010, having conducted part of her doctoral research at the Center for Mathematical Biology, University of Oxford, as a JSPS DC1 fellow. After postdoctoral training at the University of Tokyo and RIKEN, she was appointed Assistant Professor in 2014, Associate Professor in 2017, and Full Professor in 2020 at Hiroshima University. Since 2021, she has held her current position at Kyoto University. Her research focuses on pattern formation, mathematical medicine,
mathematical dermatology, spatial immunology, and applied mathematics. Website：https://ashbi.kyoto-u.ac.jp/bimed-math/