Author: boyarsky

Mathematical Biology Seminar (Modeling the Pancreatic Cancer Microenvironment in Search of Control Targets)

Speaker: Daniel Plaugher, University of Kentucky Pancreatic Ductal Adenocarcinoma is among the leading causes of cancer-related deaths globally due to its extreme difficulty to detect and treat. Recently, research focus has shifted to analyzing the microenvironment of pancreatic cancer to better understand its key molecular mechanisms. This microenvironment can be represented with a multi-scale model … Continue reading Mathematical Biology Seminar (Modeling the Pancreatic Cancer Microenvironment in Search of Control Targets)

October 6, 2021, 3:10-4:00pm | 401 Carver Hall in person or by zoom

Problem Factories: almost endless problem generation (Mathematics Colloquium)

Speaker: Dan Ashlock (University of Guelph) Abstract: Math educators often have a few cool problems that can be used to dispel the idea that math is awful, a view too often inculcated by pre-university math education. Some of these wonderful problems fit into larger frameworks which we call problem factories. The idea for problem factories … Continue reading Problem Factories: almost endless problem generation (Mathematics Colloquium)

October 5, 2021, 4:10-5:00pm | Zoom

ISU Discrete Math Seminar

Speaker: Ting-Wei Chao (Carnegie Mellon University) Title: Finite Field Kakeya Problem A set K in the n-dimensional vector space FqnF_q^nFqn​ over finite field FqF_qFq​ is called a Kakeya set if it contains a line in every direction. Dvir proved that the size ∣K∣ is at least cnqnc_nq^ncn​qn, where cn=1/n!c_n=1/n!cn​=1/n! by using polynomial method. Recently, We … Continue reading ISU Discrete Math Seminar

September 30, 2021, 2:10-3:00pm | Zoom

Mathematics and Deep Learning Collective

Dr. Levon Nurbepkyan, from UCLA Title: A neural network approach for high-dimensional real-time optimal control Abstract: Due to fast calculation at the deployment, neural networks (NN) are attractive for real-time applications. I will present one possible approach for training NN to synthesize real-time controls. A key aspect of our method is the combination of the … Continue reading Mathematics and Deep Learning Collective

October 1, 2021, 4:00-5:00pm | Zoom  

Computational and Applied Mathematics (CAM) Seminar

Please note special time! Title:   Weak Solutions in Nonlinear Poroelasticity with Incompressible Constituents Speaker: Boris Muha University of Zagreb, Croatia Abstract: We consider quasi-static poroelastic systems with incompressible constituents, nonlinear permeability dependent on solid dilation, and physical types of boundary conditions (Dirichlet, Neumann, and mixed) for the fluid pressure, motivated by applications in bio mechanics … Continue reading Computational and Applied Mathematics (CAM) Seminar

September 20, 2021, 11:00am-12:00pm | 401 Carver Hall (in person)

Mathematical Biology Seminar

Speaker: Kristina Moen, ISU Mathematics Department Title: Tracking the Molecular Evolution of SARS-CoV-2: A Topological Approach   Abstract: COVID-19, the disease caused by the SARS-CoV-2 virus, has triggered an unprecedented global pandemic and caused an estimated 4.5 million deaths worldwide. As the virus spreads, it mutates and can become more infectious, virulent, and unpredictable. To … Continue reading Mathematical Biology Seminar

September 15, 2021, 3:10-4:00pm | 401 Carver Hall in person or by zoom 

Math and Deep Learning Collective

Speaker: Mishra Siddhartha, ETH — Switzerland Title: Deep Learning and Computations of high-dimensional PDEs Abstract: Partial Differential Equations (PDEs) with very high-dimensional state and/or parameter spaces arise in a wide variety of contexts ranging from computational chemistry and finance to many-query problems in various areas of science and engineering. In this talk, we will survey recent … Continue reading Math and Deep Learning Collective

April 30, 2021, 10:00-11:00am | Zoom

Mathematics and Deep Learning (MDL) Collective Seminar: Solving Inverse Problems with Deep Learning

Speaker:  Lexing Ying (Stanford University) Abstract: This talk is about some recent progress on solving inverse problems using deep learning. Compared to traditional machine learning problems, inverse problems are often limited by the size of the training data set. We show how to overcome this issue by incorporating mathematical analysis and physics into the design … Continue reading Mathematics and Deep Learning (MDL) Collective Seminar: Solving Inverse Problems with Deep Learning

April 16, 2021, 4:00-5:15pm | Zoom