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Tag: Interdisciplinary

Interdisciplinary Talk – Mathematical Modeling with Multidisciplinary Applications in Biological Systems

The Math Department’s Center for Integration of Undergraduate, Graduate, and Postdoctoral Research (I-Center), the Kansas Louis Stokes Alliance for Minority Participation (KS-LSAMP) and the Institute of Computational Comparative Medicine (ICCM) are very pleased to announce a seminar by Dr. Majid Jaberi-Douraki. jaberi_majid

Information for the seminar is listed below:

Monday, September 26, 2016
4:00 – 5:00 pm
Big 12 Room, K-State Union

Dr. Majid Jaberi-Douraki’s interests lie in developing biological and epidemiological models. He is an expert in dynamical systems, control theory and numerical analysis. More precisely, Professor Jaberi-Douraki studies autoimmune and infectious diseases, mechanisms of relapse-remission, protein folding in endoplasmic reticulum, pathogen-host dynamics, and age-structured modelling, as well as optimal control strategies, and vaccination/treatment/isolation policies.

These talks are meant to be accessible to non-specialists in the area, including undergraduate students, and feature developing research in interdisciplinary fields. In particular, we aim to highlight career options for students in STEM, as well as a offer a discussion of how to prepare for work in this area. We hope to have students and faculty from various departments in the audience who may be interested in the subject.

Abstract for the event is as follows:

Mathematical modeling with multidisciplinary applications describe the interdisciplinary nature of quantitative modeling using numerical and theoretical algorithms. In this talk, we plan to develop and design mathematical modeling techniques and real-world processes with applications in diverse fields of biology. It is worthwhile to point out that the complexity of processes underlying mechanisms of biological systems and the difficulty in  examining them experimentally make the use of quantitative approaches and predictive mathematical models very compelling. For this purpose, we will combine a variety of methods from computational perspective to dynamical system modeling using statistical tools such as Bayesian inference and sensitivity analysis to illustrate how these techniques can be employed to study quantitative analysis of biological processes, predict fundamental and long-term behavior of biological systems, and find solutions to challenging problems which remain incompletely understood.