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Extra Form
Lecturer 김재경
Dept. KAIST
date Mar 24, 2016

The revolution of molecular biology in the early 1980s has revealed complex network of non-linear and stochastic biochemical interactions underlying biological systems. To understand this complex system, mathematical models have been widely used. In this talk, I will introduce the typical process of mathematical modelling including mathematical representation, model fitting to data, analysis and simulations, and experimental validation. Across each step of modelling process, I will also describe our efforts to develop a new mathematical tools and point to the parts of current toolbox of mathematical biology that need further mathematical development. Finally, I will present how mathematical modelling reveals the key mechanism underlying circadian clocks.   


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  1. Hybrid discontinuous Galerkin methods in computational science and engineering

  2. The phase retrieval problem

  3. Theory and applications of partial differential equations

  4. Analysis and computations of stochastic optimal control problems for stochastic PDEs

  5. <학부생을 위한 ε 강연> 압축센싱과 행렬완성

  6. <학부생을 위한 ε 강연> 수학과 예술 - 초기 컴퓨터 그래픽

  7. Faithful representations of Chevalley groups over quotient rings of non-Archimedean local fields

  8. Quasi-homomorphisms into non-commutative groups

  9. Entropies on covers of compact manifolds

  10. Iwahori-Hecke algebras and beyond

  11. On the resolution of the Gibbs phenomenon

  12. <학부생을 위한 ε 강연> What mathematics can do for the real and even fake world

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    The process of mathematical modelling for complex and stochastic biological systems

  14. Random walks in spaces of negative curvature

  15. Solver friendly finite element methods

  16. Brownian motion and energy minimizing measure in negative curvature

  17. 학부생을위한ε강연: 수학자는 왜 선망되는 직업일까?

  18. Generalized multiscale HDG (hybridizable discontinuous Galerkin) methods for flows in highly heterogeneous porous media

  19. Weyl character formula and Kac-Wakimoto conjecture

  20. Nonlocal generators of jump type Markov processes

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