Why, How, What can a mathematician contribute in machine learning?
– Brown University (Ph. D): PDE, May 2002
POSTECH (MS) : Number Theory, Feb. 1997
POSTECH (BS) : Mathematics, Feb. 1995
-POSTECH (07 2006 – present) Assistant ~ Full Professor
PCAM(포스텍 수리응용센터) (01 2017 – present) Director
POSTECH Blockchain Technology Center(06 2018-Present), Vice Director
Brown University (01 2015 – 12 2015) Visiting Professor
Trinity College Dublin (09 2005 -07 2007) Permanent Lecturer (equiv. to Assistant Professor)
Max-Planck-Institute (07 2005 – 08 2005) Visiting Scholar
Duke University (08 2003 – 07 2005) Assistant Research Professor
Max-Planck-Institute (10 2002 – 07 2003) Postdoc
Deep learning has shown remarkable achievements ever since hardware improvement enables heavy parallel computations through GPUs. Despite of its achievements in numerous fields such as image processing, natural language processing and etc, our theoretic understanding of its principles is far less studied compared to applications. In this seminar, we introduce some studies to figure out fundamentals of existing deep learning methodologies. First, we introduce the theoretic study of analytically showing convergence conditions of GANs. Second, we introduce the principled methodology to estimate reward functions in reinforcement learning. Lastly, we introduce the principled method to solve PDEs using neural networks.