Sanghyun Lee, Ph.D

● Fall 2017 - Assistant Professor, Department of Mathematics, FSU
  Spring 2018 - Associate of Geophysical Fluid Dynamics Institute (GFDI), FSU : Link

● Florida State University
  Department of Mathematics
  208 Love Building
  1017 Academic Way
  Tallahassee, FL 32306-4510
● Office : LOV 002-D
  Phone: 850-644-1587
  Fax: 850-644-4053
  Skype : sanghyun.lee25
  EM: lee(@)

News and Upcoming Events

● [12nd Dec 2019] ARPA-E Subsurface Cohort Kickoff, Denver, CO, U.S.A

● [27th Jan 2020] ErSE Seminar Talk, Division of Physical Science and Engineering, King Abdullah University of Science and Technology Link

● [23rd-28th Feb 2020] Teeratorn Kadeethum's visit from DTU (link)

● [3rd - 4th Mar 2020] The Center for Subsurface Modeling, The University of Texas at Austin

● [14th-15th Mar 2020] The 44th SIAM Southeastern Atlantic Section Conference (link), Auburn University, USA

● [11th-13th May 2020] The 50th John H. Barrett Memorial Lectures, A3N2M: Approximation, Applications, and Analysis of Nonlocal, Nonlinear Models, (link), The University of Tennessee, Knoxville, USA

● [21st-25th Jun 2020] Computational Methods in Water Resources XXIII (CMWR) 2020 (link), Stanford University, USA

● [19th-24th July 2020] WCCM & ECCOMAS Congress 2020, Paris, France (link)

Research Interest

● Design, analysis and implementation of numerical methods for partial differential equations.

● Computational mathematics with high performance computing in the area of interdisciplinary multi physics and multi scale real world problems

● Free boundary multiphase problems employing projection methods for Navier Stokes systems and level set methods with adaptive finite element methods. Link

● Analyses and computations of newly developed 'enriched Galerkin' approximation methods for coupling flow and transport for complexed fluid in porous media.

● Big data analytics for extraction of fracture related information in subsurface systems and advanced computational approaches for modeling fracture propagation by using Biot system and phase field to couple flow and geomechanics.

● Employing Machine learning/Deep learning/Neural networks process for solving partail differential equations