Computational Scientist & Software Engineer
Mujin Robotics Controller Inc., Tokyo, Japan
PhD of Computational Science and Engineering
McMaster University, Canada
Research interest: numerical analysis, scientific computing and programming, structural analysis of differential-algebraic equations (DAEs), numerical methods for ODEs/DAEs, Matlab/Python/C++ programming, automatic differentiation, inverse kinematics, data structure and algorithms
Guangning Tan (TGN, 谭广宁), was born and raised in Guangzhou, China. He received in 2010 his Bachelor degree in Communication and Electrical Engineering at Sun Yat-sen University, Guangzhou, and in 2012 and 2016 his M.Eng and PhD respectively in Computational Science & Engineering at McMaster University, Canada. While at McMaster, he was the main author of the DAESA package, a Matlab tool for structural analysis of differential-algebraic equation systems.
After finishing post-doc at MIT, TGN joined Mujin Robot Controller Inc. (Tokyo, Japan) and became the Lead Computational Scientist and Software Engineer. He tackles one of the most difficult computational problems in robotics kinematics automation. He authored IKFast++, a C++ package that solves inverse kinematics (IK) problems of any industrial robots. Within IKFast++ he has developed sophisticated mathematical theories, state-of-the-art numerical & combinatorial algorithms, and efficient modern C++ code. IKFast++ drastically exceeds the capabilities of its predecessor ikfast.py originated by Mujin's CTO Dr. Rosen Diankov, and generates much more reliable and versatile C++ solvers that can be used in numerous robotics automation applications.
DAESA: A Matlab Tool for Structural Analysis of Differential-Algebraic Equations: Theory, ACM Trans. Math. Softw., 41 (2015), pp. 9:1-9:20. PDF
Algorithm 948: DAESA: A Matlab Tool for Structural Analysis of Differential-Algebraic Equations: Software, ACM Trans. Math. Softw., 41 (2015), pp. 12:1-12:14. PDF
Conversion Methods for Improving Structural Analysis of Differential-Algebraic Equation Systems. BIT Numerical Mathematics, Volume 57, Issue 3, pp 845–865
How Algorithmic Differentiation Can Help Solve Differential-Algebraic Equations. Optimization Methods and Software, DOI: 10.1080/10556788.2018.1428605