My godparents (left) and parents (right),  May 2012

Taken in Elly's SYSU dorm in June 2010

Dr. ​Gary Guangning Tan (谭广宁)

Lead Computational Researcher / Software Engineer, Mujin Inc., Tokyo

PhD of Computational Science and Engineering

McMaster University, Canada


LinkedIn: https://www.linkedin.com/in/tgn3000
Résumé: PDF

CV: PDF  中文简历
Publications: PDF BibTex
Cover letter:  PDF
ORCID page:  orcid.org/0000-0001-7983-2691

Docs page: docs.com/tgn3000


​A quick guide to DAESA (Differential-Algebraic Equations Structural Analyzer)


​Research interest: numerical methods for differential equations,

numerical analysis, scientific computing in Matlab, Python, Julia, C, C++,

scientific programming


Research focus: structural analysis of differential-algebraic equations (DAEs),

automatic differentiation, interval arithmetic, computer algebra,

inverse kinematics

研究方向：微分代数方程, 结构分析 (Sigma方法), 三角分块, 自动微分, 并行计算, 

GPU编程, 常/偏微分方程数值解法, 科学计算与数值分析, 区间运算, 逆运动学


Biography

​Guangning Tan, a.k.a TGN, was born and raised in Guangzhou, Guangdong, China. He received Master of Engineering & PhD degrees in Computational Science & Engineering at McMaster University, Hamilton, Ontario, Canada. After eight months' postdoc career at MIT, TGN joined Mujin Inc. (Tokyo) as the lead computational researcher and software engineer. He directly collaborates with Mujin's CTO Dr. Rosen Diankov to tackle one of the most difficult problems in robotics kinematics automation. TGN is actively developing theory, computational and combinatorial algorithms, numerical methods, and efficient numerical code to solve practical kinematics problems encountered in robotics.


​Education
- PhD in Computational Science and Engineering (CSE), McMaster, 2012—2016
Thesis: Conversion methods for improving structural analysis of DAEs 
Manuscript  PDF      Defense Slides  PDF
- Master in Computational Science and Engineering, McMaster, 2010—2011
- Bachelor in Communication and Electrical Engineering
School of Information Science and Technology, Sun Yat-sen University (中山大学)
Guangzhou, China, 2006—2010


Teaching @ McMaster


Software

DAESA -- Differential-Algebraic Equations Structural Analyzer (MATLAB)

http://tgn3000.com/daesa.html
http://www.cas.mcmaster.ca/~nedialk/daesa


Selected talks​

  • Computing derivatives of a DAE solution in parallel. South Ontario Numerical Analysis Day 2016, University of Waterloo
  • ​ ____________. Best presentation in the 2016 CSE Student Symposium, McMaster
  • Conversion methods for improving structural analysis of DAEs. Invited NA seminar at DCS, University of Toronto
  • ____________. Interview seminar talk at PSEL, Massachusetts Institute of Technology
  • Symbolic-numeric methods for improving structural analysis of DAEs. SONAD-ACMES conference. Western University, London, Ontario, Canada. 2015. Slides. Recording on Youtube


Published articles

  • How automatic differentiation can help solve differential-algebraic equations. Submitted to Optimization Methods and Software, 2017. 19 pages arXiv:1703.08914
  • Conversion methods for improving structural analysis of differential-algebraic equation systems. BIT Numerical Mathematics, April 2017. 20 pages arXiv:1608.06691
  • 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​​


Book chapters and technical reports

  • ​Conversion methods, block triangularization, and structural analysis of DAEs. Submitted to BIT, 2016 arXiv:1608.06693
  • ​Symbolic-numeric methods for improving structural analysis of DAEs. Mathematical and Computational Approaches in Advancing Modern Science and Engineering, pp. 763-773. Springer International Publishing, Cham (2016) DOI 10.1007/978-3-319-30379-6 68
  • Symbolic-numeric methods for improving structural analysis of DAEs. Report CAS-15-07-NN, Dept. of Computing and Software, McMaster University, May 2015 PDF
  • Exploiting fine block triangularization and quasilinearity in DAEs. Tech. Report CAS-14-08-NN, 2014  PDF
  • Graph theory, irreducibility, and structural analysis of DAEs. Tech. Report CAS-14-09-NN, 2014  PDF
  • A simple method for quasilinearity analysis of DAEs, Interdisciplinary Topics in Applied Mathematics, Modeling and Computational Science, pp. 367--373. Springer International Publishing, Cham (2015) DOI 10.1007/978-3-319-12307-3-64
  • Exploiting block triangular form for solving DAEs: reducing the number of initial values. Interdisciplinary Topics in Applied Mathematics, Modeling and Computational Science, pp. 367--373. Springer International Publishing, Cham (2015)  DOI 10.1007/978-3-319-12307-3-53


Acknowledgement

My thanks to online storage service generously provided by Department of Computing and Software, McMaster

© Gary Guangning Tan, 2015