CV
Work experience
- 2021 - present, data scientist at BASF
- 2019 - 2020, postdoctoral researcher & data scientist at Max-Planck Institute, Düsseldorf (MPIE)
- 2016 - 2019, PhD researcher at ICAMS, Ruhr-Universität Bochum. Thesis: “Atomistic modelling and simulations of magnetic transition metals”
Skills
- Machine learning
- Statistical learning
- Deep learning
- hypothesis testing, Design of experiments
- Programming
- Python
- Fortran
- R
- PostgreSQL
- C/C++
- Bash
- Linux
- Machematical modeling
- Numerical methods for partial differential equations
- Numerical analysis
- Simulations
- Molecular dynamics
- Thermodynamics
- Density-functional theory
- Monte Carlo sampling
- Statistical machanics
- Quantum mechanics
- High-performance computing
- Electronic circuit design
- Simulation with Spice
- PCB design and Assembly
- AutoCAD
Prizes
- Mathematical modeling
- 10/2012, Second prize in China post-graduate mathematical contest in modeling
- 02/2011, Honorable mention prize in mathematical contest in modeling of America
- 10/2010, First prize in China undergraduate mathematical contest in modeling
- 06/2011, First prize in mathematical contest in modeling of Northwestern Polytechnical University
- 06/2010, First prize in mathematical contest in modeling of Northwestern Polytechnical University
- Machine learning
- four medals in Kaggle competitions
Publications
Talks
The effect of spin fluctuations and atomic vibrations on the magnetic phase transition and the dynamical stability of iron at finite temperatures
ICAMS10 International Symposium at Ruhr-Universität Bochum, Bochum
Numerical simulation of spin fluctuations in materials science: Magnetic bond-order potentials and hybrid Monte Carlo
DPG Spring Meeting at Berlin, Germany
Numerical calculations of phase transitions in iron
ADIS workshop at Schloss Ringberg, Bavaria
Unsupervised image segmentation of Scanning Transmission Electron Microscopy (STEM) High-Angle Annular Dark Field(HAADF) images with atomic resolution
3rd German-Dutch Workshop on “Computational Materials Science” at Westhove Carsle, Domburg, Netherlands
Automatic semantic segmentation of Scanning Transmission Electron Microscopy (STEM) images using an unsupervised machine learning approach
BiGmax workshop at MPIE, Düsseldorf