Modern & classical machine learning

Especially: Non-parametric methods and unsupervised learning.

Computer vision & graphics

Especially: Geometric capturing and photo-realistic modeling as well as animation of human characters.

Various related topics

E.g. causal inference, geometric deep learning, recurrent neural networks, statistical signal processing...

Experienced with data analysis, mining and exploitation

E.g. dimensionality-reduction, visualization, augmentation, dev/test/train set organization...

Practical in-depth knowledge in modeling and solving diverse kinds of optimization problems

E.g. linear/non-linear, continuous/discrete, dense/sparse, with/without constraints, regularization...

Proficient with various learning-based principles and methods

E.g. statistical learning, classical (Bayesian) machine learning, deep learning, reinforcement learning, supervised/unsupervised/semi-supervised learning...

Advanced modern C++

Using diverse state-of-the-art libraries, e.g. Ceres, Eigen, Intel MKL, OpenGL, Qt, Boost...

Fluent with various scripting languages, in particular Python + NumPy + PyTorch

E.g. R, Octave + Symbolic, Bash...