Position: Data Scientist
Category: Python
English level: upper
Years of experience: 1

2010 – May 2021: Institute of Physics of NAS of Belarus, researcher
Responsibility: 
•	Optical end electronic experimental research of semiconductor structures
•	epitaxial growth on nitride heterostructures
•	Molecular beam epitaxy setup service
•	Software development for experimental setups
•	Analysis of scientific literature and writing scientific articles
Achievements:
•	developing software module for temperature calculation for Raman microscope
•	top 10 of NAS of Belarus achievements in 2016
•	more than 10 scientific articles in pre-reviewed journals
•	first observation of stimulated emission in CIGSSe films
Implementing from scratch (in the vectorized way, using just python/numpy):
•	Softmax classifier, Multi-class SVM,
•	Multilayer perceptron, CNN, with ReLU activations, dropout, batch/layer/group normalization layers with forward and backward computation path;
•	RNN, LSTM nets with tanh and sigmoid activations with forward and backward computation path
•	some first-order gradient-based optimization algorithms like SGD (+momentum, Nesterov momentum), RMS-prop, Adam. 

  Taking derivatives for all mentioned above neural net layers to do backpropagation
	
  Applying these algorithms to image classification (train on CIFAR-10 dataset) and image captioning problems (COCO). 

Implementing with PyTorch:
•	CNN with module API and sequential API and training them on CIFAR-10 dataset; 
•	multi-head attention transformer architecture (with module API) and combing it with DENSENET feature extractor for image captioning;
•	generative-advertising networks (LS, DC GAN) and training them to generate hand-written style numbers images that match the MNIST dataset; 
•	self-supervised SimCLR-like net, train it on unlabeled CIFAR-10 and then finetune it on the small amount of labeled data;
•	style transfer model with pretrained squeeze-net as a feature extractor. 
•	image gradient-based generator of fooling images, saliency maps, and class visualization images to neural nets.