Tl;dr
I am Head of Research at Tinkoff AI Center, working on various machine learning applications. I focus on the intersection of machine learning and product development to create novel solutions to improve customer experience.
I am actively contributing to open source projects and leading the development of Catalyst, the open-source PyTorch framework for Deep Learning R&D.
In my spare time, I enjoy writing posts or give talks on ML-related topics.
Interests
- Sequential Decision Making
- Process organisation
- Psychology
Education
MSc in Math and Computer Science
Moscow Institute of Physics and Technology
BSc in Math and Computer Science
Moscow Institute of Physics and Technology
Catalyst
Catalyst is a PyTorch framework for Deep Learning Research and Development.
It focuses on reproducibility, rapid experimentation, and codebase reuse so you can create something new rather than write yet another train loop.
Break the cycle - use the Catalyst!
Course
Week 1: Deep learning intro
Deep learning – introduction, backpropagation algorithm. Optimization methods. Neural Network in numpy.
Week 2: Deep learning frameworks
Regularization methods and deep learning frameworks. Pytorch basics & extras.
Week 3: Convolutional Neural Networks
CNN. Model Zoo. Convolutional kernels. ResNet. Simple Noise Attack.
Week 4: Object Detection, Image Segmentation
Object Detection. (One, Two)-Stage methods. Anchors. Image Segmentation. Up-scaling. FCN, U-net, FPN. DeepMask.
Week 5: Metric Learning
Metric Learning. Contrastive and Triplet Loss. Samplers. Cross Entropy Loss modifications. SphereFace, CosFace, ArcFace.
Week 6: Autoencoders
AutoEncoders. Denoise, Sparse, Variational. Generative Models. Autoregressive models.
Week 7: Generative Adversarial Models
Generative Adversarial Networks. VAE-GAN. AAE. Energy based model.
Week 8: Natural Language Processing
Embeddings. RNN. LSTM, GRU.
Week 9: Attention and transformer model
Attention Mechanism. Transformer Model.
Week 10: Transfer Learning in NLP
Pretrained Transformers. BERT. GPT. Data Augmentation in Texts. Domain Adaptation.
Week 11: Recommender Systems
Collaborative Filtering. FunkSVD. Neural Collaborative Filtering.
Week 12: Reinforcement Learning for RecSys
Reinforcement Learning. DQN Algorithm. DDPG Algorithm. Wolpertinger.
Week 13: Extras
Research & Deploy. Config API. Reaction.
Recent Posts
Как мы в SIGIR-соревновании участвовали
Catalyst.Neuro: A 3D Brain Segmentation Pipeline for MRI
Catalyst — A PyTorch Framework for Accelerated Deep Learning R&D
Representation Learning and Faces
![RL in RecSys, an overview [EN]](/post/2101-recsys-rl-en/featured_huc1b81a3dbdee40429bf3706801ce1a16_168912_150x0_resize_lanczos_2.png)
Recent & Upcoming Talks
Projects
4th place solution for the SIGIR 2021 challenge.
4th place solution for the SIGIR 2021 challenge.
ETNA
ETNA - Time Series Library
Kittylyst
A tiny Catalyst-like experiment runner framework on top of micrograd.
Deep Learning with Catalyst
Deep Learning with Catalyst.
ML workflow
Checklist and questions to make ML right.
Catalyst codestyle
Accelerated Python code formatter
RL Intro
Brief introduction to Reinforcement Learning and Recommender Systems.
Alchemy
Experiments logging & visualization
NeurIPS RL 2019 challenge
2nd place solution for NeurIPS RL 2019 challenge.
Reaction
Convenient deep learning models serving
Catalyst detection
Object detection with Catalyst
Catalyst segmentation
Image segmentation with Catalyst
Catalyst classification
Image classification with Catalyst
Catalyst.RL
Catalyst.RL: A Distributed Framework for Reproducible RL Research
Catalyst
Accelerated deep learning research and development
NeurIPS RL 2018 challenge
3rd place solution for NeurIPS RL 2018 challenge.
OpenAI Retro Contest
4th place solution for OpenAI Retro Contest.
NIPS RL 2017 challenge
3rd place solution for NIPS RL 2017 challenge.