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Lev Novitskiy

Machine learning engineer / Data Scientist

Fields of interest: Diffusion Distillation, Diffusion, Mulimodal models, Flow Matching, GANs


πŸ“Š ML stack

python pytorch pytorch-geometric albumentations huggingface diffusers wandb catbost catbost pandas spark plotly seaborn

πŸ”§ Other skills

bash git

πŸ‘¨β€πŸ’» Work experience

  • DL Researcher at SBER AI (April 2024 - Present day):

    • Kandinsky 5 Pro - best T2V open source model on LLM Arena
    • Kandinsky 5 Diffusers integration
    • Kandinsky 5 Pro and Lite Distillation (x6 Speedup with 45% Human Preference Score)
    • Novel step caching strategy for Diffusion models (NDA) with 2.0–3.5x Speedup
    • Kandinsky 5 RLHF Pipeline, Open-source project on Diffusion Reward Modeling for Text Rendering
    • Kandinsky 4.1 Released, 3 stage Distillaiton Pipeline (x8 Speedup with 51% Human Preference Score)
    • WAN 2.1, Hunyuan Video Distillation
    • 1000+ GPU training (PyTorch), large pipeline for diffusion pre-training with Tensor Parallel + FSDP
    • Stable pipeline for training 40b GAN model. Proposed custom GAN Parallelism (instead of TP) for training Large Diffusion GAN Models
    • Created Kandinsky 4.0 T2V Flash (x25 speedup with 45% Human Preference Score)
    • CogVideoX Diffusion GAN Finetuning (59% Human Preference Score)
    • Image Auto Encoder for Kandinsky (Proposed SOTA VIVAT VAE)
    • Dragon Diffusion editing with Kandinsky 3
  • DL Researcher at AIRI (April 2023 - May 2024)
    As a result of the research we propose several new models for crystal structure generation and modification code

    • New materials design with VAEs (VAEs)
    • Formation Energy regression with neural networks (GNNs, PointNet, CNNs, Transformers)
    • Crystall structure generation, optimization (Diffusion, Flow Matching, Bridge Matching, Auto-Encoders, CNNs, Transformers, En-Transformers)
  • DL Engeneer at SBER Cyber Security (July 2022 - April 2024):

    • NLP: fraud call analisys
    • Classic ML: fraud detection with gradient boosting
    • Transaction Graph Neural Networks (GCN, GAT, GraphSAGE, Graphormer, GIN)
    • Transaction Graph Neural Network pretraining (self-supervised: ARGA, ARGVA, GAEs, VGAEs, Contrastive Learning)
    • Transaction Graph Neural Network (Temporal GNNs)
    • Transaction sequence scoring (LSTM, GRU, Transformer Encoder)
    • User scoring (Gradient Boosting, Ensembling)
    • Vulnerability detection in assembly with neural networks (Angr, GNNs)
    • Antifraud Voice Bot for preventinng call fraud (BERT, NSP, Pretraining, SFT, GPT, FAISS, HNSW, Retrieval, Whisper, ConFormer)

πŸŽ“ Education

Cources

πŸ† Competition Background

πŸŽ‰ Other Achievements

πŸ‘¨β€πŸ« Other activities

🐢 Projects

Languages

πŸ‡·πŸ‡Ί Russian - Native
πŸ‡¬πŸ‡§ English - C1
πŸ‡¨πŸ‡³ Chinese - A2

Hobbies

  • πŸ„β€β™‚οΈ Surfing
  • πŸŠβ€β™‚οΈ Swimming
  • πŸ‚ Snowboarding

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