Aleksandr Razin

Hey! 👋

I’m Aleksandr Razin, an MSc graduate in Machine Learning from ITMO University. My research sits at the intersection of image restoration, generative computer vision, and multimodal learning. I’m broadly interested in controllable, personalized generative systems that combine structural understanding with creative synthesis under low latency, and I enjoy turning theoretical advances into practical, reliable tools.

Along the way, I’ve worked on pipeline-inspection robotics (patented) and advanced industrial computer vision at Huawei, where I focused on efficient image restoration—super-resolution, denoising, and demosaicing for both images and video—as well as diffusion models for image generation. My MSc thesis studied spatial conditioning in generative models, with a depth-conditioned approach to super-resolution that reduced artifacts and improved fidelity in low-level vision tasks.

Currently, I’m turning image restoration methods into generative modeling to improve personalization and efficiency. I’m seeking a PhD supervisor/professor and open to challenging, impactful research opportunities.

You can find my CV here. I’m always open to collaboration, feel free to reach out by email!

Selected Publications

arXiv

One Small Step in Latent, One Giant Leap for Pixels: Fast Latent Upscale Adapter for Your Diffusion Models

Aleksandr Razin, Danil Kazantsev, and Ilya Makarov

2025

arXiv preprint arXiv:2511.10629

arXiv Bib

@misc{razin2025smallsteplatentgiant,
  title = {One Small Step in Latent, One Giant Leap for Pixels: Fast Latent Upscale Adapter for Your Diffusion Models},
  author = {Razin, Aleksandr and Kazantsev, Danil and Makarov, Ilya},
  year = {2025},
  eprint = {2511.10629},
  archiveprefix = {arXiv},
  primaryclass = {cs.CV},
  url = {https://arxiv.org/abs/2511.10629},
  note = {arXiv preprint arXiv:2511.10629},
}

JSTITMO

Improving question answering in programming domain with pretrained language model finetuning using structured diverse online forum data

Alexey V. Gorbatovski, Alexandr D. Razin, Artem A. Aliev, and 1 more author

Journal Scientific and Technical Of Information Technologies, Mechanics and Optics, 2024

Bib HTML

@article{gorbatovski2024improving,
  title = {Improving question answering in programming domain with pretrained language model finetuning using structured diverse online forum data},
  author = {Gorbatovski, Alexey V. and Razin, Alexandr D. and Aliev, Artem A. and Kovalchuk, Sergey V.},
  journal = {Journal Scientific and Technical Of Information Technologies, Mechanics and Optics},
  volume = {158},
  number = {6},
  pages = {1024},
  year = {2024},
}

Infrastructures

Robot crawler for surveying pipelines and metal structures of complex spatial configuration

Vladimir Pshenin, Anastasia Liagova, Alexander Razin, and 2 more authors

Infrastructures, 2022

Bib HTML

@article{pshenin2022robot,
  title = {Robot crawler for surveying pipelines and metal structures of complex spatial configuration},
  author = {Pshenin, Vladimir and Liagova, Anastasia and Razin, Alexander and Skorobogatov, Alexander and Komarovsky, Maxim},
  journal = {Infrastructures},
  volume = {7},
  number = {6},
  pages = {75},
  year = {2022},
  publisher = {MDPI},
}

RU Patent

Program for processing data for the presence of corrosion damage and its detection using a convolutional neural network

G.S. Zakirova, V.V. Pshenin, and A.D. Razin

2022

Application number: 2022615229

Bib

@misc{patent2022corrosion,
  title = {Program for processing data for the presence of corrosion damage and its detection using a convolutional neural network},
  author = {Zakirova, G.S. and Pshenin, V.V. and Razin, A.D.},
  year = {2022},
  howpublished = {Russian Patent},
  number = {2022616881},
  note = {Application number: 2022615229},
}

RU Patent

Program for object detection on raw data using a pre-trained neural network model for classification

E.R. Pshenin, V.V. Pshenin, A.D. Razin, and 1 more author

2022

Application number: 2022661008

Bib

@misc{patent2022object,
  title = {Program for object detection on raw data using a pre-trained neural network model for classification},
  author = {Pshenin, E.R. and Pshenin, V.V. and Razin, A.D. and Dzhemilev},
  year = {2022},
  howpublished = {Russian Patent},
  number = {2022662358},
  note = {Application number: 2022661008},
}

RU Patent

Database of typical structural elements of gas pipelines, collected during testing of a robotic device and processed by a convolutional neural network

E.R. Dzhemilev, V.V. Pshenin, and A.D. Razin

2022

Application number: 2022621389

Bib

@misc{patent2022database,
  title = {Database of typical structural elements of gas pipelines, collected during testing of a robotic device and processed by a convolutional neural network},
  author = {Dzhemilev, E.R. and Pshenin, V.V. and Razin, A.D.},
  year = {2022},
  howpublished = {Russian Patent},
  number = {2022621490},
  note = {Application number: 2022621389},
}