Daniil Pakhomov
Research Scientist at Adobe
San Jose, California, United States
Summary
👤
Senior🎓
Top SchoolDaniil Pakhomov is a research scientist and PhD candidate in Computer Science with 12 years of experience applying deep learning to computer vision problems such as image classification, segmentation, face detection and recognition. Based in San Jose and currently at Adobe, he bridges applied research and engineering—shipping prototype-ready PyTorch implementations for segmentation and detection while contributing tested implementations to scikit-image. His work spans from dataset engineering and video-generation tooling to Cython-optimized image descriptors, reflecting both systems-level care and algorithmic depth. Trained at Johns Hopkins and Technical University of Munich, he combines rigorous academic research with hands-on production contributions and an eye for reproducible, well-documented code. A less obvious strength is his history of improving core image-processing primitives (e.g., MB-LBP) and documentation, showing a commitment to foundational tooling as well as novel models.
12 years of coding experience
8 years of employment as a software developer
Bachelor’s Degree, Computer Science, 3.7 GPA, Bachelor’s Degree, Computer Science, 3.7 GPA at Saint Petersburg State UniversityJohns Hopkins UniversityMaster’s Degree, Computer Vision. Data Science., 4.0 GPA, Master’s Degree, Computer Vision. Data Science., 4.0 GPA at Technical University Munich
English, German, Russian
Github Skills (17)
pytorch10
convolutional-neural-networks10
python10
image-processing10
machine-learning10
scikit-image10
data-loading10
image-segmentation10
deep-learning10
segmentation10
resnet10
neural-network10
computer-vision10
test-automation10
numpy9
Programming languages (9)
Github contributions (5)
warmspringwinds/pytorch-segmentation-detection
Jun 2017 - Mar 2022
Image Segmentation and Object Detection in Pytorch
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:262 commits, 3 PRs, 221 pushes in 4 years 10 months
Contributions summary:Daniil primarily contributed to the development of a Pytorch-based project focused on image segmentation and object detection. Their contributions included implementing and modifying several image segmentation models, particularly focusing on variations of ResNet and U-Net architectures, and integrating them for image segmentation tasks. The user also developed data loaders for the Pascal VOC and Endovis datasets, and implemented video generation scripts. Their contributions demonstrate a focus on deep learning for computer vision tasks.
pytorchdeep-learningimage-segmentationobject-detectioncomputer-vision
scikit-image/scikit-image
May 2015 - Jun 2016
Image processing in Python
Role in this project:Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:73 commits, 5 PRs, 120 comments in 1 year 1 month
Contributions summary:Daniil's commits primarily focus on improving and extending image processing functionalities. They corrected documentation for the `ellipse` function, making it more formal and addressing code style violations. The user implemented a plain Python multi-block local binary pattern (MB-LBP) with test coverage. Further improvements involved Cython implementation of MB-LBP, including visualization and gallery examples.
image-processingpythoncomputer-visionimage
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