Stanislav Pidhorskyi
Graduate Research Assistant
Morgantown, West Virginia, United States
Summary
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Rockstar🎓
Top SchoolStanislav Pidhorskyi is a PhD student and Graduate Research Assistant at West Virginia University with 11 years of experience combining production software development and academic research in computer vision and machine learning. His current research targets generative models and novelty/anomaly detection, and he has hands-on ML engineering experience contributing to the CVPR2020 ALAE project—tweaking VAE architectures, losses, and integrating style-based layers and discriminators. He interned with AWS Rekognition and previously spent several years in gamedev at Gameloft as a senior C++ developer, shipping graphics/gameplay features and implementing Thai text support using harfbuzz. That mix of low-level systems and graphics work with cutting-edge generative ML lets him move ideas from prototype to production.
11 years of coding experience
6 years of employment as a software developer
PhD, Computer Science, PhD, Computer Science at West Virginia UniversityMaster's Degree, Dynamics and Strength of Machines, Master's Degree, Dynamics and Strength of Machines at National Aerospace University -'Kharkiv Aviation Institute'
Stackoverflow
1,572reputation
184kreached
32answers
4questions
opengl
top-5%
Github Skills (15)
pytorch10
machine-learning10
image-generation10
autoencoder10
opengl9
alpha-blending6
opengl-es6
projection6
rotation6
matrix6
shader6
glsl6
3d6
framebuffer6
tensorflow3
Programming languages (8)
Github contributions (5)
podgorskiy/ALAE
Mar 2019 - Dec 2020
[CVPR2020] Adversarial Latent Autoencoders
Role in this project:
ML Engineer
ML EngineerContributions:292 commits, 5 PRs, 21 pushes in 1 year 9 months
Contributions summary:Stanislav's commits primarily focus on modifications to the VAE (Variational Autoencoder) and related files, particularly `VAE.py` and `net.py`, suggesting a focus on implementing and refining the autoencoder architecture. The changes include modifications to the loss function, the introduction of style-based layers, and integration with a discriminator. This indicates the user is actively involved in improving and experimenting with the autoencoder, possibly for image generation or related tasks.
pytorchautoencoderunsupervised-learningdeep-learningadversarial
podgorskiy/PicoRender
Aug 2015 - Oct 2020
Contributions:18 pushes, 4 branches, 4 tags in 5 years 3 months
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