Vitaliy Kinakh

InfoSCC-GAN

Ph.D. research project, main author • Aug, 2021 - Dec, 2021

Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN. Paper accepted to NIPS 2021 BDL Workshop

SC-GAN

Ph.D. research project, co-author • Aug, 2021 - Dec, 2021

Generation of data on discontinuous manifolds via continuous stochastic non-invertible networks. Paper accepted to NIPS 2021 BDL Workshop

Turbo-Sim

Ph.D. research project, co-author • Aug, 2021 - Dec, 2021

Turbo-Sim: a generalised generative model with a physical latent space. Paper accepted to NIPS 2021 Machine Learning and the Physical Sciences Workshop

ScatSimCLR

Ph.D. research project, main author • Mar, 2021 - Aug, 2021

Self-supervised contrastive learning method with pretext task regularization for small-scale datasets. 🏆 SOTA on CIFAR20 Unsupervised Image Classification. Paper accepted to ICCV 2021 2nd Visual Inductive Priors for Data-Efficient Deep Learning Workshop

University of Geneva

Ph.D. student, Computer Science • 2021 — current

I work on SNF Sinergia RODEM project in Stochastic Information Processing group. We are bringing deep learning to solar and particle physics.

Lviv Polytechnic National University

Master, Mathematical and Computer Modelling • 2020 — 2021

Laval University

Research Intern, Computer Science • 2019

MITACS Globalink Internship. I was working on project Spatial Clustering and Possibility Areas for Maritime Search and Rescue Operations Optimization

Lviv Polytechnic National University

Bachelor, Applied Mathematics • 2016 — 2020

I was working on projects related to analysis of spatial greenhouse gas emissions and nightlight light data in particular on mitigating geolocation errors in nighttime light satellite data and global CO2 emission gridded data.

ABTO Software

Research & Development Engineer • Jan, 2018 — Feb 2021

I was using Computer Vision, Machine Learning and Deep Learning to solve real-worl problems in various industries:

• retail
• security
• automatic document processing
• agriculture

International Institute for Applied Systems Analysis

GeoData Annotation Specialist (Remote) • Sep, 2017 — Feb, 2019

ABTO Software

Computer Vision Intern • Jun, 2017 — Aug, 2017

Computer Vision Internship. During the internship, I have created two projects:

• Currency recognition app - application that recognizes currency
• Computer vision calculator - app that performs arithmetical operations from the photo or video

Programming

Python; C++

Machine Learning/Deep Learning/Computer Vision

Scikit-Learn; Keras; Pytorch; OpenCV; DLib; Tesseract

Geoinformation systems

QGIS; Google Earth Engine

Languages

Ukrainian (Native); English (Advanced); Russian (Advanced); French (Beginner); Polish (Beginner); German (Beginner)

Other

Web libraries: Flask; Operational systems: Linux, Windows; Git, SVN, Docker

Air Quality Monitor

NASA Space Apps Challenge • 2019

Application for predicting air quality index (AQI) based on satellite data and on-ground observations using machine learning. This project was local round winner in Lviv, Ukraine.

Make Cities Green Again

NASA Space Apps Challenge • 2018

Application for analyzing raster images from satellite, detecting trees and green zones in cities, calculating the percentage of green zones in cities using computer vision techniques. This project was local round winner in Lviv, Ukraine.