Taras Boiko
Java Software Developer at 2D WORKFLOW LLC
Lviv, Lviv Oblast, Ukraine
Summary
👤
Senior🎓
Top SchoolTaras Boiko is a Java Software Developer based in Lviv with over a decade of hands-on experience and a Master's in Computer Science from Lviv Polytechnic. He has delivered production-grade applications using Java/Spring Boot, React, SQL and NoSQL databases, Docker, and modern testing tools, and currently develops software at 2D WORKFLOW after prior work at AMC Bridge. Taras also contributes to prominent open-source projects such as Keras, where he improved RNN testing, activation functions, and pooling tests—showing an ability to bridge applied software engineering with machine learning tooling. Comfortable in Agile teams, he pairs clean, well-documented code with a background in 3D geometry algorithms, enabling him to tackle both backend services and mathematically inclined features. Colleagues describe him as a collaborative communicator who focuses on maintainability and practical solutions in fast-changing environments.
13 years of coding experience
2 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at Lviv Polytechnic National University
Stackoverflow
1,419reputation
131kreached
55answers
5questions
algorithm
top-5%
Github Skills (14)
neural-network10
keras10
machine-learning10
deep-learning10
tensorflow10
python10
pytest9
algorithm9
graph6
data-structures6
function-call6
greedy6
math6
dynamic-programming6
Programming languages (12)
Github contributions (5)
keras-team/keras
Sep 2016 - Dec 2016
Deep Learning for humans
Role in this project:
ML Engineer
ML EngineerContributions:8 commits, 12 PRs, 27 comments in 2 months
Contributions summary:Taras contributed to the Keras library by addressing several key areas within deep learning. They made changes related to recurrent neural networks, including splitting tests and ensuring compatibility. They also modified the ELU activation function implementation, and added functionality to return multiple metrics from a function. Further, the user improved the testing infrastructure by adding tests for AveragePooling2D and ensuring the output dimensions are checked correctly.
pythondata-sciencedeep-learningneural-networksmachine-learning
kilotaras/keras
Sep 2016 - Dec 2016
Contributions:35 pushes, 20 branches in 2 months
pythondeep-learningrecurrent-neural-networkstheanoneural-networks
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