About me

I am a 4th year PhD student at Wrocław University of Science and Technology. My research is focused on unsupervised representation learning methods for attributed graphs.

  • Graph Representation Learning
  • Unsupervised Learning
  • Attributed Graphs
  • PhD in Computer Science (Machine Learning), 2019-ongoing

    Wrocław University of Science and Technology

  • MSc in Computer Science (Data Science specialization), 2018-2019

    Wrocław University of Science and Technology

  • BEng in Computer Science, 2014-2018

    Wrocław University of Science and Technology


Retrofitting structural graph embeddings with node attribute information
Retrofitting structural graph embeddings with node attribute information

Representation learning for graphs has attracted increasing attention in recent years. In this paper, we define and study a new problem of learning attributed graph embeddings. Our setting considers how to update existing node representations from structural graph embedding methods when some additional node attributes are given. To this end, we propose Graph Embedding RetroFitting (GERF), a method that delivers a compound node embedding that follows both the graph structure and attribute space similarity. Unlike other attributed graph embedding methods, GERF is a novel representation learning method that does not require recalculation of the embedding from scratch but rather uses existing ones and retrofits the embedding according to neighborhoods defined by the graph structure and the node attributes space. Moreover, our approach keeps the same embedding space all the time and allows comparing the positions of embedding vectors and quantifying the impact of attributes on the representation update. Our GERF method updates embedding vectors by optimizing the invariance loss, graph neighbor loss, and attribute the neighbor loss to obtain high-quality embeddings. Experiments on WikiCS, Amazon-CS, Amazon-Photo, and Coauthor-CS datasets demonstrate that our proposed algorithm receives similar results compared to other state-of-the-art attributed graph embedding models despite working in retrofitting manner.



ML Ops Developer
Sep 2022 – Present Wrocław
Senior Machine Learning Developer
Sep 2020 – Dec 2022 Wrocław
Working on machine learning based recommendation systems for company interactions
Research Assistant
Jan 2019 – Present Wrocław

Conducting research in the area of network science and network embedding algorithms.

01.2019 - 06.2019 - Scholar in a project of the National Science Centre Poland (Narodowe Centrum Nauki).

06.2019 - 12.2019 - project on financial transactions

06.2019 - 08.2020 - project on analysis of online user behavior

03.2020 - now - project on edge representation learning in graphs

Junior DevOps
Jan 2018 – Dec 2018 Wrocław

Member of the Public Cloud team. Development and maintenance of OpenStack. Preparation of the automation platform for Openstack deployments using Jenkins.

13.11.2018 - Speaker at Openstack Summit Berlin 2018: “From messy XML to wonderful YAML and pretty Job DSL - an in-Jenkins migration story”

Python, Bash, Ansible, Jenkins

Software Developer Intern
Jul 2017 – Sep 2017 Gdańsk

Research & development of ML based resource manager for modern cluster schedulers

Python, Tensorflow, Keras, Reinforcement Learning

Junior Java & Javascript Developer
Nov 2016 – Mar 2017 Wrocław

Working on web-application as frontend and backend developer.

JavaScript (AngularJS), Java 8 (Spring), MongoDB

QA Test Automation Engineer
Feb 2016 – Oct 2016 Wrocław

Test Automation Team:

  • review of existing test scripts
  • reporting new defects
  • script adaptation after defect fixing
  • writing new test scripts