I am Michał Bariła, a junior data scientist with a Master’s degree in Engineering and Data Analysis from Lublin University of Technology. I gave a lecture on Variational Autoencoders at MAIÓWKA, a scientific festival about artificial intelligence, which took place at Maria Curie-Skłodowska University. My notable project is about AI models called Autoencoders, with a focus on image compression. I also developed a data generation project using GAN models and performed segmentation using UNET models, as well as anomaly detection with VAE and AE. During my internship I developed a Retrieval-Augmented Generation (RAG) system using LlamaIndex to chat with 1,300 medium articles based on LLMs. I developed and trained a retrieval model with contrastive learning to identify and extract relevant text chunks from a ChromaDB vector database. I am familiar with the approaches presented in Style-Based Generator Architecture for Generative Adversarial Networks and Attention Is All You Need, among other important papers. Additionally, I worked as a Data Science Freelancer analyzing Czech language event descriptions.

Michał Bariła

I am Michał Bariła, a junior data scientist with a Master’s degree in Engineering and Data Analysis from Lublin University of Technology. I gave a lecture on Variational Autoencoders at MAIÓWKA, a scientific festival about artificial intelligence, which took place at Maria Curie-Skłodowska University. My notable project is about AI models called Autoencoders, with a focus on image compression. I also developed a data generation project using GAN models and performed segmentation using UNET models, as well as anomaly detection with VAE and AE. During my internship I developed a Retrieval-Augmented Generation (RAG) system using LlamaIndex to chat with 1,300 medium articles based on LLMs. I developed and trained a retrieval model with contrastive learning to identify and extract relevant text chunks from a ChromaDB vector database. I am familiar with the approaches presented in Style-Based Generator Architecture for Generative Adversarial Networks and Attention Is All You Need, among other important papers. Additionally, I worked as a Data Science Freelancer analyzing Czech language event descriptions.

Available to hire

I am Michał Bariła, a junior data scientist with a Master’s degree in Engineering and Data Analysis from Lublin University of Technology. I gave a lecture on Variational Autoencoders at MAIÓWKA, a scientific festival about artificial intelligence, which took place at Maria Curie-Skłodowska University. My notable project is about AI models called Autoencoders, with a focus on image compression. I also developed a data generation project using GAN models and performed segmentation using UNET models, as well as anomaly detection with VAE and AE.

During my internship I developed a Retrieval-Augmented Generation (RAG) system using LlamaIndex to chat with 1,300 medium articles based on LLMs. I developed and trained a retrieval model with contrastive learning to identify and extract relevant text chunks from a ChromaDB vector database. I am familiar with the approaches presented in Style-Based Generator Architecture for Generative Adversarial Networks and Attention Is All You Need, among other important papers. Additionally, I worked as a Data Science Freelancer analyzing Czech language event descriptions.

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Experience Level

Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
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Language

Polish
Fluent
English
Advanced
Czech
Intermediate

Work Experience

AI Engeenier Internship at Ideo
September 16, 2024 - February 16, 2025
During this time I was devoloping chatbot system and recommendation system for web store
Data Science Freelance at Frelance
January 16, 2025 - February 16, 2026
Language analysis of Czech event description. That analysis involved use of classic ML methods for keyword mining and AI for classifying events based on Wikipedia articles.

Education

Bachelor in Computer Science at Lublin University of Technology
January 11, 2030 - January 16, 2026

Qualifications

Add your qualifications or awards here.

Industry Experience

Software & Internet, Education, Professional Services

Experience Level

Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
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