Doctoral School of Informatics · Department of Data Science and Engineering
Eötvös Loránd University (ELTE) · Budapest, Hungary
My doctoral work sits at the intersection of generative AI, large language models, and intelligent data systems — building pipelines that automate complex workflows and enable adaptive, data-driven decision-making across digital ecosystems.
| Area | Topics |
|---|---|
| Generative AI & LLMs | Instruction tuning, domain-specific generation, LLM-powered assistants |
| Parameter-Efficient Fine-Tuning | LoRA, P-Tuning, Prefix Tuning on Llama-3.2 and related architectures |
| Bioinformatics | Sequence-based prediction of protein post-translational modification sites |
| AI-Driven Data Integration | Intelligent pipelines, adaptive decision systems, enterprise AI |
| Educational Data Science | Student performance modelling, data-driven pedagogy |
Peer-reviewed contributions across venues in machine learning, NLP, and computational biology:
NeurIPS · EMNLP · AAAI · IEEE Access · Neural Computing and Applications · Cognitive Computation · Computers in Biology and Medicine
Research spans LLM-powered banking assistants, generative AI systems, and protein post-translational modification prediction.
Core ML / DL
NLP & Generative AI
Engineering & Infrastructure
| Repository | Description | Stars |
|---|---|---|
| DSIQ-GEN | Auto-generation & classification of Data Science interview questions via LoRA, P-Tuning, Prefix Tuning on Llama-3.2 | — |
| SAP-AI-Skill-Framework | AI skill framework for SAP ecosystem integration | — |
| MeSEP | Prediction of Lysine Methylation sites using evolutionary & structural information | ★ 1 |
| KmethPred | Accurate Lysine Methylation site prediction from sequence features | — |
| Using-DM-To-Predict-Student-Performance | Data mining approach to predict secondary school student performance | ★ 1 |
| Repository | Description | Stars | Forks |
|---|---|---|---|
| IntroToDS | Introduction to Data Science Practice — comprehensive teaching resource | ★ 10 | 3 |
| Machine-Learning | Machine Learning code and experiments using Python | — | — |
| Recommender-Sys | Best practices and implementations of Recommendation Systems | ★ 1 | — |
| Python-Bachelor | Python coursework and exercises from undergraduate studies | — | — |
Open to research collaborations, visiting positions, and industry opportunities in AI, machine learning, and data science.
Let's connect and explore how we can shape the future of AI together.
Open to Work
20 Public Repositories
21 Private Repositories
I Mostly Code in Jupyter Notebook (Python)
Jupyter Notebook 8 repos █████████████████░░░░░░░░ 66.67%
Python 1 repo ██░░░░░░░░░░░░░░░░░░░░░░░ 8.33%
Elm 1 repo ██░░░░░░░░░░░░░░░░░░░░░░░ 8.33%
PHP 1 repo ██░░░░░░░░░░░░░░░░░░░░░░░ 8.33%
Roff 1 repo ██░░░░░░░░░░░░░░░░░░░░░░░ 8.33%