Demetris Paschalides

Lead ML & NLP Engineer at Laboratory for Internet Computing

Nicosia, Cyprus
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Summary

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Senior
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Top School
Demetris Paschalides is a Lead ML & NLP Engineer with 11 years of experience and over nine years focused on end-to-end NLP pipelines, transformer model development, LLM fine-tuning, and production ML systems using PyTorch, HuggingFace, Kafka, Docker, and AWS. He combines research rigor from a PhD in Computer Science with hands-on engineering, deploying LLaMA/Mistral/Qwen-based systems, INT8-optimized ONNX inference, and serverless architectures for real-time streaming applications. His work spans social media, misinformation detection, cybersecurity, energy, and labor markets, often enriching models with GNNs and knowledge graphs via PyTorch Geometric and RAG. A collaborative R&D lead, he has coordinated projects with 50+ partners, supervised multiple theses, and reviewed for top conferences, translating academic insight into operational ML products. Based in Nicosia, Cyprus, he’s equally comfortable tuning models for cloud and on-device inference (TensorFlow.js), reflecting a practical bent for moving novel research into production.
code11 years of coding experience
job9 years of employment as a software developer
bookDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Cyprus
bookLyceum Deploma, Computer Science, Lyceum Deploma, Computer Science at Palouriotissa Lyceum
languagesGreek, English
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Github Skills (68)

coreference10
coreference-resolution10
nlp8
huggingface8
natural-language-understanding8
laravel7
python7
eloquent7
natural-language-processing7
mongodb7
machine-learning7
backend6
deep-learning6
react6
spacy6

Programming languages (6)

JavaCSSCPHPJupyter NotebookPython

Github contributions (5)

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dpasch01/spdx-compat-tools

Jun 2015 - Sep 2016

Contributions:10 commits, 9 pushes, 1 branch in 1 year 3 months
dpasch01/polarlib

Sep 2021 - Dec 2024

Code repository for POLAR, a framework for the modelling of polarization and identification of polarizing topics in news articles. POLAR utilizes a variety of computational method to model polarization, including NLP, Graphs, and Clustering. It produces hierarchical knowledge of polarization for a given dataset of news articles.
Contributions:2 PRs, 42 pushes, 3 branches in 3 years 3 months
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Demetris Paschalides - Lead ML & NLP Engineer at Laboratory for Internet Computing