
Δημοσθένης Ελμάς
@0007479
AI/ML engineer developing LLM and RAG tools in C++ and adapting Transformer and diffusion models for wind-farm forecasting.
What I'm looking for
At CERTH-ITI, I developed web and mobile applications and built APIs for database transactions and user authentication. I also co-authored two peer-reviewed papers on social-robot-based platforms for diet tracking and childhood-obesity prevention.
For my MSc thesis at Aristotle University of Thessaloniki, I adapted PyTorch implementations of the Informer, Temporal Fusion Transformer, and CSDI diffusion model to forecast wind-farm power output. I modified attention and training procedures and designed a multi-stage training scheme.
In my free time, I’m building a local RAG assistant in C++ with a FAISS index, ONNX Runtime embeddings, and a quantized LLM served through llama.cpp. It’s designed to run without a dedicated GPU and includes multi-turn conversation memory.
I’m also experimenting with a Unity-based tri-dexel machining simulation and reinforcement-learning environment with G-code support. Alongside that project, I’m collaborating with clinical doctors on statistical shape modeling of 3D medical volumes for a research publication.
Experience
Work history, roles, and key accomplishments
Research Collaborator
Clinical Research Team
Collaborating on statistical shape modeling and homologous meshing of 3D medical volumes for a research publication.
Research Assistant
Information Technologies Institute (CERTH-ITI)
Feb 2018 - Aug 2020 (2 years 6 months)
Developed web and mobile applications for research projects using Angular, JavaScript/TypeScript, and Apache Cordova. Built web APIs handling database transactions and user authentication, and co-authored two peer-reviewed papers.
Education
Degrees, certifications, and relevant coursework
Aristotle University of Thessaloniki
MSc, Artificial Intelligence
Grade: 9.21/10
MSc in Artificial Intelligence with grade 9.21/10. Thesis on forecasting renewable energy generation using deep learning.
University of Macedonia
BSc, Applied Informatics
Grade: 8.26/10
BSc in Applied Informatics with grade 8.26/10, top 6.9% of graduates.
Tech stack
Software and tools used professionally
Availability
Location
Authorized to work in
Salary expectations
Social media
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