Alessandro Romano

Alessandro Romano

Senior Data Scientist

About

Alessandro Romano is a Senior Data Scientist at Kuehne + Nagel and an accomplished public speaker. With over 7 years of experience in data analysis, he brings deep technical expertise in implementing large language model (LLM) - based solutions across diverse industries.

A technical and personal talk about training a LoRA adapter on years of Telegram conversations to see whether an LLM can learn a person's writing style, humor, recurring expressions, and group chat dynamics.

The talk would cover the full pipeline: exporting Telegram data, cleaning and structuring messy chat logs, preparing instruction-style datasets, choosing a base model, applying LoRA/QLoRA fine-tuning, running the training process, and comparing the fine-tuned model against the original base model.

Beyond the technical implementation, the talk also explores evaluation: how do you know if the model really sounds like you? I would show qualitative examples, failure modes, overfitting risks, privacy concerns, and the strange experience of interacting with an AI system trained on your own digital footprint.

The goal is to make LoRA fine-tuning concrete, practical, and memorable through a real personal use case rather than a generic benchmark.

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In this hands-on workshop, you will build an Agentic AI application from scratch and take it all the way to production. Starting from a simple LLM-based workflow, you will progressively introduce tool usage, ReAct-style reasoning, and LangGraph orchestration to create a system capable of making decisions, retrieving information, and taking actions.

You will connect the application to external data sources using RAG, structure and control its behavior through context engineering, and extend its capabilities with MCP and multi-agent patterns. Finally, you will package everything into a Streamlit interface and deploy it to a real cloud service.

By the end of the workshop, you will have a fully working AI knowledge assistant and a clear understanding of how to design and ship production-ready agentic systems in Python.

*Note: These are tentative details and are subject to change. 

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