Assessing Risk of Extreme Events & Knowledge Extraction for RAG Systems

Details
📅 Date: 27-11-2025
📍 Location: BCG @Via Ugo Foscolo, 1, 20121 Milano MI

Agenda
18:30 - Doors Open
18:50 - Welcome to PyData
19:00 - Talk 1: Extreme Hail Risk Estimation for Utility-scale Solar Parks
Speaker: Valerio Bonometti, Senior Data Scientist @Lightsource bp
19:45 - Talk 2: Autocurator: AI-Powered Knowledge Extraction for RAG Systems
Speakers: Eleonora Vardè, Lead Data Scientist @ BCG X & Marco P. Abrate, BCG X and UCL
20:30 - Networking
21:00 - Join us for dinner after the event too to continue chatting!

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Talks and Speakers

Extreme Hail Risk Estimation for Utility-scale Solar Parks
Extreme hail events pose a serious risk not just for people but also for business and assets. Due to their nature this is particularly true for utility-scale solar parks.

Accurately assessing the risk of extreme hail is therefore critical when planning, developing, and operating large-scale solar farms. Yet, the challenge is compounded by limited, noisy historical data and the unpredictable nature of severe weather events.

In this talk, I will show how a range of statistical and simulation methods can be used to estimate hail risk—even in situations with limited or noisy data.

Valerio Bonometti is a Senior Data Scientist currently working in the renewable energy sector at Lightsource bp. Before that I was a Data Scientist at Tesco where I worked both in the Forecasting and in the Search and Recommendation teams.

I hold an industrial PhD in computer science from the University of York carried out full-time within a major Japanese video-game publisher (Square Enix) in London. My thesis focused on how to blend neuroscientific theories of motivation with deep learning model for estimating motivational states and predicting behavioral engagement.

Before that I was a research intern between Ghent and Padua where I focused on the study of the reward process both from a behavioral and psychobiological perspective.

I hold a master and bachelor degree in psychology from the Univeristy of Padua.

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Autocurator: AI-Powered Knowledge Extraction for RAG Systems
In the era of information overload, organizations struggle to harness the vast amount of unstructured data stored across presentations, reports, images, and text documents. That's why we created the "Autocurator", an AI-powered tool designed to automatically extract, structure, and curate knowledge from heterogeneous document repositories to support Retrieval-Augmented Generation (RAG) systems.

Autocurator integrates advanced document parsing pipelines, multimodal AI models, and semantic structuring techniques to convert diverse content - including text, slides, tables, and diagrams - into machine-readable knowledge. This enables downstream RAG systems to query not only text-based insights but also visual and conceptual knowledge that traditionally remained inaccessible.
Our system employs a multi-stage pipeline: (1) document ingestion and format normalization, (2) de-duplication of redundant and conflicting information (3) multimodal content understanding using large language and vision models, (4) entity and relationship extraction with human-in-the-loop validation, and (5) generation of structured outputs optimized for retrieval.
We will showcase Autocurator’s effectiveness on large enterprise document corpora, showcasing significant gains in retrieval precision and generative quality across several applied AI use cases.
By bridging unstructured data and structured knowledge, Autocurator provides a scalable and transparent foundation for next-generation knowledge management and reasoning systems.

Eleonora Vardè is a Lead data scientist @ BCG X Milan, specialized in Customer Service and Gen AI topics

Marco P. Abrate is a Visiting AI engineer at BCG X and a PhD candidate in Neuroscience and AI at University College London.

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