cv
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Last updated: August 14, 2026
Basics
| Name | Tommaso Mencattini |
| Label | MSc Data Science Student & Researcher |
| tommaso.mencattini@epfl.ch | |
| Summary | MSc student in Data Science at EPFL with research experience at ISTA, ETH Zürich, and GLADIA. My work focuses on large language models, interpretability, causal inference, and AI safety. First-author papers at ICLR, ICML, and ACL, plus publications at NeurIPS workshops and ECAI. Co-PI of a Coefficient Giving-funded research team on activation steering at Rome AI Safety. |
Work
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2026.01 - Present Rome, Italy
Co-Principal Investigator
Rome AI Safety (RAIS)
Proposer and co-PI, together with Donato Crisostomi, of a 7-person research team funded by Coefficient Giving with $80k.
- Developing disentangled, composable steering vectors for fine-grained behavioral control via white-box interventions in activation space.
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2025.07 - Present Vienna, Austria
Research Intern
Institute of Science and Technology Austria (ISTA)
Research on causality and multimodal foundation models within the Locatello Group (PI: Francesco Locatello).
- Co-first author of an ICLR 2026 Oral (top 1.2%): a framework (theory + library) for unsupervised causal effect discovery in RCTs.
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2024.04 - Present Rome, Italy
Research Student
GLADIA · Sapienza University of Rome
Research on scalable model merging, interpretability, and ability estimation for large language models (PI: Emanuele Rodolà).
- Scaled evolutionary model merging of LLMs to consumer GPUs and developed ability-estimation theory via Item Response Theory: 50× faster, 2% of the data, 85% performance retained. Two first-author papers at ICML 2025 and ACL 2025.
- Co-first author of an ICLR 2026 paper proving injectivity of LLM hidden representations and presenting the first exact inversion algorithm (5M views on the tweeprint).
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2023.06 - 2023.08 Zurich, Switzerland
Research Student
ETH Zürich (LRE Lab)
Applied causal inference to explain and assess LLM performance on math word problems (PI: Mrinmaya Sachan).
- Worked on applying causal inference techniques to large language models to detect spurious heuristics in reasoning tasks.
- Ran large-scale experiments on high-performance computing clusters.
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2023.01 - 2023.06 Amsterdam, Netherlands
Research Assistant
Vrije Universiteit Amsterdam · KAI Lab
Mitigated hallucinations in LLMs using knowledge graphs for content planning (PI: Ilaria Tiddi).
- Designed Text-to-Graph and Graph-to-Text methods for content planning.
- Trained and evaluated multiple knowledge-graph-integrated LLMs.
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2022.07 - 2023.04 Rome, Italy
Research Student
Sapienza University of Rome
NLP research in e-justice applications using specialized large language models.
- Developed GPT-2-based writing assistant for legal documents.
- Publication at ECAI 2023 (CICERO project).
- Conducted evaluation with legal professionals.
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2022.05 - 2022.12 Amsterdam, Netherlands
Technology Assistant
Network Institute VU Amsterdam
Technical support for interdisciplinary projects in XR, VR, and motion capture.
- Worked with Unity, C#, iClone, Motive, and Optitrack.
- Organized and presented at Surf XR-On Tour and Reshaping Work 2022 Conference.
Education
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2024.09 - Present Lausanne, Switzerland
Master of Science
École Polytechnique Fédérale de Lausanne (EPFL)
Data Science
- Coursework focused on the Mathematics of data science
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2024.06 - 2024.08 Remote
Summer School (3 months)
BlueDot Impact
AI Safety
- Studied sparse autoencoder diffing and transfer; work accepted at the UniReps Workshop @ NeurIPS 2024 (blog post track).
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2021.09 - 2024.07 Amsterdam, Netherlands
Bachelor of Science
Vrije Universiteit Amsterdam
Artificial Intelligence (Major) & Mathematics (Minor)
- Honours Programme (30 credits of advanced coursework, e.g. PDEs)
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2018.09 - 2021.09 Rome, Italy
Bachelor’s Degree
Sapienza University of Rome
Philosophy
- Focused on formal logic and philosophy of language
Awards
- 2022.01.01
KHMW Young Talent Incentive Award
Royal Holland Society of Sciences and Humanities
National award presented to the student with the highest GPA in their degree program at a Dutch research university.
- 2021.01.01
Intelligent Systems Competition — 1st Place
Vrije Universiteit Amsterdam
Ranked 1st out of 270 students by developing an intelligent agent for the card game Schnapsen.
Interests
| AI Safety | ||||
| Interpretability | ||||
| Sparse Autoencoders | ||||
| Causal Models | ||||
| Theoretical ML | ||||
| Representations | ||||
| Optimization | ||||
| Information Theory | ||||