About Me
I am Carlo Nicolini, a senior AI research scientist working at the intersection of artificial intelligence, statistical physics, computational neuroscience, and information theory. For over 15 years, my work has focused on problems that require both theoretical clarity and reliable execution: building models, abstractions, and scientific software that help researchers and engineers reason about complex systems with more rigor. Over time, this has evolved into a form of technical leadership grounded in framing hard problems well, creating reusable foundations, and helping collaborative work move with more clarity.
A recurring theme in my work is taking a fragmented technical space, identifying the right abstractions, and turning them into tools, APIs, and explanations that make an entire line of work more usable.
Scientifically I am interested in modern artificial intelligence from the point of view of a statistical physicist (I started with the great McKay book). I have been working on LLM systems, vector symbolic architectures, mechanistic interpretability, AI reliability, probabilistic language programming, and the design of compound systems that make inference-time behavior more auditable, controllable, and semantically grounded.
In parallel, I have developed quantitative finance models, implemented in the libraries scikit-portfolio first, then becoming a maintainer of skfolio, a large collaborative project to improve the scientific foundations of portfolio allocation with modern optimization methods.
In my recent writing, I explore how branching, verification, and decomposition shape the reliability of AI systems, how inference-time scaffolds can be understood as structured probabilistic procedures, and how better interfaces between theory and engineering can make advanced systems more legible and robust.
My research portfolio extends to online convex optimization, information theory and large scale machine learning models. Across these projects, I tend to work at the boundary between research, engineering, and real-world constraints: framing the problem clearly, choosing the right level of abstraction, aligning technical choices with practical goals, and turning ideas into systems that are mathematically grounded, computationally efficient, and genuinely useful in practice.
My contributions
I have been working across research, engineering, and open-source software, with publications spanning modern AI, machine learning for finance, and complex systems. My work appears at venues such as COLM, ICAIF, TMLR, EPJ Data Science, Physical Review E, and NeuroImage, and is collected on Google Scholar.
- Senior AI Research Scientist, Ipazia SpA (2022–present)
- Maintainer of skfolio (portfolio optimization in Python)
- Research: reliable AI systems, interpretability, compound AI, NLP, optimization, complex systems
- Focus: connecting deep theory, reusable software, and collaborative research execution
- Strengths: technical direction, cross-functional collaboration, and building research infrastructure others can rely on
Selected publications
Recent work on LLM interpretability, NLP, and statistical physics. Full list →
- Sokobench: Evaluating Long-Horizon Planning and Reasoning in Large Language Models
- Unveiling LLMs: The evolution of latent representations in a dynamic knowledge graph
- Hopfield Networks for Asset Allocation
- Glitter or gold? Deriving structured insights from sustainability reports via large language models
Latest blog posts
I maintain a research blog where I develop ideas in public, document ongoing work, and refine the conceptual foundations behind the systems and tools I build.
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Agentic Soft Logical Circuits: from ReAct Chains to Structured Variational Inference
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The continuous Sheffer stroke is all you need
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Interpretations for the Kullback-Leibler divergence, or relative entropy
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From hard to soft operators: between machine learning and statistical physics
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Random rooted trees, continuation free energy, and the Diligent Learner
Contact
If you are working on reliable AI systems, mechanistic interpretability, inference-time methods, or machine learning for finance, feel free to write me at c.nicolini@ipazia.com. I am also available on LinkedIn for professional contact.
Connect on LinkedIn.