At Iteralabs we believe in one core principle: To achieve consequential engineering results, science goes before hype.

“Most people use statistics like a drunk man uses a lamppost; more for support than illumination” ― Andrew Lang

And thus, we focus on statistical soundness and parametric stability for the models we use, with this hierarchical sourcing of knowledge: statistical learning > machine > large heuristics learning (Generative AI, which we could use, even daily, but as an optional tool not as a protagonistically, for-its-own-sake goal).

• Classical ML OnChain Computation.
• DeFi Market Making, Order Routing and Risk Modeling.
• Synthetic Data Generation (OffChain, and, OnChain).

• Classical ML and Quantitative Finance.
• Distributed Convex Optimization Models.
• Financial timeseries inner-pattern recognition (subsequential clustering).

• atelier-rs : Rust Engine for High Frequency, Synthetic and Historical, Market Microstructure Modeling.
• luciene-sl : Transparent and Stateless Agent for OnChain Risk Modeling.
• supermass-rs : Subsequential Timeseries Clustering for anomaly detection in Timeseries data.