Amir Sani is an experienced AI and automation strategist with 11 years blending academic rigor and product-focused deployment across private markets, finance, and complex systems. He holds a PhD in Machine Learning and has translated advanced agent-based modeling, surrogate ML, and calibration techniques from research at institutions like Oxford, Imperial and Scuola Superiore Sant'Anna into production analytics and fund intelligence at Techstars, 13books Capital and EY. Today he advises VCs, family offices and private companies while leading AI & Automation at I2ntelligence and R&D at AdapData, specializing in scalable data pipelines, surrogate-based sampling, and human-in-the-loop model design. Notably, his background spans both high-frequency market microstructure optimization and large-scale scraping/ETL architectures, enabling pragmatic bridges between cutting-edge research and commercial value creation.
11 years of coding experience
4 years of employment as a software developer
Australian National University
University College London
Certificate Law, Certificate Law at University of San Diego School of Law
Clarkson School Coursework, Clarkson School Coursework at Clarkson University
University of California, San Diego
Doctor of Philosophy (Ph.D.) Machine Learning, Doctor of Philosophy (Ph.D.) Machine Learning at INRIA & University of Lille 1
A moment-free estimator of the Sharpe (signal-to-noise) ratio.
Contributions:2 releases, 35 commits, 1 PR in 2 years 10 months
signalaudio-processingsignal-to-noisetradingratio
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