Transactions of ADIA Lab
This inaugural volume of Transactions of ADIA Lab showcases a curated selection of peer-reviewed studies spanning the lab’s interdisciplinary focus on data science, computational finance, AI, and emerging digital infrastructures. It presents a comprehensive snapshot of the lab’s evolution into a global research hub and underlines its commitment to applying advanced computational techniques to real-world challenges.
Key Themes and Contributions
1. Computational Finance
Several papers rethink foundational finance concepts using cutting-edge mathematics and machine learning:
A geometric framework for asset allocation integrates investor views with robust optimisation.
An analysis of static liquidation strategies explores risk in constrained portfolios.
Theoretical justification is provided for Hierarchical Risk Parity (HRP) as a solution to Markowitz portfolio instability.
A machine learning approach to local volatility calibration improves derivative pricing techniques.
2. Digital Economy
Contributors address the interplay between technological evolution and digital economic structures:
A systematic review explores how AI and quantum technologies challenge regulatory, ethical, and technical norms.
Another paper maps research trends in the digital economy, offering a taxonomy of current challenges and future directions.
Technical barriers to tokenised asset interoperability are dissected, with insights into governance and standardisation in decentralised finance.
3. Advanced Computational Methods
Work in this section pushes the boundaries of performance and complexity:
New methods for quantum-safe symmetric encryption are evaluated in the context of future computational threats.
A data-centric approach to feature engineering uses performance-based dimensionality reduction to enhance model robustness.
Computational demands of advanced climate models are profiled to inform specialised supercomputer design.
Exploratory mathematical work on dimension walks contributes to the theoretical landscape of geometry and probability.
4. Trustworthy AI
This section connects technical development with societal trust and ethical oversight:
A position paper outlines core requirements for Trustworthy AI, grounded in regulation and public values.
A novel method for causally regularised A/B testing improves efficiency in online experimentation.
A proposal for automated causal discovery applies AI to uncover structural dependencies in financial data.
Strategic Context
Through its emphasis on applied, interdisciplinary research, ADIA Lab demonstrates a deep engagement with both academic rigour and practical impact. The volume reflects:
Strategic partnerships with academic institutions (e.g., in Spain and beyond)
A commitment to ethical, secure, and high-performance systems across domains
An ambition to shape global discourse on technology governance
This collection is both a scholarly contribution and a forward-looking signal of ADIA Lab’s intent to help steer the future of finance, AI, and scientific computing.