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June 14, 2025, 5:18 p.m.
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SOURCE: Simon H. Annual ADIA Lab Transactions in Data Science and Finance Vol 1. 2025
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COVER
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MEDIAINFO
Textbook in PDF format Transactions of ADIA Lab is a peer-reviewed publication that captures the pioneering research emerging from ADIA Lab's interdisciplinary efforts in computational and data science. This inaugural volume reflects the Lab's rapid evolution into a global research hub, showcasing significant contributions in computational finance, the digital economy, advanced computational methods, and trustworthy AI. The collection highlights groundbreaking advancements, including geometric approaches to asset allocation, AI-driven financial modeling, quantum-safe encryption, and ethical frameworks for AI. By bridging theory and practice, the publication underscores ADIA Lab's commitment to developing innovative, real-world solutions that address some of today's most pressing challenges in finance, technology, and beyond. With a strong emphasis on collaboration, Transactions of ADIA Lab brings together leading researchers, industry experts, and policymakers to explore the transformative potential of computational science. This volume not only presents state-of-the-art methodologies but also demonstrates ADIA Lab's dedication to fostering global partnerships, as seen in its collaborations with institutions in Spain and beyond. By integrating advanced computational techniques with practical applications, the research featured here paves the way for more secure financial systems, resilient digital infrastructures, and ethical AI frameworks. As ADIA Lab continues to grow, this publication serves as a testament to its vision of harnessing technology to drive meaningful and lasting impact across industries and society. Computational Finance: A Geometric Approach to Asset Allocation with Investor Views (Alexandre V Antonov, Koushik Balasubramanian, Alexander Lipton, and Marcos Lopez de Prado) Static Liquidation and Risk Management (Álvaro F Macías and Jorge P Zubelli) Overcoming Markowitz's Instability with the Help of the Hierarchical Risk Parity (HRP): Theoretical Evidence (Alexandre Antonov, Alexander Lipton, and Marcos Lopez de Prado) A Statistical Learning Approach to Local Volatility Calibration and Option Pricing (Vinicius V L Albani, Leonardo Sarmanho, and Jorge P Zubelli) Digital Economy: Challenges of Artificial Intelligence and Quantum Potential in the Digital Economy: A Literature Review (Laura Sanz Martín, Javier Parra Domínguez, Guillermo Rivas, Alexander Lipton, and Juan Manuel Corchado) Exploring the Digital Economy: Current Research Trends, Challenges, and Opportunities (Manuel J Cobo, Nadia Karina Gamboa-Rosales, José Ricardo López-Robles, and Enrique Herrera-Viedma) Interoperability Challenges in Tokenized Asset Networks (Thomas Hardjono, Alexander Lipton, and Alex Pentland) Advanced Computational Methods: Symmetric Encryption on a Quantum Computer (David Garvin, Oleksiy Kondratyev, Alexander Lipton, and Marco Paini) Performance-Driven Dimensionality Reduction: A Data-Centric Approach to Feature Engineering in Machine Learning (Joshua Chung, Marcos Lopez de Prado, Horst D Simon, and Kesheng Wu) Toward Specialized Supercomputers for Climate Sciences: Computational Requirements of the Icosahedral Nonhydrostatic Weather and Climate Model (Torsten Hoefler, Alexandru Calotoiu, Anurag Dipankar, Thomas Schulthess, Xavier Lapillonne, and Oliver Fuhrer) Dimension Walks on Generalized Spaces (Ana Paula Peron, Emilio Porcu) Trustworthy Artificial Intelligence: Trustworthy Artificial Intelligence: Nature, Requirements, Regulation, and Emerging Discussions (Francisco Herrera, Andres Herrera, Javier Del Ser, Enrique Herrera-Viedma, and Marcos López de Prado) Getting More for Less: Better A/B Testing via Causal Regularization (Kevin Webster and Nicholas Westray) Toward Automating Causal Discovery in Financial Markets and Beyond (Alik Sokolov, Fabrizzio Sabelli, Behzad Azadie Faraz, Wuding Li, and Luis Seco) Readership: Researchers, scientists, and professionals in computational and data science; as well as advanced academics and industry practitioners seeking applications of AI, HPC, and data science
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