Noah Farhadi is professor of practice and CIB endowed chair at The American University in Cairo. He brings more than 25 years of combined academic, corporate and advisory experience spanning finance, strategy, business analytics, mergers and acquisitions and international management.

Before joining academia, Farhadi held senior positions at Steelcase, Roland Berger and EY. As EMEA strategy director at Steelcase, a US-based global manufacturer, he was responsible for strategy and the development of an international ecosystem of distribution partners across more than 60 countries in Europe, the Middle East and Africa. Earlier in his career, he advised multinational corporations, including Fortune Global 500 automotive manufacturers, on cross-border mergers and acquisitions, divestitures, carve-outs, corporate finance and post-merger integration across 36 countries.

Farhadi holds a PhD in business administration with a concentration in business analytics from the University of South Alabama, a Doctor of Business Administration in corporate finance from Henley Business School, University of Reading, an MBA from Frankfurt School of Finance and Management and a Diplom-Informatiker degree from Frankfurt University of Applied Sciences.

His research lies at the intersection of finance, business analytics and artificial intelligence. His current work focuses on machine learning in finance, sustainable finance and ESG analytics, portfolio theory and optimization, financial prediction, risk and return dynamics and advanced multivariate path modeling. A major stream of his methodological research integrates partial least squares modeling with machine learning methods to develop explanatory and predictive approaches for complex, high-dimensional financial and business data.

His empirical research examines the relationship between environmental performance, carbon emissions, ESG information, corporate operating performance and capital markets. He also studies the evolution of financial assets across changing risk and return states and develops computational approaches to financial prediction and portfolio construction. His work draws on large-scale financial and panel datasets and combines machine learning, structural equation modeling, predictive analytics, multivariate statistics and computational finance.

Farhadi publishes in peer-reviewed academic journals and collaborates internationally on research in sustainable finance, machine learning, ESG, quantitative finance and advanced analytical methods. At AUC, he teaches Machine Learning in Finance, Portfolio Theory, Corporate Finance and Sustainable Finance, connecting contemporary analytical methods with financial theory and practical decision-making.