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Keith Lee

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Keith Lee is Professor of AI and Finance at the Gordon School of Business and Head of Research at the Swiss Institute of Artificial Intelligence (SIAI). His research focuses on financial mathematics, AI-driven computational science, and quantitative modeling of complex economic and financial systems, integrating machine learning, stochastic modeling, and data-centric methods.

His recent work examines the socioeconomic consequences of artificial intelligence, including labor markets, public finance, demographic change, institutional adaptation, and the distributional effects of technological progress.

He also serves as Executive Editor, Research & Review at The Economy, overseeing research-oriented editorial coverage, analytical contributions, and engagement with academic and industry specialists.

He holds a PhD in Mathematical Finance from Boston University, an MSc in Finance and Economics from the London School of Economics, and a bachelor's degree in Economics from Seoul National University, where he studied under the Korea Foundation for Advanced Studies scholarship program.

Keith Lee

Web novel to Webtoon conversion is not only based on 'profitability'If the novel author is endowed with money or bargaining power, 'Webtoonization' may be nothing more than a marketting tool for the web novel.

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Keith Lee

Not the quality of teaching, but the way it operatesEasier admission and graduation bar applied to online degrees

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Keith Lee

Asian companies convert degrees into years of work experienceWithout adding extra values to AI degree, it doesn't help much in salary

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Keith Lee

The relationship between a commercial district and the concentration of consumers in a specific generation mostly is not by causal effectSimultaneity oftern requires instrumental variables

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Keith Lee

One-variable analysis can lead to big errors, so you must always understand complex relationships between various variables. Data science is a model research project that finds complex relationships between various variables. Obsessing with one variable is a past way of thinking, and you need to improve your way of thinking in line with the era of big data. When providing data science speeches, when employees come in with wrong conclusions, or when I give external lectures, the point I always emphasize is not to do 'one-variable regression.'

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Keith Lee

With high variance, 0/1 hardly yields a decent model, let alone with new set of dataWhat is known as 'interpretable' AI is no more than basic statistics

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