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Keith Lee

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Keith Lee is Professor of AI and Finance at the Gordon School of Business, Swiss Institute of Artificial Intelligence (SIAI). His primary research lies in financial mathematics and AI-driven computational science, with a focus on quantitative modeling of complex economic and financial systems. His work integrates machine learning, stochastic modeling, and data-centric methods to study structural transformations in markets and institutions.

In recent years, his research has extended to the economic and fiscal implications of technological change, including the interaction between artificial intelligence, demographic shifts, and public finance sustainability.

He holds a PhD in Mathematical Finance from Boston University, and previously earned an MSc in Finance and Economics from the London School of Economics. He completed his undergraduate studies in Economics at Seoul National University under the Korea Foundation for Advanced Studies scholarship program.

He regularly contributes analytical essays on the broader socioeconomic implications of AI to The Economy Review.

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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