Category: Data Science Education

Integrating Data for AI : All You Ever Wanted To Know About Metadata Standards, Ontologies, Data Models, and Process Models But are Too Afraid to Ask

August 17, 2026

In the last 10 years or so, there has been a paradigm shift in how we train AI models. In particular, the best practice has shifted from Model-Centric AI, where we endless tweak model architectures and algorithms in pursuit of better performance, to what we now call Data-Centric AI, where we keep the model architecture … More Integrating Data for AI : All You Ever Wanted To Know About Metadata Standards, Ontologies, Data Models, and Process Models But are Too Afraid to Ask

What Makes a Good Question in the Age of AI

August 9, 2026

In a recent TED talk titled One Thing to Teach in the Age of AI, innovation practitioner Bryan Cassady shared a story about an exam created by one of his colleagues. The exam question was deceptively simple: “Prove you’ve learned the material in this course by giving me five good questions.” Many students struggled with that, and certainly … More What Makes a Good Question in the Age of AI

JEC Special Issue on Economic Crime in a Changing World

July 14, 2026

A significantly expanded version of a paper I shared earlier on Privacy Technologies for Financial Intelligence is now published in the Journal of Economic Criminology’s Special Issue on Economic Crime in a Changing World: Emerging Threats and Strategic Responses. The paper is open-access and written for a broad non-technical audience. It can be found here: … More JEC Special Issue on Economic Crime in a Changing World

Notes on Conformal Prediction and Testing

February 8, 2026

All my life I have been searching for simple and effective methods for constructing prediction intervals for different AI/ML models. I don’t know why I never encountered Conformal Prediction until recently, but I suppose it is better late than never. Conformal prediction is (arguably) the most elegant and practical technique for improving the robustness in … More Notes on Conformal Prediction and Testing

AI Governance vs AI Assurance

January 6, 2026

AI governance and AI assurance are sometimes conflated in conversations but they are not the same thing. In simple terms, In more details, the AI Governance Framework for an organisation is the strategic blueprint that defines the organisation’s overarching approach to developing, deploying, and managing AI systems. Its purpose is to define the roles, responsibilities, … More AI Governance vs AI Assurance

On the Semantics of Differential Privacy and Its Responsible Use

September 15, 2025

Differential Privacy (DP) is one of the most widely adopted formal model of privacy protection but its semantics, especially in the presence of correlated data and in the adversarial interactive setting, is still not broadly understood among data science practitioners. In this paper, we first look at how DP originated from research on database-reconstruction attacks … More On the Semantics of Differential Privacy and Its Responsible Use

Update on Social Cost of Multi-Agent Reinforcement Learning Paper

April 24, 2025

I recently released on arXiv a new version of the paper The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis, which can be found at https://arxiv.org/abs/2412.02091 The new version has These are all non-trivial extensions of the paper that build on recent new results in different fields and they are worth … More Update on Social Cost of Multi-Agent Reinforcement Learning Paper

Algebraic Intuitions behind Fourier Transforms

February 7, 2025

There’s been a lot written about Fourier Transforms over the years. From a physics perspective, I would recommend the Harmonics chapter in the Feynman Lectures on Physics. I also found Elan Ness-Cohn’s visual explanation of Fourier Transforms really satisfying. In the notes linked below, I have tried to give, in 5 short pages, the intuition … More Algebraic Intuitions behind Fourier Transforms

A Simplistic Guide to Using Fairness Criteria in Machine Learning

January 10, 2025

Fairness in Machine Learning is a topic that I have been wanting to better understand for a little while now, and this blog post summarises what I learned from reading the Fairness and Machine Learning (FML) book by Solon Barocas, Moritz Hardt and Arvind Narayanan available at https://fairmlbook.org these past couple of days. (The book … More A Simplistic Guide to Using Fairness Criteria in Machine Learning