معرفی
Alejandro Kuratomi Hernandez is an Associate Senior Lecturer (ranked as Senior Lecturer) at Stockholm University's Department of Computer and Systems Sciences, part of the Faculty of Social Sciences. His research focuses on Applied Machine Learning, Interpretability, and Fairness in AI. He holds a M.Sc. in Mechatronics from KTH Royal Institute of Technology and dual B.Sc. degrees in Mechanical Engineering and Industrial Engineering from Universidad de Los Andes. He has supervised multiple master’s theses on topics like counterfactual explanations, interpretable algorithms, and fairness measurement. His work bridges academic research with industrial applications, often collaborating with companies to develop AI solutions. He is affiliated with the Data Science Research Group, which emphasizes core data science methodologies and their practical decision-making applications. Recent publications address challenges in positioning error prediction, justified counterfactual explanations, and fairness metrics using counterfactual analysis.
Teaching includes roles as a teaching assistant for Machine Learning, Programming for Data Science, and AI Principles courses. His advising spans projects in XAI (eXplainable AI), medical image analysis, and interpretable neural networks. He actively contributes to the development of algorithms that enhance AI transparency and ethical compliance.
