Biography

Dongseok Kim is a Computer Scientist working on Trustworthy AI. His main research areas are Explainable AI (XAI), Uncertainty Quantification, ML Fairness, AI Governance and AI Ethics. He is interested in methods for analyzing and evaluating the rationale behind a model's decisions, the uncertainty of its predictions, and the fairness of its outcomes, alongside predictive performance. Giving equal weight to statistical and mathematical rigor and to practical applicability, he aims to develop methods that can be applied across diverse datasets and models.

He has published 2 first-author papers in Transactions on Machine Learning Research (TMLR) and 1 in Applied Soft Computing. His recent work extends to AI for Advanced Manufacturing, including Anomaly Detection and Process Optimization. He is also a co-inventor on 7 registered and 3 published Korean patents in the areas of AI and Metaverse.

Alongside his research, he regularly takes part in the peer review process of the academic community. He has reviewed submissions for journals in IEEE Transactions and ACM Transactions series, and serves as a reviewer for ICML, NeurIPS, AISTATS, and FAccT leading international conferences in ML. Through reviewing across theory and applications, and across journals and conferences, he contributes to the scholarly process of verifying the validity and reproducibility of research.