{"id":1676,"date":"2022-03-02T15:00:56","date_gmt":"2022-03-02T20:00:56","guid":{"rendered":"https:\/\/www.yorku.ca\/science\/profiles\/?post_type=faculty&#038;p=1676"},"modified":"2025-08-21T09:54:46","modified_gmt":"2025-08-21T13:54:46","slug":"hyejin-ku","status":"publish","type":"faculty","link":"https:\/\/www.yorku.ca\/science\/profiles\/faculty\/hyejin-ku\/","title":{"rendered":"Hyejin Ku"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Short <strong>Bio<\/strong><\/h2>\n\n\n\n<p>Hyejin Ku is a Full Professor in the Department of Mathematics and Statistics at 快播视频. Her research spans mathematical finance, machine learning, and reinforcement learning, with a focus on risk-sensitive decision-making. She has supervised multiple PhD and Master\u2019s students, with publications in leading venues such as ICML and\u00a0<em>Expert Systems with Applications<\/em>. Her work bridges theory and practice, contributing to the development of reliable learning methods under uncertainty across domains including finance and operations.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Research Interests<\/h2>\n\n\n\n<p><strong><em>Risk-Sensitive Reinforcement Learning, Mathematical Finance, and Machine Learning<\/em><\/strong><\/p>\n\n\n\n<p>My research lies at the intersection of risk measurement, decision-making under uncertainty, and machine learning. I focus on developing principled, mathematically grounded models for risk-sensitive reinforcement learning, optimal policy learning, portfolio optimization, and credit risk assessment. These efforts aim to advance both theoretical understanding and&nbsp;practical methodologies for sequential decision-making under risk.<\/p>\n\n\n\n<p>In a recent project, we proposed a novel Distributional Reinforcement Learning (DRL) algorithm that optimizes a broad class of static spectral risk measures. The method offers theoretical convergence guarantees and interpretable policies through coherent risk decomposition. Empirical results show improved performance over existing risk-neutral and risk-sensitive models on benchmark environments.<\/p>\n\n\n\n<p>Previous work includes ensemble learning models for credit rating prediction using real-world financial data and sequence-based clustering of firms for long-term credit risk assessment. These projects integrate AI techniques with domain-specific modeling to enable robust, data-driven decision-making in high-stakes financial contexts.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Sub-Disciplines<\/h2>\n\n\n\n<p>Mathematical Finance, Reinforcement Learning<\/p>\n","protected":false},"template":"","department":[5],"research-area":[87],"un-goal":[],"research-method":[],"graduate-program-membership":[],"graduate-program-physics":[],"admin-faculty-title-department":[],"program":[],"class_list":["post-1676","faculty","type-faculty","status-publish","hentry","department-math-stats","research-area-appmath"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Hyejin Ku - Faculty of Science Profiles<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.yorku.ca\/science\/profiles\/faculty\/hyejin-ku\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Hyejin Ku - Faculty of Science Profiles\" \/>\n<meta property=\"og:description\" content=\"Short Bio Hyejin Ku is a Full Professor in the Department of Mathematics and Statistics at 快播视频. 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