{"id":9,"date":"2014-08-20T01:13:11","date_gmt":"2014-08-19T17:13:11","guid":{"rendered":"http:\/\/sims.im.ntust.edu.tw\/simslabweb\/?page_id=9"},"modified":"2025-12-18T00:04:52","modified_gmt":"2025-12-17T16:04:52","slug":"introduction","status":"publish","type":"page","link":"https:\/\/sims.im.ntust.edu.tw\/simslabweb\/en\/introduction\/","title":{"rendered":"Introduction"},"content":{"rendered":"

The System Informatics and Management Science Laboratory (SIMS)<\/strong> was established in 2011 within the Department of Industrial Management at National Taiwan University of Science and Technology (NTUST). The laboratory is directed by Dr. Chao-Lung Yang<\/strong>. The primary focus of the laboratory\u2019s research is on leveraging artificial intelligence models<\/strong> and data science<\/strong> methodologies to analyze system information and extract valuable knowledge or insights, which are then transformed into management decisions<\/strong> through operations research<\/strong> approaches.<\/p>\n

What Is System Informatics (SI)?<\/h3>\n

System Informatics is a multidisciplinary and integrative approach<\/strong> that aims to make information processing, analysis, and application within complex systems more streamlined and efficient<\/strong>. The term \u201ccomplex systems\u201d here is not limited to information systems alone; it encompasses systems in natural sciences, engineering, services, and management domains<\/strong>. To cope with the increasing volume, velocity, and real-time nature of information, System Informatics emphasizes the integration of information technologies and data analytics models<\/strong>, adopting a data-driven and practice-oriented approach<\/strong>.<\/p>\n

What Is Management Science (MS)?<\/h3>\n

Management Science is a discipline dedicated to applying scientific methods<\/strong> to support managerial decision-making and solve management-related problems. Management problems can be described as the gap between the current state<\/strong> and the desired state<\/strong> in a decision-making process, such as issues related to resource allocation<\/strong> or cost\u2013benefit optimization<\/strong>. These problems can be addressed by analyzing the current state and developing scientific methodologies\u2014such as mathematics, information science, and artificial intelligence<\/strong>\u2014to bridge the gap.<\/p>\n

Graduate Training and Core Activities<\/h3>\n

The laboratory cultivates fundamental research capabilities in graduate students through three core activities<\/strong>:<\/p>\n

    \n
  1. \n

    Data analysis skills<\/strong> (statistical analysis and multivariate analysis)<\/p>\n<\/li>\n

  2. \n

    Artificial intelligence model development skills<\/strong> (programming, computer vision data processing, and language model training)<\/p>\n<\/li>\n

  3. \n

    Management decision applications<\/strong> (case studies and optimization model development)<\/p>\n<\/li>\n<\/ol>\n

    Through these activities, the laboratory aims to train data scientists<\/strong> and AI practitioners<\/strong>, while also developing management analytics professionals<\/strong> tailored to specific industries and application domains.<\/p>\n

    Research Directions<\/h3>\n

    The laboratory\u2019s current research projects are organized into four major directions<\/strong>, starting from foundational data analysis and extending to deep learning\u2013based artificial intelligence applications combined with industry-specific data<\/strong> and open datasets<\/strong> to create innovative services:<\/p>\n