Cognitive Systems and Decision Intelligence (CSDI) Lab
Research Field
PI’s Introduction
The Principal Investigator of the Cognitive Systems and Decision Intelligence (CSDI) Lab focuses on time series analytics, decision science, and data-driven modeling for industrial and enterprise systems. Core research areas include anomaly detection, forecasting, optimization, and decision intelligence, grounded in statistics, machine learning, and other domains related to industrial engineering.
His recent work extends to applied AI topics such as computer vision and industrial robotics, with an emphasis on process monitoring, human–machine collaboration, and smart manufacturing in Industry 4.0 contexts. The lab actively publishes in international venues and conducts interdisciplinary research that bridges theory and real-world applications. Students from AI, robotics, statistics, data science, industrial engineering, operations research, and related fields are encouraged to engage in application-driven, interdisciplinary research.
Lab's Introduction
Welcome to the Cognitive Systems and Decision Intelligence Lab (CSDI Lab) at NTUT! Our lab is dedicated to advancing research at the intersection of computer science, artificial intelligence, statistics, and industrial engineering. We focus on building data-driven and AI-enabled systems that enhance decision-making, process monitoring, and optimization in industrial and enterprise contexts. With strong foundations in time series analytics, anomaly detection, operations research, and machine learning, we aim to bridge fundamental methods with real-world applications in Industry 4.0, manufacturing, logistics, and intelligent automation.
Our projects cover a broad spectrum under the CSDI umbrella, with strong emphasis on:
- Artificial Intelligence & Machine Learning – applying AI methods in industrial robotics, process monitoring, and decision support.
- Statistics & Data Science – developing models for forecasting, anomaly detection, and pattern recognition in complex datasets.
- Industrial Engineering & Operations Research – building optimization and decision-making frameworks for production systems, supply chains, and logistics.
At the Cognitive Systems and Decision Intelligence Lab (CSDI Lab), we explore a wide range of interdisciplinary topics at the intersection of AI, data science, industrial engineering, and management science.
- Human–Machine Collaboration & Robotics - Exploring how robots and AI systems can work alongside humans to perform repetitive or complex tasks, combining insights from industrial automation, human–computer interaction, and data-driven process design.
- Industrial AI & Machine Learning Applications - Applying advanced AI and machine learning models to challenges in manufacturing, logistics, and enterprise systems, with links to predictive analytics, optimization, and cyber-physical systems.
- Time Series Analytics & Anomaly Detection - Developing statistical and AI-based methods to detect patterns and anomalies in sensor data, financial series, and operational processes, supporting applications in process monitoring, forecasting, and risk management.
- Optimization & Operations Research - Building mathematical and computational models to improve production planning, supply chains, and resource allocation, while also connecting to algorithm design, decision science, and statistical learning.
- Data Mining & Statistical Modeling - Leveraging statistical, computational, and AI techniques for forecasting, classification, clustering, and knowledge discovery, with applications ranging from business analytics to intelligent systems design.
- Decision Intelligence & Management Science - Integrating AI, data analytics, and optimization with management science principles to support strategic and operational decision-making in both industrial and enterprise contexts.
Grants
- NSTC, Research Grant (2023-2026), Title: “Optimization-based Time Series Anomalous Subsequence Detection and Its Application in Abnormal Action Recognition under Cloud-Edge Computing Systems”.
- MOE, Taiwan Education Experience Program (TEEP) - 2024, 2025
Guest Lectures
- Pattern Mining in Time Series and Applications in Intelligent Manufacturing, Universitas Diponegoro, Indonesia, 2025
- Working as a foreigner professional in Taiwan: Opportunities and Challenges, Universitas Negeri Malang, Indonesia, 2024
- The Good AI, Yayasan Pendidikan Nasional Karang Turi, Indonesia, 2022
- Financial management in the era of financial technology for Indonesian migrant workers in Taiwan, Indonesian Economic and Trade Office to Taipei (IETO), Taipei, Taiwan, 2019
- Short-term traffic prediction, weather-sensitive road analysis, and dynamic intermediate stops for last-mile deliveries: A case study in Jakarta, New York City Department of City Planning (DCP), New York City, U.S., 2019
Ph.D. in Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan
Job Description
Undergraduate Research Intern
The primary objective of this role is to support the ongoing research work within the lab. You will assist the research team in executing experiments, managing data, and supporting the technical development of active projects.
We are looking for students interested in a long-term research collaboration. Preference is given to those planning to apply for graduate school at Taipei Tech, with the potential to join our research team as a full-time graduate student and continue their academic development with us.
Based on your interests and capabilities, you will be involved in our ongoing research, focusing on one or more of the following:
- Programming Support: Writing and debugging code for AI, optimization, or robotic systems.
- Data Management: Assisting with data collection, cleaning, and preprocessing.
- Statistical Support: Helping with basic classification, clustering, and forecasting tasks.
- System Testing: Supporting the practical testing of decision intelligence tools.
Application Requirements (send by email to hendri@mail.ntut.edu.tw):
- Curriculum Vitae (CV) including a GitHub repository or other portfolio for reference .
- Intended Starting Date (clearly stated in the email).
Preferred Intern Educational Level
Undergraduate Year 3 or above.
Skill sets or Qualities
- Programming Skills: Proficiency in Python, R, or related languages used in data science and engineering.
- Related Skills: Basic understanding of machine learning, statistical software, or optimization logic.
- Detail-Oriented: Precision in handling data and code to ensure research integrity.