Embedded Files
Professor
Professor
안흥조 (Heungjo An)
안흥조 (Heungjo An)
Kumoh National Institute of Technology
Office: G627
Tel: +82-54-478-7666
Email: Heungjo.An@kumoh.ac.kr
Education
TEXAS A&M UNIVERSITY, COLLEGE STATION, TX, 2006 - 2011, Ph.D., Industrial and Systems Engineering
HANYANG UNIVERSITY, SEOUL, SOUTH KOREA, 1997 - 1999, MS, Material Science & Engineering
HANYANG UNIVERSITY, SEOUL, SOUTH KOREA, 1993 - 1997, BS, Material Science & Engineering
Professional Experiences
2025 – Present: Associate Professor, Industrial Engineering, Kumoh National Institute of Technology
2022 – 2025: Assistant Professor, Industrial Engineering, Kumoh National Institute of Technology
2015 – 2022: Assistant Professor, Industrial and Systems Engineering, Khalifa University of Science and Technology, UAE
(2018 June-Aug: Visiting Scientist, KAIST, Korea)
2013 – 2015: Assistant Professor, Systems Engineering, KFUPM, Saudi Arabia (QS ranking 160)
2011 – 2013: Operations Research Analyst, LG Chem Research Park, Korea
2001 – 2006: IT System Engineer, Daewoo Information System Co, Ltd., Korea.
1999 – 2001: Researcher, Dooray Air Metal, Korea
CV
Link to Google Scholar
https://scholar.google.co.kr/citations?user=s8Z2vvYAAAAJ&hl=en
Lab Members
Lab Members
Dr. Umar Zaman
Dr. Umar Zaman
(Post-Doc)
(Post-Doc)
Ph.D.: Artificial Intelligence, Chungnam National University, Daejeon, South Korea
MS: Computer Science, Iqra University, Islamabad Campus, Pakistan.
BS: Computer Science (Gold Medalist), University of Malakand, Pakistan.
Research Interests: AI Vision Inspection Under Data Scarcity
Email: . umarzaman2010@gmail.com
Bidur Kiran Tiwari
Bidur Kiran Tiwari
(Ph.D.)
(Ph.D.)
MS: IE, Kangwon National University
BS: ECE, National college of Information Technology, Nepal
Research Interests: Incomplete Data Handling, Data Meta-Visualization, AI
Email: bidur.kiran.tiwari@kumoh.ac.kr
Alumni
Alumni
Ninda Nurseha Amalina (MS)
Ninda Nurseha Amalina (MS)
Papers:
[1] Amalina, Ninda Nurseha, and Heungjo An. 2026. "A Multi-Head Attention Soft Random Forest for Interpretable Patient No-Show Prediction" Systems 14, no. 5: 576. https://doi.org/10.3390/systems14050576
[2]Adaptive Double-Booking Strategy for Outpatient Scheduling Using Multi-Objective Reinforcement Learning, arXiv:2603.07270
MS: IE, Kumoh National Institute of Technology, South Korea
BS: IE, Maranatha Christian University, Indonesia
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