Hyerim Bae(배혜림)
Professor
Basics
| Name | Hyerim Bae |
| Label | Professor |
| hrbae@pusan.ac.kr | |
| Phone | +82-51-510-2733 |
| Url | https://baelab.pusan.ac.kr |
| Summary | Professor of Industrial Engineering and Dean of the Graduate School of Data Science at Pusan National University, leading the Big Data Analytics and Engineering Lab. |
Work
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2025.06 - Now -
2025.06 - Now -
2024.03 - Now -
2023.01 - 2023.12 -
2022.01 - Now 연구소장 / Director
부산대학교 지능물류빅데이터연구소 / Pusan National University, Intelligent Logistics Big Data Research Institute
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2019.01 - 2020.12 -
2019.01 - 2020.12 부원장 / Associate Director
부산대학교 교육인증원 / Pusan National University, Office of Educational Accreditation
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2018.01 - Now -
2017.01 - 2018.12 전문위원 / Expert Committee Member
부산과학기술기획평가원 / Busan Institute of Science and Technology Evaluation and Planning
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2016.01 - Now -
2016.01 - Now -
2016.01 - Now -
2016.01 - 2020.12 -
2016.01 - Now -
2014.01 - 2015.12 -
2011.01 - 2012.12 기술사업부장 / Technology Business Division Manager
부산대학교 산학협력단 / Pusan National University, Industry-University Cooperation Foundation
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2010.01 - Now -
2007.01 - 2011.12 -
2005.01 - 2010.12 교육지원실장 / Education Support Division Manager
차세대물류IT연구사업단 / Next Generation Logistics IT Research Center
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2004.03 - Now
Education
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1998.03 - 2002.09 Seoul, South Korea
PhD
Seoul National University, Seoul, South Korea
Industrial Engineering
- e-Business 문서관리: 워크플로우 프로세스를 위한 비즈니스 문서 변경관리, 공학박사학위논문, 서울대학교, 2002.08
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1996.03 - 1998.03 Seoul, South Korea
Master
Seoul National University, Seoul, South Korea
Industrial Engineering
- 동적 워크플로우 관리기의 개발, 공학석사학위논문, 서울대학교, 1998.02
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1989.03 - 1996.02 Seoul, South Korea
Publications
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2023.09.26 Correlation recurrent units: A novel neural architecture for improving the predictive performance of time-series data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proposed a novel neural architecture called Correlation Recurrent Unit (CRU) that performs time-series decomposition within neural cells and learns correlations between decomposition components, achieving over 10% improvement in long- and short-term predictive performance.
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2008.05.15 Genetic algorithms for job shop scheduling problems with alternative routings
International Journal of Production Research
Developed mixed-integer linear programming (MILP) formulations and genetic algorithms for job shop scheduling problems with alternative machine routings, addressing four performance measures: mean flow time, makespan, maximum lateness, and total absolute deviation from due dates.
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2004.08.31 Automatic control of workflow processes using ECA rules
IEEE transactions on knowledge and data engineering
Proposed a novel approach for automatic execution of business processes using event-condition-action (ECA) rules with active database capabilities, introducing block-based process classification and hierarchical tree transformation methods.
Languages
| Korean | |
| Native speaker |
| English | |
| Fluent |
Interests
| Big Data Analytics | |
| Data Mining | |
| Machine Learning | |
| Predictive Analytics | |
| Statistical Analysis |
| Artificial Intelligence | |
| Deep Learning | |
| Neural Networks | |
| Time Series Forecasting | |
| LLMs |
| Process Mining | |
| Business Process Analysis | |
| Workflow Optimization | |
| Process Discovery | |
| Performance Analysis |
| Reinforcement Learning | |
| Decision Making | |
| Optimization | |
| Dynamic Programming |
| Industrial Engineering | |
| Operations Research | |
| Supply Chain Management | |
| Quality Control | |
| Manufacturing Systems |
| Port Logistics & SCM | |
| Port Logistics | |
| Supply Chain Optimization | |
| Transportation Systems | |
| Warehouse Management |