Scholar
Dong-Wan Choi
Google Scholar ID: ZL6COHUAAAAJ
Associate Professor of Department of Computer Engineering, Inha University
Citations & Impact
Citations
270
H-index
10
i10-index
10
Publications
20
Co-authors
17
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Best Paper Award in AI, KSC2024 (Dec. 2024)
- Best Paper Award (Runner Up) in AI, KSC2023 (Dec. 2023)
- Best Paper Award (Runner Up) in AI, KSC2022 (Dec. 2022)
- Best Paper Award in AI, KSC2020 (Dec. 2020)
- Best Paper Award (Runner Up) in AI, KCC2020 (July 2020)
- Outstanding PhD Thesis Award, Department of Computer Science, KAIST (Feb. 2014)
- Principal Investigator, 'People-Centered AI Core Technology Development' Project, IITP (Apr. 2022 – Present)
- Principal Investigator, Basic Research Projects, National Research Foundation of Korea (NRF) (June 2021 – Present; June 2018 – 2021)
- Principal Investigator, ICT & Broadcasting R&D Project, IITP (Apr. 2019 – 2021)
Research Experience
- Visiting Scholar, Department of Computing, Imperial College London, UK (Jan. 2024 – Dec. 2024)
- Assistant → Associate Professor, Department of Computer Engineering, Inha University, Korea (Mar. 2018 – Present)
- Assistant Professor, School of Software, Kookmin University, Korea (Sep. 2017 – Feb. 2018)
- Research Associate, Department of Computing, Imperial College London, UK (Nov. 2016 – Aug. 2017); with Dr. Thomas Heinis
- Postdoctoral Fellow, School of Computing Science, Simon Fraser University, Canada (Jan. 2015 – Oct. 2016); with Dr. Jian Pei
- Postdoctoral Researcher, Department of Computer Science, KAIST, Korea (Mar. 2014 – Dec. 2014)
Background
- Associate Professor in the Department of Computer Engineering at Inha University, Korea
- Research interests include continual (lifelong) learning, neural network compression, and federated learning
- Focuses on advancing the efficiency, scalability, and accessibility of machine learning over big data
- Strong background in deep learning, data mining, algorithms, and databases
- Pursues research that is both theoretically rigorous and practically efficient
