Main research areas & tools

  • Deep learning / Machine learning
  • Statistical signal processing
  • Information theory
  • Many areas that deal with mathematics and computation
  • Main tools: Python/NumPy, C++/CUDA, Pytorch

Current Projects

Core machine intelligence algorithms

  • Neural network based denoising / estimation
  • Multi-task / continual / incremental learning (Being supported by NRF Mid-Career Project (2021-2025) )
  • Interpretable machine learning (Supported by KIST, Joint work with Prof. Klaus-Robert Müller)
  • Fairness in machine learning (Supported by IITP, Joint work with Prof. Flavio Calmon)

Data science applications

  • Pre-training large-scale multi-modal data (Supported by IITP-MSRA (Microsoft Research Asia))
  • Neuroscience / Medical data analyses (Joint work with Prof. Choong-Wan Woo, Prof. Jangsup Moon)
  • Random variation prediction for semiconductor manufacturing (Supported by KEIT, Joint work with Prof. Chang-Hwan Shin)

Past Projects

  • Interpretable machine learning (Supported by KIST (2018-2020), Joint work with Prof. Klaus-Robert Müller)
  • Adaptive machine learning for digital companion (Supported by IITP (2016-2020))
  • Satellite data based PM2.5 level estimation (Joint work with Prof. Yang Liu)
  • Neural network based denoising / estimation (Supported by NRF(2016-2019), Samsung(2018-2019))
  • Non-intrusive load monitoring (Jointly with Encored Technologies, Inc., SNU ADSL Lab)
  • DNA sequence denoising (Jointly with SNU DSAI Lab)