Kaist

Overall system architecture for EEG signal classification with Deep RL (IEEE SMC 2018)

Attentional Control for Time-Series Data (Master's Thesis)

Master’s thesis at KAIST on attentional control for time-series classification and synthesis, solving the memory-based vs. memoryless trade-off for EEG signals. Oral presentation at IEEE SMC 2018.

Hunter-prey model and Deep Boltzmann Machine diagrams for multi-agent cognitive policy learning (KIIS 2016)

Multi-Agent Cognitive Policy Learning through Competition

Undergraduate research at KAIST on multi-agent cognitive policy learning through competitive reinforcement learning, demonstrating emergence of complex behaviors. Won Best Paper Award at 2016 KIIS Conference.