Projects
Topics in Causal Machine Learning
Human-centric AI that aims at Interaction between human and ML&DL models via explainability and visualization
Augmenting BERT-based TOD system with financial ontology (Jisoo Jang) – combining ML model and knowledge base [LINK]
Human-centric model training (Jisoo Jang) – new design of NN structure and converting human input to part of the model
Fusion of ML and knowledge base/human input (WSL’s BP project): Bi-direction exchange between ML and knowledge base with flexible converting operators
XAI aims at Interpretation, reliability, verifiability, traceability, auditability, trustworthiness, fairness, and safety of ML&DL models
XAI for professional classic piano music (Jisoo Park, Bjørn Are Therkelsen)
Device Life Expectation Estimation (Jisoo / Chang) [LINK]
ML model reliability testing and safety assessment (TBD)
XAI for high criminal rate (student group project)
Bias analysis for criminal sentencing (XAI class student project)
XAI for factors with time delay effects or continuous effects (WSL, Jisoo Jang, Naomi) scenarios include
The high crime rate, supply chain bottleneck,
Air pollution, global warming. This is a combination of ambient AI and XAI (Cha, WSL)
Sleeping disorder analysis (Chang Sub Chang)
Causal discovery aims to find causal structures by analyzing observational data.
Data partition and parallel causal discovery (Iju Lee)
The architectural design of GNN Ensemble (Duc) – similar to TPOT/AutoML
Graph database system (Iiju, Alex)
Knowledge discovery in supply chain graphic data (Naomi)