Rate My Professor Veronica Liesaputra

Liesaputra's research specializes in human-oriented AI, bridging natural language processing, information retrieval, computer vision, machine learning, deep learning, and human-computer interaction. She focuses on creating explainable AI models that deliver comprehensible explanations for decisions and predictions. Key applications encompass question-answering from medical images and documents for diagnosis and treatment, accessible explanations of legal cases for non-experts, and cross-lingual Māori-English document search and translation. As a member of the Otago AI Group, her contributions appear in prominent journals. Selected publications include Wang, Liesaputra, & Huang (2025), 'Gated parallel feature fusion multi-task learning for motor imagery EEG classification,' Expert Systems with Applications; Johnston et al. (2024), 'Low-intensity pulsed electric field processing prior to germination improves in vitro digestibility of faba bean (Vicia faba L.) flour...,' Food Chemistry; Thekkekara, Yongchareon, & Liesaputra (2024), 'An attention-based CNN-BiLSTM model for depression detection on social media text,' Expert Systems with Applications; Wang et al. (2024), 'An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification,' Artificial Intelligence in Medicine; and Liesaputra & Witten (2009), 'Computer graphics techniques for modeling page turning.'