Publications

2025

A. W. Mulyadi, L. Wehling, A. Kumar, N. Boucher, F. Abdessalem, S. Jager, M. H. Mosa, T. Klabunde, T. Andreani, G Singh, ``BioMedReasoner: Towards Multi-Hop Reasoning using Path-based Relational Learning on Biomedical Knowledge Graphs”, 2025 NeurIPS Workshop: AI for Science, San Diego, USA, December 2-7, 2025.
K. Oh, D.-W. Heo, A. W. Mulyadi, W. Jung, E. Kang, K. H. Lee, H.-I. Suk, “A quantitatively interpretable model for Alzheimer’s disease prediction using deep counterfactuals,” NeuroImage, Vol. 309, 2025.

G. Singh, L. Wehling, A. W. Mulyadi*, R. H. Sreenath, T. Klabunde, T. Andreani and D. McCloskey, “Talk2Biomodels and Talk2KnowledgeGraph: AI agent-based application for prediction of patient biomarkers and reasoning over biomedical knowledge graphs”, 2025 ICLR Workshop: Machine Learning for Genomics Explorations, 2025. *) Equally contributed.
W. Park, A. W. Mulyadi, E. Kang, and H.-I. Suk, “Prototype-Guided Contrastive Knowledge Graph Representation Learning for Diagnosis Prediction,” International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI), Jeju Island, Korea, 2024. (Oral Presentation)

A. W. Mulyadi and H.-I. Suk, “Harnessing Personalized Medical Knowledge Graph for Safe Medication Recommendation,” Explainable Artificial Intelligence (XAI) Workshop, Korea Artificial Intelligence Society Korea Computer Science Conference, Jeju Island, South Korea, 2023.
A. W. Mulyadi, J. S. Yoon, E. Jeon, W. Ko, and H.-I. Suk, “An Introduction to Neural Networks and Deep Learning,” Deep Learning for Medical Image Analysis, Elsevier, 2023.
A. W. Mulyadi and H.-I. Suk, “KindMed: Knowledge-Induced Medicine Prescribing Network for Medication Recommendation,” arXiv preprint, arXiv:2310.14552, 2023.
S. Jeong, W. Ko, A. W. Mulyadi, and H.-I. Suk, “Deep Efficient Continuous Manifold Learning for Time Series Modeling,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
A. W. Mulyadi, W. Jung, K. Oh, J.S. Yoon, K.H. Lee and H.-I. Suk, “Estimating Explainable Alzheimer’s Disease Likelihood Map via Clinically-guided Prototype Learning,” NeuroImage, vol. 273, pp. 120073, June, 2023.

A. W. Mulyadi, W. Jung, K. Oh, J.S. Yoon, and H.-I. Suk, “Clinically-guided Prototype Learning and Its Use for Explanation in Alzheimer’s Disease Identification,” 2022 NeurIPS Workshop: ‘Medical Imaging meets NeurIPS (MedNeurIPS)’, New Orleans, USA, November 28-December 3, 2022. (Oral Presentation)
K. Oh, D.-W. Heo, A. W. Mulyadi, W. Jung, E. Kang, and H.-I. Suk, “Quantifying Explainability of Counterfactual-Guided MRI Feature for Alzheimer’s Disease Prediction,” 2022 NeurIPS Workshop: ‘Medical Imaging meets NeurIPS (MedNeurIPS)’, New Orleans, USA, November 28-December 3, 2022.
H.-I. Suk, and A. W. Mulyadi, “Method and Apparatus for Predicting and Explaining Diagnosis of Alzheimer’s Disease,” Korean Patent, No. 10-2022-0143264, November 2022.
A. W. Mulyadi, W. Jung, K. Oh, J.S. Yoon, and H.-I. Suk, “Topological-aware Prototype Learning for Estimating Explainable Alzheimer’s Disease Likelihood Map,” Conference of Korea Artificial Intelligence Association (KAIA), Jeju Island, South Korea, 2022.
A. W. Mulyadi, W. Jung, K. Oh, J.S. Yoon, and H.-I. Suk, “XADLiME: eXplainable Alzheimer’s Disease Likelihood Map Estimation via Clinically-guided Prototype Learning,” arXiv preprint arXiv:2207.13223, 2022.
A. W. Mulyadi, E. Jun and H.-I. Suk, “Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Series,” IEEE Transactions on Cybernetics, vol. 52(9), pp. 9684 - 9694, 2022.

W. Ko, W. Jung, E. Jeon, A. W. Mulyadi, and H.-I. Suk, “ENGINE: Enhancing Neuroimaging and Genetic Information by Neural Embedding,” Proc. 21st IEEE International Conference on Data Mining (ICDM), Auckland, New Zealand, December 7-10, 2021.
A. W. Mulyadi and H.-I. Suk, “ProtoBrainMaps: Prototypical Brain Maps for Alzheimer’s Disease Progression Modeling,” Medical Imaging with Deep Learning (MIDL) (Short Paper), Lübeck, Germany, 2021.

E. Jun, A. W. Mulyadi, J. Choi and H.-I. Suk, “Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32(9), pp. 4052 - 4062, 2020.

W. Jung, A. W. Mulyadi, and H.-I. Suk, “Unified Modeling of Imputation, Forecasting, and Prediction for AD Progression,” 2019 Medical Image Computing and Computer Assisted Intervention (MICCAI), Shenzhen, China, 2019.
E. Jun*, A. W. Mulyadi*, and H.-I. Suk, “Stochastic Imputation and Uncertainty-Aware Attention to EHR for Mortality Prediction,” 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 2019, pp. 1-7. *) Equally contributed.

A. W. Mulyadi, C. Machbub, A. S. Prihatmanto, and B.-K. Sin, “Design of Music Learning Assistant Based on Audio Music and Music Score Recognition,” 한국멀티미디어학회논문지, vol. 19, no. 5, pp. 826–836, May 2016.

A. W. Mulyadi, B.-K. Sin, “Music Learning Assistant Using Audio-Visual Analysis,” 한국정보과학회 2015년 동계학술발표회 논문집, pp. 733 - 734, 2015.