Duong Nhu

Duong Nhu profile photo

I am a Research Fellow working on artificial intelligence, digital health, and clinical decision support for epilepsy. My research focuses on deep learning for EEG analysis, automated interictal epileptiform discharge detection, multimodal treatment-response prediction, wearable sensing, and anti-seizure medication management.

My work is motivated by clinically useful AI: models that can support diagnosis, monitoring, and treatment decisions in complex neurological conditions.

Current role: Research Fellow, Department of Neuroscience, Monash University
Research themes: epilepsy, EEG, deep learning, wearable technology, signal processing, health informatics
Full publication list: Google Scholar

Research

Epilepsy AI and EEG

I develop machine learning methods for epilepsy monitoring and diagnosis, with a focus on scalp EEG and interictal epileptiform discharges. This includes scalable time-series classification, graph neural networks, generative models, and evaluation methods that are closer to clinical deployment.

Multimodal Precision Medicine

I work on multimodal approaches that combine EEG, MRI, genomics, and clinical data to support anti-seizure medication selection and treatment-response prediction in newly diagnosed epilepsy.

Wearable and Digital Health

I collaborate on wearable sensing systems for neurological and physiological monitoring, including user-centered studies of seizure detection devices and AI-enabled compact sensing systems.

Publications

For citation metrics and the complete, current publication list, see my Google Scholar profile.

Recent Work

  1. D. K. Pham, D. Mehta, Y. Jiang, D. Thom, R. S. K. Chang, M. Nazem-Zadeh, E. Foster, T. Fazio, S. Holper, K. Verspoor, J. Liu, D. Nhu, S. Barnard, T. O’Brien, Z. Chen, J. French, P. Kwan, and Z. Ge. Knowledge Tree Driven Contextualized Instruction Tuning of Foundation Models for Epilepsy Drug Recommendation. MICCAI 2025 Proceedings, 2026. DOI

  2. L. Zhang, D. Nhu, Y. Zhao, E. Foster, L. Millist, S. Sivathamboo, P. Kwan, Z. Ge, L. Du, and L. Kuhlmann. Interictal Epileptiform Discharge Detection Through Probabilistic Diffusion Models with Maximization of Precision Recall Metrics. International Journal of Neural Systems, 2026. DOI

  3. L. W. Yap, A. Levin, Y. Jiang, D. Nhu, S. Gong, R. Warty, D. Vera Anaya, Q. Li, Y. Lu, R. Gao, X. Zhang, T. Ilyas, V. Smith M. M., A. Thomas, A. Wibrianto, Y. Zhang, J. Limas, S. McCracken, J. M. Morris, B. W. J. Mol, E. M. Wallace, L. A. Ju, Z. Ge, F. Marzbanrad, and W. Cheng. An intelligent, compact wearable pressure-strain combo sensor system for continuous fetal movement monitoring. Science Advances, 11(48), eady2661, 2025. DOI

  4. W. Feng, D. Nhu, A. Anderson, D. Thom, S. N. Barnard, R. Zeibich, E. Foster, and collaborators. Integrative Deep Learning of Genomic and Clinical Data for Predicting Treatment Response in Newly Diagnosed Epilepsy. Neurology, 2025. DOI

  5. D. Nhu, J. Liu, R. S. Chang, D. Thom, Z. Chen, M. Nazem-Zadeh, A. Anderson, S. Barnard, J. French, P. Kwan, and Z. Ge. Predicting Antiseizure Medication Outcomes in Early Diagnosed Epilepsy: A Multimodal Framework Using EEG, MRI, and Clinical Data. medRxiv, 2025. DOI

  6. M. Janmohamed, D. Nhu, L. Shakhatreh, O. Gonen, L. Kuhlmann, A. Gilligan, C. W. Tan, P. Perucca, T. J. O’Brien, and P. Kwan. Comparison of Automated Spike Detection Software in Detecting Epileptiform Abnormalities on Scalp-EEG of Genetic Generalized Epilepsy Patients. Journal of Clinical Neurophysiology, 41(7), 618-624, 2024. DOI

  7. D. Nhu, M. M. A. Janmohamed, L. I. K. Shakhatreh, O. Gonen, P. Perucca, A. Gilligan, P. Kwan, T. J. O’Brien, C. W. Tan, and L. Kuhlmann. Automated interictal epileptiform discharge detection from scalp EEG using scalable time-series classification approaches. International Journal of Neural Systems, 33(1), 2350001, 2023. DOI

Reviews and Clinical Perspectives

  1. D. Nhu, M. M. A. Janmohamed, A. Antonic-Baker, P. Perucca, T. J. O’Brien, A. Gilligan, P. Kwan, C. W. Tan, and L. Kuhlmann. Deep learning for automated epileptiform discharge detection from scalp EEG: a systematic review. Journal of Neural Engineering, 19(5), 2022. DOI

  2. M. M. A. Janmohamed, D. Nhu, L. Kuhlmann, A. Gilligan, C. W. Tan, P. Perucca, T. J. O’Brien, and P. Kwan. Moving the field forward: detection of epileptiform abnormalities on scalp electroencephalography using deep learning: clinical application perspectives. Brain Communications, 4(5), fcac218, 2022. DOI

  3. S. Sivathamboo, D. Nhu, L. Piccenna, A. Yang, A. Antonic-Baker, S. Vishwanath, M. Todaro, L. W. Yap, L. Kuhlmann, W. Cheng, T. J. O’Brien, N. A. Lannin, and P. Kwan. Preferences and User Experiences of Wearable Devices in Epilepsy: A Systematic Review and Mixed-Methods Synthesis. Neurology, 99(13), e1380-e1392, 2022. DOI

Earlier Work

  1. D. Nhu, M. Janmohamed, L. Shakhatreh, O. Gonen, P. Kwan, A. Gilligan, C. W. Tan, and L. Kuhlmann. Automated Inter-Ictal Epileptiform Discharge Detection from Routine EEG. Healthier Lives, Digitally Enabled, 276, 65-71, 2021. DOI

  2. D. Nhu, M. Janmohamed, P. Perucca, A. Gilligan, P. Kwan, T. O’Brien, C. W. Tan, and L. Kuhlmann. Graph Convolutional Network for Generalized Epileptiform Abnormality Detection on EEG. IEEE Signal Processing in Medicine and Biology Symposium, 2021. DOI

  3. D. Nhu and C. Chua. Preprocessing Code Example for Searching. Proceedings of the 24th Australasian Software Engineering Conference, 28-32, 2015. DOI

Project

Machine learning for physiological monitoring and epilepsy diagnosis
Primary Chief Investigator, with collaborators and supervisors across Monash, Epworth HealthCare, and Alfred Health. This project focused on machine learning methods for physiological monitoring and epilepsy diagnosis.

Education

Awards and Recognition

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