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H. Wayne Huizenga
College of Business and Entrepreneurship
College of Business and Entrepreneurship
Assistant Professor - Decision Sciences
Management
H. Wayne Huizenga College of Business & Entrepreneurship
Vanessa D’Amario holds undergraduate and graduate degrees in Physics from the University of Genova, Italy. She earned her PhD in Computer Science in 2020 from the same institution, focusing her dissertation on machine learning methods in epilepsy research.
Vanessa served as a visiting PhD student and later as a postdoctoral researcher at the Department of Brain and Cognitive Sciences (MIT) where she conducted joint research in industry with Fujitsu Research of America. She joined NSU in 2023 and she is currently an assistant professor in the Department of Decision Sciences. Her research experience spans machine learning for healthcare, emphasizing predictive analytics, generalization, and large-scale machine learning experiments. Her scholarly interests focus on leveraging technology to improve health outcomes in low-resource settings and promote social good. Currently, she is exploring how artificial intelligence can contribute to these goals, particularly through large language models and object detection algorithms.
Ramim, M., D’Amario, V., Wallace-Ross, J. & Bronsburg, S. (2024). Design and Implementation of a Novel Electronic Health Record (EHR) System for Knowledge Sharing at an International Medical Outreach Program Field Clinic. In Online Journal of Applied Knowledge Management (Vol. 12, No. 2, pp. 59-77)
D'Amario, V., Sasaki, T., & Boix, X. (2021). How Modular should Neural Module Networks Be for Systematic Generalization?. Advances in Neural Information Processing Systems, 34, 23374-23385
Casper, S., Boix, X., D'Amario, V., Guo, L., Schrimpf, M., Vinken, K., & Kreiman, G. (2021, May). Frivolous units: Wider networks are not really that wide. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 8, pp. 6921-6929).
D’Amario, V., Arnulfo, G., Nobili, L., & Barla, A. (2018, September). Classification of Epileptic Activity Through Temporal and Spatial Characterization of Intracranial Recordings. In International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (pp. 69-79). Cham: Springer International Publishing.
D’Amario, V., Kannayan, M. , Jain, S., Renesca, V. & Rozenfeld, I,. (October 22-25, 2025). Investigating Biomarker Associations in ME / CFS: a Data-Driven Approach to Understand Symptom Severity. at the International Association for Chronic Fatigue Syndrome / Myalgic Encephalomyelitis, 17th Research and Clinical Conference
Khurana, K., Edamadaka, D., Tozzo, V. & D’Amario V. (September 6-8, 2024). Interpretable Multi-Variate Time Series Machine Learning Algorithm for Seizure Prediction. at the 7th International Conference on Healthcare Service Management (ICHSM 2024)
Bronsburg, S. & D’Amario, V. (September 8-10, 2023). Health Informatics: Machine Learning and Artificial Intelligence. at the Florida Osteopathic Medical Association (FOMA) Virtual Seminar 2023
Smith, B., Ramadoss, T., D’Amario, V., Shoja, M. M., Rajput, V., & Cervantes, J. (2025). Utilization and perception of generative artificial intelligence by medical students in residency applications. Journal of Investigative Medicine, 73(4), 338-344.
Rahimi, A., D'Amario, V., Yamada, M., Takemoto, K., Sasaki, T., & Boix, X. (2024). D3: Data Diversity Design for Systematic Generalization in Visual Question Answering. Transactions on Machine Learning Research.
Cervantes, J., Smith, B., Ramadoss, T., D’Amario, V., Shoja, M. M., & Rajput, V. (2024). Decoding medical educators’ perceptions on generative artificial intelligence in medical education. Journal of Investigative Medicine, 72(7), 633-639.
Giacomini, T., Luria, G., D’Amario, V., Croci, C., Cataldi, M., Piai, M., ... & Nobili, L. (2022). On the role of REM sleep microstructure in suppressing interictal spikes in Electrical Status Epilepticus during Sleep. Clinical Neurophysiology, 136, 62-68.
D'Amario, V., Srivastava, S., Sasaki, T., & Boix, X. (2022). The data efficiency of deep learning is degraded by unnecessary input dimensions. Frontiers in Computational Neuroscience, 16, 760085.