Camillo Maria Caruso

Unit of Artificial Intelligence & Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma

via Álvaro del Portillo 21

00128 Rome RM, Italy

👋 Hello! I’m Camillo, an ML Researcher with a Ph.D. in AI. I design and build multimodal deep-learning systems that turn heterogeneous clinical data, imaging, tabular records, free text, into actionable tools for diagnosis, prognosis, and treatment support, with a particular focus on learning robustly from incomplete information.

I earned my Ph.D. at University Campus Bio-Medico of Rome, where I developed transformer-based architectures resilient to missing data for classification and time-to-event prediction tasks, publishing in venues such as AI Open, Computers in Biology and Medicine, and IEEE Access, and releasing every model as open-source code.

Outside the lab I’m a lifelong scout, happiest when planning menus, packing supplies, and making sure our summer camps run without a hitch.

selected publications

AI Open

Not another imputation method: A transformer-based model for missing values in tabular datasets

Camillo Maria Caruso, Paolo Soda, and Valerio Guarrasi

AI Open, 2026

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Handling missing values in tabular datasets presents a significant challenge in training and testing artificial intelligence models, an issue usually addressed using imputation techniques. Here we introduce “Not Another Imputation Method” (NAIM), a novel transformer-based model specifically designed to address this issue without the need for traditional imputation techniques. NAIM’s ability to avoid the necessity of imputing missing values and to effectively learn from available data relies on two main techniques: the use of feature-specific embeddings to encode both categorical and numerical features also handling missing inputs; the modification of the masked self-attention mechanism to completely mask out the contributions of missing data. Additionally, a novel regularization technique is introduced to enhance the model’s generalization capability from incomplete data. We extensively evaluated NAIM on 5 publicly available tabular datasets, demonstrating its superior performance over 6 state-of-the-art machine learning models and 5 deep learning models, each paired with 3 different imputation techniques when necessary. The results highlight the efficacy of NAIM in improving predictive performance and resilience in the presence of missing data. To facilitate further research and practical application in handling missing data without traditional imputation methods, we made the code for NAIM available at https://github.com/cosbidev/NAIM.
CIBM

MARIA: a Multimodal Transformer Model for Incomplete Healthcare Data

Camillo Maria Caruso, Paolo Soda, and Valerio Guarrasi

Computers in Biology and Medicine, 2025

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In healthcare, the integration of multimodal data is pivotal for developing comprehensive diagnostic and predictive models. However, managing missing data remains a significant challenge in real-world applications. We introduce MARIA (Multimodal Attention Resilient to Incomplete datA), a novel transformer-based deep learning model designed to address these challenges through an intermediate fusion strategy. Unlike conventional approaches that depend on imputation, MARIA utilizes a modified masked self-attention mechanism, which processes only the available data without generating synthetic values. This approach enables it to effectively handle incomplete datasets, enhancing robustness and minimizing biases introduced by imputation methods. We evaluated MARIA against 10 state-of-the-art machine learning and deep learning models across 8 diagnostic and prognostic tasks. The results demonstrate that MARIA outperforms existing methods in terms of performance and resilience to varying levels of data incompleteness, underscoring its potential for critical healthcare applications. To support transparency and encourage further research, the source code is openly available at https://github.com/cosbidev/MARIA.