MIT Critical Data expert, Leveraging data to devise machine learning systems for clinical, management and strategic decision support to improve quality of care, optimize processes and discovering knowledge. Leading the data science team through the envisioning and development of data-driven solutions and AI products. On a perpetual fight against bias.
Areas of interest
- Reinforcement learning
- Interpretable Machine Learning
- Automated Machine Learning
- Transfer Learning
- Proactive analytics
- Distributed computing
- Predictive modeling
Current Opinion in Anesthesiology 33, no. 2 (2020): 162-169.
2020 “ICU management based on big data” Falini, S., Angelotti, G., & Cecconi, M. 40th IEEE Engineering in Medicine and Biology Society International Conference 2018
“The Role of Baroreflex Sensitivity in Acute Hypotensive Episodes Prediction in the Intensive Care Unit” 16th International Conference on Complex Acute Illness, ICCAI
July 2017
“Advanced signal processing for monitoring and assessment in the ICU” about assessing the impact of advanced signal processing techniques coupled with machine learning models in intensive care settings.
