Paola Casti

Employment

• University of Rome Tor Vergata Researcher

Education

• Universita degli Studi di Roma Tor Vergata Ph.D. in Microelectronic and Telecommunication Engineering
  2011 - 2015

Projects & Funding

Projects & funding information is unavailable.

Publications (36)

• Modeling Noise and Artifacts in Brain Organoids Spike Measurements for Robust 2-D MEA Analysis
  IEEE Transactions on Instrumentation and Measurement 2026 DOI: 10.1109/TIM.2026.3661704
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• High-throughput contractility analysis platform for drug-response evaluation of hiPSC-derived cardiac models based on dynamic frequency conditioning
  Biosensors and Bioelectronics 2026 DOI: 10.1016/j.bios.2026.118717
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• Effects of Uncertainty Contributions on the Examination of Alzheimer’s Disease via MEG Data and Graph Neural Networks
  IEEE Transactions on Instrumentation and Measurement 2025 DOI: 10.1109/TIM.2025.3643054
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• Lab-on-Chip Label-Free Sensing System for Electroporation Based on Time-Lapse Microscopy
  IEEE Sensors Journal 2024 DOI: 10.1109/JSEN.2024.3463959
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• Optically Induced Dielectrophoresis and Machine Learning Algorithms for the Identification of the Circulating Tumor Cells
  2024 DOI: 10.3390/proceedings2024097071
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• Development of integrated optical biosensors based on low-cost stereolithography fabrication and multispectral signature for Lab-On-Chip applications
  Sensors and Actuators B: Chemical 2024 DOI: 10.1016/J.SNB.2023.135009
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• S3-VAE: A novel Supervised-Source-Separation Variational AutoEncoder algorithm to discriminate tumor cell lines in time-lapse microscopy images
  Expert Systems with Applications 2023 DOI: 10.1016/J.ESWA.2023.120861
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• Uncertainty-Based Feature Selection for Improved Adequacy of Dermoscopic Image Classification
  IEEE Transactions on Instrumentation and Measurement 2023 DOI: 10.1109/TIM.2023.3303498
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• OET Cell Signature: Cells Discrimination and Drug Response Evaluation with Opto-Electronic Tweezers and Machine Learning Algorithms
  2022 DOI: 10.2139/ssrn.4013205
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• Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography
  Neural Computing and Applications 2021 DOI: 10.1007/s00521-021-06105-4
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