👋 Hi, I’m Prisca, a machine learning engineer and data scientist passionate about applying mathematical and computational methods to solve complex real-world problems.

I hold a PhD in Biomedical Engineering (University of Bern) and an MSc in Mathematics (EPFL). Currently, I work as an AI Consultant at Artificialy, where I apply ML and mathematical optimisation expertise to real-world engineering problems across diverse domains. I also previously collaborated with the SIB Swiss Institute of Bioinformatics on ML workflows for genomic data analysis.

My research and work experience bridge deep learning, mathematics, and applied sciences, with a focus on:

• End-to-end AI/ML pipelines for video and biomedical imaging (3D U-Net, CNNs, GNNs)
• Computer vision: pose estimation, object detection, segmentation
• Genomic data analysis, feature selection, and predictive modelling
• Reproducible workflows, open-source development, and collaborative science
• Interdisciplinary projects spanning computational, biological, and engineering domains

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Work Highlights

• AI Consultant — Artificialy (Dec 2025 – Present) Applying ML and mathematical optimisation to real-world engineering problems across diverse domains. Managing client relationships alongside hands-on technical delivery.
• Generative AI Intern — Skyscraper Software (Jun–Dec 2025) Evaluated deep learning and computer vision models (including pose estimation in video) for integration into mobile healthcare applications.
• Data Analyst — SIB Swiss Institute of Bioinformatics (May–Nov 2025) Designed and implemented ML pipelines for feature selection and predictive modelling on genomic datasets. Contributed to the open-source package PAGEpy.
• Visiting Research Scientist — University of Melbourne (Jun 2024) Validated deep learning models using mechanistic modelling and synthetic datasets to improve segmentation accuracy in microscopy imaging.

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Research Highlights

• Deep learning for subcellular calcium imaging Developed the first AI-driven framework for detection, segmentation, and classification of Ca²⁺ release events in cardiac cells.
  • 📄 Published as first author in Cell Calcium (2024)
  • 📂 Open dataset on Zenodo
  • 💻 GitHub Repositories:
    • Deep Learning Model
    • Graphical Interface
• Graph neural networks for formal verification Research at the University of Fribourg on graph neural networks (GNNs) applied to verification of cyber-physical systems, working with automata theory and linear temporal logic.
  • 📄 Preprint: Analyzing Büchi Automata with Graph Neural Networks (2022)
• International collaborations Visiting Research Scientist at the University of Melbourne, validating deep learning models with synthetic data and mechanistic models.
• Open-source contributions Contributor to PAGEpy, a bioinformatics package for genomic feature selection and classification.

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Publications

• Prisca Dotti et al. (2024). A deep learning-based approach for efficient detection and classification of local Ca²⁺ release events in full-frame confocal imaging. Cell Calcium. DOI link
• Stammet C, Dotti P, Ultes-Nitsche U, Fischer A. (2022). Analyzing Büchi Automata with Graph Neural Networks. Preprint. DOI link

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Conferences

• ESC Working Group on Cardiac Cellular Electrophysiology – Toledo, Spain (2022)
• Biophysical Society Annual Meeting – San Diego, USA (2023)
• German Physiological Society – Berlin, Germany (2023)
• Bern Data Science Day – Bern, Switzerland (2023)

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Beyond Research

Outside work, I am committed to inclusion and accessibility. Since 2012, I’ve volunteered with the Ticino Association of Parents and Friends of Children in Need of Special Education, leading inclusive camps for neurodiverse youth.

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Skills

• 🖥️ Programming & Tools Python (PyTorch, TensorFlow, scikit-learn, SciPy, pandas, matplotlib), Git, Slurm (HPC), Bash/CLI, basic knowledge of C/C++
• 📊 Data Science & AI Deep learning, machine learning, computer vision, feature engineering, model development, pipeline design, quantitative analysis, data visualisation
• 🧬 Bioinformatics & Computational Biology Genomic data processing, biomedical image and video analysis
• ➗ Mathematics Statistics, optimisation, linear algebra, graph theory, signal processing, automata theory
• 🌍 Languages Italian (native), English (fluent), French (fluent), German (intermediate)