CV

You can explore my full professional background below or download a one-page PDF version for offline viewing.

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Skills

• Pipelines
  • API: Websockets and Rest
  • Databases: Microsoft and InfluxDB
  • Ingestion: Node-Red, MQTT
• Data Analysis, ML Ops & Software
  • Programming: MATLAB and Python
  • Libraries: Pytorch, TensorFlow and Scikit-learn
  • Dashboard: Dash, Streamlit, Django, React and Grafana
  • Containers: Docker
  • Versioning: GitHub
• Cloud Environments
  • Azure: Databricks and Data Factory
  • Amazon: Routing services
  • Heroku
  • Google Cloud Platform
• Operative Systems
  • Ubuntu
  • Windows

Work experience

• 2026-present: Postdoctoral Research Fellow, Brighton University, Brighton

• 2025–2026: Digital Transformation Lead, Newcastle University, Newcastle Upon Tyne
  Position funded by Innovate UK as a Knowledge Transfer Partnership associate with Newcastle University and Merit.

  • Process optimization: Translating manufacturing operational needs into digital transformation requirements.
  • Product owner: Development and coordination of PowerApps (e.g., MES solutions) to improve the operational visibility of manufacturing shop floors.
  • ETL pipelines: Implementation of workflows to store manufacturing-driven data streams into databases.
  • Data mining: Development of feature-extracted datasets by fusing manufacturing and design data. These data streams are leveraged by machine and deep learning models to improve production monitoring, planning, and scheduling.
  • Experimental campaign: Coordination of large teams and 30+ stakeholders ranging from C-level executives to shop floor workers to deploy digital transformation manufacturing architectures.
  • Dashboard: Design and development of data analytics platforms to turn manufacturing data into actionable KPIs and improve manufacturing monitoring and planning.

• 2021-2025: PhD Candidate, University of Trento, Chair: Manufacturing Systems

  • Thesis: Cyber-physical systems to monitor the efficiency and sustainability of human-centric manufacturing systems.
  • IoT sensor: Deployment of human-centric digital technologies to monitor manual task executions and safety of workers in industrial scenarios. Hands-on with many sensors such as RTLS (UWB), Inertial Measuring Units, Azure Kinect, RFID readers, and Electromyography. Responsible for designing the desired data flow and storage based on business requirements.
  • ML frameworks/pipeline: Applied ML algorithms to pre-processed and feature-extracted data, enabling target-oriented decision-making. Training, fine-tuning, and inference of PyTorch algorithms (supervised and unsupervised) in Jupyter Notebooks and HPC cluster.
  • Experimental campaign: Managing mid-term projects with plant supervisors to meet milestones and business goals. Responsible for architecture installation, data acquisition, and storage. Involvement of 70+ workers in production and logistics environments of both SMEs and multinational companies.
  • Dashboards: Delivering actionable KPIs and KRIs to drive strategic process monitoring and enable efficiency and safety-related business optimizations.
  • Supervision: Technical support and mentoring of students for Master’s, Bachelor thesis, and freshman Ph.D. students.
  • Dissemination: Deliver engaging presentations to both technical and non-technical audiences at major conferences, enhancing the team’s visibility and attracting potential new collaborations.

• 2023: Visiting PhD Candidate, University of Twente, Chair: Manufacturing Systems

  • Project: Development of a cyber-physical architecture to monitor human-centric reconfigurable manufacturing systems (RMS)
  • IoT sensor: Design, installation, and test of an UWB-based RTLS to monitor industrial entities and the workforce in a RMS set-up
  • ML frameworks/pipeline: Applied ML algorithms to pre-processed and feature-extracted data, enabling target-oriented decision-making. Training, fine-tuning, and inference of PyTorch algorithms (supervised and unsupervised) in Jupyter Notebooks.
  • Experimental campaign: Managed more than 40+ students across 8 production set-up, resulting in a dataset of more that 1M input data.
  • Dashboards: Delivering actionable KPIs to monitor the efficiency and social sustainability of RMS environments.
  • Supervision: Technical support and mentoring of Learning Factory students.

• 2020-2021: Research Assistant, University of Göttingen, Chair: Production and Logistics

  • Support: Lecture preparation and student mentoring and support throughout the course
  • Laboratory: Delivered 8 hours of lectures utilizing simulation tools to address real-world case applications
  • Research Project: Literature review on the usage of indoor positioning systems in manufacturing and logistics environments

Education

• MBA in Business Administration, London School of Economics, 2025
• M.Sc. in Management Engineering, University of Bologna, 2018-2021
• B.S. in Management Engineering, University of Bologna, 2015-2018

Certifications

• CMI Level 5 in Management and Leadership, Ashorne Hill, 2025
• Computer Vision Fundamentals with Google Cloud, Google Cloud Skills Boost, 2024
• Machine Learning, Stanford University, 2020