CV

Below you can find a summary of my career path.

Education

• MSc. in Data Science and Engineering, EURECOM, 2020 - 2022 (GPA: 17/20)
• MSc. in Data Science and Engineering, Polytechnic University of Turin, 2019 - 2022 (GPA: 110 cum laude/110)
• BSc. in Computer Engineering, Polytechnic University of Turin, 2016 - 2019 (GPA: 108/110)

Work experience

• Fellow, Jan 2023 – Present, CERN, Geneva, Switzerland
  • Main developer and Task 6.5 lead (interTwin): architected and delivered itwinai, an abstraction layer for distributed AI on HPC that combines data‑parallel training with scalable hyper‑parameter optimization while reducing engineering overheads for scientists.
  • Validated itwinai on European HPC by integrating four physics digital‑twin use cases (e.g., MLPF) and three Earth‑observation use cases; optimized code scalability and throughput.
  • Digital‑twin engineering at CERN openlab: coordinated two projects — LHC infrastructure digital twin with NVIDIA Omniverse and a CMS ECAL DQM digital twin with Imperial College London — and led technical discussions with NVIDIA, Google, and Imperial.
  • Cloud‑HPC integration: enabled large‑scale AI with the Ray Kubernetes Operator via interLink; built cross‑infrastructure container CI/CD with Dagger; presented results at KubeCon + CloudNativeCon EU 2025.
  • Benchmarking & energy: measured performance and energy consumption of AI workloads for scientific digital twins on LUMI (AMD MI250X), Deucalion (Arm A64FX), Jülich (NVIDIA A100/V100), and Vega (NVIDIA A100); built Grafana dashboards for Prometheus telemetry.
  • EC‑funded projects: interTwin (2023–2025), ODISSEE (2025–present) — gathered use‑case requirements; assessed digital‑twin frameworks for data‑centre operations (e.g., ExaDigiT); evaluated Energy Aware Runtime (EAR); liaised with partners (SURF, LHCb, NextSilicon, SiPearl).
  • Community: organized a Birds‑of‑a‑Feather on scientific digital twins at ISC 2025 (speakers from NVIDIA, SURF, CINES, ECMWF, CERN); presented work at ISC 2024, CHEP 2024, and PASC 2025.
  • Mentoring & recruiting: supervised 4 summer students and 3 technical students; supported hiring of new fellows.
• Cyber‑security Data Scientist, Jun 2022 – Dec 2022, Huawei, Munich, Germany
  • Co‑designed and co‑developed an LLM‑based malware‑analysis framework; scaled AI workloads on Huawei Cloud; authored periodic technical progress reports; applied graph ML and NLP methods.
• Research Intern, Sep 2021 – Feb 2022, Huawei, Munich, Germany
  • Built a reinforcement learning proof‑of‑concept to automate reverse engineering of evasive malware, improving dynamic‑analysis efficiency and accuracy. (Thesis available on request.)
• Big Data Analyst Intern, Mar 2019 – Jun 2019, Technology Reply, Turin, Italy
  • Extracted unstructured information from bank transfers with Spark; engineered NLP pipelines (document embeddings, clustering, semi‑supervised class discovery); delivered ML models for transaction classification and an OWL ontology for semantic queries.

Research projects

• Digital twins at CERN openlab (2023 – present)
  • LHC infrastructure DT with NVIDIA Omniverse for interactive 3D visualization and operations context.
  • CMS ECAL DQM DT with Imperial College London: AI‑assisted data‑quality monitoring and visualization.
  • Cloud–HPC DT workflows with interLink + Ray on Kubernetes; cross‑site CI/CD with Dagger.
• itwinai (interTwin Task 6.5) (2023 – present)
  • Open‑source framework to scale AI workflows on HPC (PyTorch/TensorFlow, DDP/Horovod/DeepSpeed, Ray Tune) with provenance and experiment management; validated on physics and Earth‑observation digital‑twin use cases.
• Energy & performance benchmarking of DT AI workloads (2023 – 2025)
  • Comparative measurements across LUMI, Deucalion, Jülich, Vega; telemetry via Prometheus/Grafana; analysis focused on throughput, scaling efficiency, and energy consumption.

Skills

• Machine learning & deep learning
  • Classification, regression, clustering; supervised/unsupervised/self‑supervised/semi‑supervised
  • NLP, CV, generative modeling, graph ML, RL
  • PyTorch, TensorFlow; scaling with DDP, Horovod, DeepSpeed; Ray Tune for HPO
• HPC & Cloud for AI
  • SLURM, Kubernetes; Ray on K8s; interLink for cloud–HPC workflows
  • Containers & CI/CD: Docker, Singularity/Apptainer, Dagger, GitHub Actions
  • Observability & energy: Prometheus, Kepler, Grafana; PUE/energy analysis
• Digital twin tooling
  • NVIDIA Omniverse for 3D/interactive visualization; ExaDigiT for DC modeling; Grafana dashboards
• Programming & software engineering
  • Python, Go, C++, Bash; Git; Agile/iterative delivery; Unix/Linux
• Data management
  • ETL pipelines; Hadoop/Spark; SQL/NoSQL

Selected talks & publications

• Bunino M. et al. itwinai: Enabling Scalable AI Workflows on HPC for Digital Twins in Science, EPJ Conf., 2025. DOI: 10.1051/epjconf/202533701361
• Testing AI Containers for Digital Twins in Science: a Cloud‑HPC Workflow, KubeCon EU 2025 (talk)
• BoF organiser & speaker — Shaping the Future of Scientific Digital Twins (NVIDIA, SURF, CINES, ECMWF, CERN), ISC 2025
• BoF speaker — Synergistically Integrating HPC and Cloud, ISC 2024

Downloads

Download my CV here!