Bruno Guindani, Ph.D.

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I am a Software Engineer at Akamas. I pursue research on the optimization of Kubernetes-managed cloud infrastructure and AI workloads.

Until 2026, I was a researcher in the Software Engineering Group (deepse) at Politecnico di Milano, focusing on statistical optimization methods for cloud-edge configuration and digital twin healthcare architectures.

I earned my Ph.D. degree in Information Technology in 2024 and my M.Sc. degree in Mathematical Engineering (Applied Statistics) in 2021.

Feel free to reach out at address or through my social media accounts linked below.

Some stuff I worked on

  • R. Sala, B. Guindani, D. Ardagna, A. Guglielmi. d-MALIBOO: a Bayesian Optimization framework for dealing with Discrete Variables. IEEE International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), 2024. Best Paper Award. Link
  • M. Beraha, B. Guindani, M. Gianella, A. Guglielmi. BayesMix: Bayesian Mixture Models in C++. Journal of Statistical Software, 2025. Link
  • B. Guindani, R. Rocco, D. Gadioli, D. Ardagna, G. Palermo. Efficient Parameter Tuning for a Structure-Based Virtual Screening HPC Application. Journal of Parallel and Distributed Computing, 2025. Link
  • B. Guindani, M. Camilli, L. Lestingi, M. Bersani. Agentic Generation of Structured Clinical Specifications for Digital Healthcare Services. IEEE/ACM International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS), 2026. Link