Research interests

My research interests are in software verification and in building reliable and secure software systems using techniques, including machine learning, deep neural networks with TensorFlow, numerical optimization, SAT solvers, code-to-code translation, and formal methods.

Curriculum Vitae

You can download a copy of my Curriculum Vitae here.

Current Academic Position

I am a Research Fellow in the PAC research group, where we develop and implement distributed model checking algorithms to find bugs in concurrent programs. The project is partially supported by Amazon Research Awards - “Program Analysis in the Clouds (PAC): a distributed symbolic algorithm to scale up bug-finding in concurrent programs”.

Education

I got a PhD at IMT Lucca (Lucca, Italy), where I worked on three techniques to automatically derive white-box or gray-box performance models for microservice architectures (MSA) using system profiling, code analysis, deep neural networks, and queuing networks. I got a MSc and a BSc in Computer Science at Università degli Studi di Torino (Turin, Italy).

Software and Tools developed

• LaDR: Static Data Race Detection via Lazy Sequentialization
• Approximate Method for Program Analysis in CBMC
• PAC: Program Analysis in the Clouds
• μP: A Development Framework for Accurate Performance Predictions in Microservices Systems
• GoAt: Attribute-based Interaction in Google Go
• GreatSPN: GRaphical Editor and Analyzer for Timed and Stochastic Petri Nets

Publications

• Bernd Fischer, Giulio Garbi, Salvatore La Torre, Gennaro Parlato, Peter Schrammel: Static Data Race Detection via Lazy Sequentialization. NetYS 2024 (under publication) – BEST PAPER AWARD
• Giulio Garbi, Emilio Incerto, Mirco Tribastone: μP: A Development Framework for Predicting Performance of Microservices by Design. CLOUD 2023: 178-188
• Yehia Abd Alrahman, Giulio Garbi: A distributed API for coordinating AbC programs. Int. J. Softw. Tools Technol. Transf. 22(4): 477-496 (2020)
• Giulio Garbi, Emilio Incerto, Mirco Tribastone: Learning Queuing Networks by Recurrent Neural Networks. ICPE 2020: 56-66
• Elvio Gilberto Amparore, Susanna Donatelli, Marco Beccuti, Giulio Garbi, Andrew S. Miner: Decision Diagrams for Petri Nets: A Comparison of Variable Ordering Algorithms. Trans. Petri Nets Other Model. Concurr. 13: 73-92 (2018)
• Yehia Abd Alrahman, Rocco De Nicola, Giulio Garbi, Michele Loreti: A Distributed Coordination Infrastructure for Attribute-Based Interaction. FORTE 2018: 1-20
• Yehia Abd Alrahman, Rocco De Nicola, Giulio Garbi: GoAt: Attribute-Based Interaction in Google Go. ISoLA (3) 2018: 288-303
• Elvio Gilberto Amparore, Susanna Donatelli, Marco Beccuti, Giulio Garbi, Andrew S. Miner: Decision Diagrams for Petri Nets: Which Variable Ordering? PNSE @ Petri Nets 2017: 31-50