Andrea Micheli
Head Of Planning Scheduling And Optimization Unit
Trentino – Alto Adige/Südtirol, Italy
Summary
👤
Senior🎓
Top SchoolAndrea Micheli is a research-driven technology leader with 11 years of experience bridging formal verification and AI planning; he currently heads the Planning, Scheduling and Optimization Unit at Fondazione Bruno Kessler and coordinates the AIPlan4EU project. With a PhD in AI and a background developing model checkers like NuSMV and KRATOS, he combines deep expertise in temporal reasoning, planning in continuous domains, and SMT/QF techniques to tackle planning under temporal uncertainty and resource constraints. He has a hands-on open-source pedigree, contributing robustness and partial-model support to the widely used pySMT library, reflecting his taste for practical enabling technologies. A former NASA Ames visiting scientist and summa cum laude computer science graduate, he pairs rigorous academic training with impactful applied research and cross-institutional collaboration.
11 years of coding experience
12 years of employment as a software developer
Master Degree, Computer Science, Summa cum Laude (110/110 with Distinction), Master Degree, Computer Science, Summa cum Laude (110/110 with Distinction) at Università degli Studi di Trento / University of TrentoHigh School Diploma, Scientific High School, 100/100, High School Diploma, Scientific High School, 100/100 at Liceo Scientifico Bertrard Russel ClesDoctor of Philosophy (Ph.D.), Artificial Intelligence, Doctor of Philosophy (Ph.D.), Artificial Intelligence at ICT Doctoral School University of Trento
Italian, English
Github Skills (7)
bit-vector10
solver10
sat-solver10
python10
smt10
testing9
backend9
Programming languages (8)
Github contributions (5)
pysmt/pysmt
Nov 2014 - Jan 2023
pySMT: A library for SMT formulae manipulation and solving
Role in this project:
Back-end Developer
Back-end DeveloperContributions:36 reviews, 729 commits, 378 PRs in 8 years 2 months
Contributions summary:Andrea primarily focused on fixing bugs and improving the robustness of the pySMT library. Their contributions involved fixing import errors, addressing issues related to handling different numeric types, and improving the accuracy of results for particular operators. Furthermore, the user improved the solver by adding features like support for partial models.
manipulationformulaepythonsolvingsmt
mikand/tamer-docker
Dec 2016 - Apr 2021
Contributions:20 pushes, 1 branch in 4 years 5 months
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