Hadi Zaatiti

MagnetoEncephaloGraphy (MEG) and ElectroEncephaloGraphy (EEG) Labs, part of the BioMedical Imaging Core at NYUAD.

• Experimental design & hypotheses: Collaborate on the design and implementation of MEG/EEG experimental paradigms, aligning technical infrastructure with neurobiological objectives to investigate complex cognitive and clinical phenomena.
• System integration & maintenance: Maintain and optimize MEG, EEG, and fMRI-compatible EEG systems; proactively adapt hardware and software to evolving research requirements while ensuring peak performance through rigorous calibration and technical monitoring.
• Advanced analysis & pipeline development: Engineer robust data-processing pipelines and apply sophisticated modeling techniques to transform raw signals into high-fidelity neural data, enabling deeper neuroscientific insight and reproducible results.
• Scientific impact & lifecycle management: Lead research projects from conceptualization to dissemination, ensuring rigorous methodology and high-quality outputs for peer-reviewed publications and applied clinical contexts.

Model Manager for two programs: FCAS (Future Combat Air System) and ED (EuroDrone). Also served as MBSE & Simulation Tutor at Airbus Academy.

• MBSE processes: Design technical processes for writing requirements and modeling operational, functional and technical architecture elements.
• Systems engineering deployment: Apply architectural frameworks such as UAF and UBM (Cameo Systems Modeler, Enterprise Architect, Doors, RPE, Jira, Confluence).
• Mission simulation: Define and simulate mission scenarios at different levels of abstraction (Cameo Simulation Kit, MATLAB).
• Automate generation & verification: requirements/documentation generation and automated testing/verification of models (JavaScript, NoMagic API, Jenkins, GitHub, Bitbucket).
• Train and standardize: deliver MBSE and simulation training to future complex-system designers; contribute to state-of-the-art systems engineering (INCOSE, AFIS), ISO standards, and certification programs (CSEP).

Architect for autonomous transportation systems in collaboration with RATP, Transdev, SNCF, Renault, PSA, Navya and EasyMile.

• Autonomous shuttles: Designed, specified and modeled a complete autonomous shuttle system with project partners, ensuring correct behavior via verification and simulation techniques.
• Representation learning on manifolds (with Stanford and Inria): Worked on data representation on manifolds (mainly graphs) as the 3rd contributor to geomstats.ai, and on learning methods for complex system models (simple, timed and hybrid automata).

Taught 4th-year engineering students system modeling, requirements engineering and simulation through a practical case: capturing stakeholder needs, building an architecture, evaluating technical solutions and justifying design choices.

Modelling, simulation and formal verification of hybrid dynamical systems.

• Modeled complex systems as finite state machines coupled with differential equations (hybrid automata) and formally verified them via Satisfiability Modulo Theories (SMT and SAT).
• Developed an approach that automates the abstraction of such models to verify safety properties expressed through constraints.
• Implemented the approach in C++ (over 15k lines) interfaced with QepCAD and other SMT solvers, and validated it on more than 10 complex examples.

During my Ph.D., taught 2nd- and 3rd-year computer science students. Courses included programming languages (C++), algorithms, software testing, networking and routing.

Addressed the safety of a cyber-physical railway system by applying a contract-based design methodology and demonstrating it in a 3D graphical model. A Python tool checks whether a safety property is violated throughout an animated automatic-metro scenario.